Interview with Prof. Rage Uday Kiran about Data Mining

Today, I have the pleasure to interview Rage Uday Kiran researcher at the National Institute of Informatics in Tokyo, Japan.  R. Uday Kiran is an Indian researcher who has been working in Japan for several years. He has been active mainly in the field of data mining, and is a well-known researcher on the topic of discovering patterns in databases. He has taken the time to answer several questions for this interview.

1) Could you please give a brief overview of your most important contributions?

Frequent itemset mining is an important model in data mining. Its mining algorithms discover all itemsets in the data that satisfy the user-specified minimum support (minSup) constraint. The minSup controls the minimum number of transactions that an itemset must cover within the data. Since only a single minSup threshold is used for the entire data, the model implicitly assumes that all items within the data have uniform frequency. However, this is the seldom case in many real-world applications. In many applications, some items appear very frequently within the data, while others rarely appear. If the frequencies of items vary greatly, then we encounter the following two problems:

  • If minSup is set too high, we miss those itemsets that involve rare items in the data.
  • In order to find the itemsets that involve both frequent and rare items, we have to set minSup very low. However, this may cause a combinatorial explosion, producing too many itemsets, because those frequent items associate with one another in all possible ways and many of them are meaningless depending upon the user and/or application requirements.

This dilemma is known as the rare item problem.   During my PhD, I have tried to address this problem by developing frequent itemset models based on multiple minimum supports.

Periodic itemsets are an important class of regularities that exist within the data. Most previous studies have tried to find periodic itemsets based on an implicit assumption that all transactions within the data occur at a fixed time interval.  However, in many real-world applications, transactions occur irregularly within the data.  For the past few years, I am developing models to discover different types of periodic itemsets in irregular time series/temporal databases.

2) What do you think are the key problems that remain to be solved in the field of pattern mining?

1. Rare Item problem is still a major problem which needs to be addressed in many pattern mining models.

2.  Non-support measures, such as occupancy, have to be investigated to assess the interestingness of an itemset.

3. Tuning is a common practice in pattern mining. So disk based algorithms have been investigated to lower the operational cost.

3) What do you expect to achieve in the next 5 years?

In the near future,  IoT devices become the main source of data. The data generated by these IoTs is often large (petabytes of data) and typically have spatiotemporal characteristics.  In the next few years, I would like to develop models that can extract useful information in spatiotemporal databases. In addition, I would like to investigate parallel and disk-based algorithms to find useful information in very large databases efficiently.

4) Do you think that it is important to collaborate with the industry? What are the keys to a successful collaboration?

Yes. I firmly believe it is important for an academician to collaborate with the industry persons. Industrial collaboration facilitates an academician to know the limitations of current research on a particular topic, thereby, enabling an academician to develop models and algorithms that can cater to the industrial requirements. Mutual trust, regular discussions and openness are crucial factors for a successful collaboration.

5) What is the current state of data mining and artificial intelligence technology in Japan?

In my opinion, this is the hardest question to answer. Japanese government has initiated a project, called Society 5.0, which is a human-centered society that balances economic advancement with the resolution of social problems by a system that highly integrates cyberspace and physical space. In this context, most researchers in Japan are working on developing parallel deep neural network algorithms that can analyze the real-world data effectively.  In my lab at the University of Tokyo, researchers are working on language translation using deep neural networks.

6) Which conferences do you like to attend? Why?

I generally wish to attend top international conferences (e.g. KDD, CIKM, PAKDD, SSDBM, EDBT, DASFAA and DEXA). The reasons are as follows : (1) To know about the hot research problems  which are being addressed by the researchers. (2) Interact with the speakers/authors to gain in-depth perception on the interested topics. (3) Collaboration with fellow researchers working on similar topics.

7) Do you have some advices for young researchers?

Have an open mind. Read as many research papers as possible, and ensure that you are covering many topics. Try to get the grasp of implicit and explicit assumptions made by authors in every research paper. Carefully manage the time. Try to collaborate with the senior research students/persons in your lab.

Thanks for participating to this interview!

The best data mining mailing lists for researchers

Today, I will list a few useful mailing lists related to data mining and big data. Subscribing to these mailing list is useful for PhD students and researchers, as many jobs, conferences, special issues and other opportunities are advertised on these mailing lists. It is also good to post your own announcements for jobs, call for papers, etc.

Here is the list:

If you think that I have missed some important mailing lists, please share it in the comment section, and I will update the page. Thanks for reading!

Philippe Fournier-Viger is a full professor working in China and founder of the SPMF open source data mining software.

How to write an academic book?

Have you ever wanted to write an academic book or wondered what are the steps to write one? In this blog post, I will give an overview of the steps to write an academic book, and mention some lessons learned while writing my recent book on high utility pattern mining.

how to write an academic book?

Step 1. Think about a good book idea.
The first step for writing a book is to think about the topic of the book and who will be the target audience. The topic should be something that will be interesting for an audience. If a book focuses on a topic that is too narrow or target a small audience, the impact may be less than if a more general topic is chosen or if a larger audience is targeted.

One should also think about the content of the book, evaluate how much time it would take to write the book, and think about the benefits of making the book versus spending that time to do something else. It is also important to determine the book type. There are three main types of academic books:

  • First, one may publish a textbook, reference book or handbook. Such book must be carefully planned and written in a structured way. The aim is to write a book that can be used for teaching or used as a reference by researchers and practitioners. Because such book must be well-organized, all chapters are often written by the same authors.
  • Second, one may publish an edited book, which is a collection of chapters written by different authors. In that case, the editors typically write one or two chapters and then ask other authors to write the remaining chapters. This is sometimes done by publishing a “call for chapters” online, which invite potential authors to submit a chapter proposal. Then, the editor evaluates the proposal and select some chapters for the book. Writing such book is generally less time-consuming than writing a whole book by oneself because the editors do not need to write all the chapters. However, a drawback of such book is that chapters may contain redundancy and have different writing styles. Thus, the book may be less consistent than a book entirely written by the same authors. A common type of edited book is also the conference or workshop proceedings.
  • Third, one may publish his Ph.D. thesis as a book if the thesis is well-written. In that case, one should be careful to choose a good publisher because several predatory publishers offer to publish theses with a very low quality control, while taking all the copyrights, and then selling the theses at very expensive prices.

Step 2. Submit a book proposal
After finding a good idea for a book, the next step is to choose a publisher. Ideally, one should choose a famous publisher or a publisher that has a good reputation. This will give credibility to the book, and will help to convince potential authors to write chapters for the book if it is an edited book.

After choosing a publisher, one should write a book proposal and send it to the publisher. Several publishers have specific forms for submitting a book proposal, which can be found on their website or by contacting the publisher. A book proposal will request various information such as: (1) information about the authors or editors, (2) some sample chapter (if some have been written), (3) is there similar books on the market?, (4) who will be the primary and secondary audience?, (5) information about the conference or workshop if it is a proceedings book, (6) how many pages, illustrations and figures the book will contain?, (7) what is the expected completion date?, and (8) a short summary of your book idea and the chapter titles.

The book proposal will be evaluated by the publisher and if it is accepted, the publisher will ask to sign a contract. One should read the contract carefully and then sign it if it is satisfying.

Step 3. Write the book
Then the next step is to write the book, which is generally the most time-consuming part. In the case of a book written all by the same authors, this can require a few months. But for an edited book, it can take much less time. Editor must still find authors for writing the chapters and perhaps also write a few chapters.

After the book have been written, it should be checked carefully for errors and consistency. A good idea is to ask peers to check the book to see if something need to be improved. For an edited book, a review process can be organized by recruiting reviewers to review each chapter. The editors should also spend some time to put all the chapters together and combine them in a book. This can take quite a lot of time, especially if the authors did not respect the required format. For this reason, it is important to give very clear instructions to authors with respect to the format of their chapters before they start writing.

Step 4. Submit the book the publisher
After the book is written, it is submitted to the publisher. The publisher will check the content and the format and may offer other services such as creating a book index or revising the English. A publisher may take a month or two to process a book before publishing it.

Step 5. Promote the book
After writing a book, it is important to promote it in an appropriate on the web, social media, or at academic conferences. This will ensure that the book is successful. Of course, if one choose a good publisher, the book will get more visibility.

Lessons learned
This year, I published an edited book on high utility pattern mining with Springer. I followed all the above steps to edit that book. I first submitted a book proposal to Springer, which was accepted. Then, I signed the contract, and posted a call for chapters. I received several chapter proposals and also asked other researchers to write chapters. The writing part took a bit of time because although I edited the book, I still participated to the writing of six of the twelve chapters. Moreover, I also asked various people to review the chapters. Then, it took me about 2 weeks to put all the chapters together and fix the formatting issues. Overall, the whole process was done over about 1 year and half, but I spent perhaps 1 or 2 months of my time. Would I do it again? Yes, because I think it is a good for my career, and I have some other ideas for books.

The most important lesson that I learned is to give more clear instructions to authors to reduce formatting problems and other issues arising when putting all chapters together.

In this blog post, I have discussed how to write an academic book. Hope you have learned something! Please share your comments below. Thanks for reading!

Philippe Fournier-Viger is a full professor working in China and founder of the SPMF open source data mining software.

How to write a research grant proposal?

Today, I will discuss how to write a good research grant proposal. This topic is important for researchers, who are at the beginning of their careers and want to obtain funding for their research projects. A good research proposal can be career-changing as it may allow to secure considerable funding that may for example, help to obtain a promotion. On the other hand, a poorly prepared research proposal is likely to fail. To avoid writing a very long post on this topic, I will focus on the key points for writing a good project proposal.


Before writing a research grant proposal, the first step is preparation. Preparation should ideally start several weeks or months before the deadline. The reason is that writing a proposal takes time and that unexpected events may occur, which may delay the progress. Moreover, starting earlier allows to ask feedback from peers and to think more about the content and how to improve the proposal.

Another important aspect of preparation is to choose an appropriate funding program for the proposed research project.

The research question

A key aspect of preparing a research grant proposal is to choose a research question that will be addressed by the research project.

The key points to pay attention related to the research question are that: (1) the research question is new and relevant, (2) the research project is feasible within the time frame, using the proposed methodology and given the researcher(s)’s background and skills, and (3) the research project is expected to have an important impact (be useful). In the project proposal, the above elements (1), (2), and (3) need to be clearly explained to convince the reviewers that this project deserved to be funded.

Choosing a good research question takes time, but it is very important.

Literature review

Another important part of a project proposal is the literature review, which should provide an overview of relevant and recent studies on the same topic. It is important that the literature review is critical (highlight the limitations of previous studies) with respect to the research question. Moreover, the literature review can be used to highlight the importance of the research question, and its potential impact and applications.

References should be up-to-date (preferably from the last five years). But older references can be included, especially if they are the most relevant.


A good proposal should also clearly explain the methodology that will be used, and the theoretical basis for using that methodology.

About carrying out experiments, one should explain how participants will be recruited and/or data will be obtained, how big the sample size will be(to ensure that results are significant), how results will be interpreted, and how do deal with issues related to ethics.

If a methodology is well-explained and detailed, it indicates that the researcher has a clear plan about how he will conduct the research. This is important to show that the project is feasible.

Timeline of the project

To further convince reviewers that the project will succeed, it is important to also provide a clear timeline for the project. That timeline should indicate when each main task will be done, by who, and what will be the result or deliverables for each task. For example, one could say that during the first 6 months, a PhD student will do a literature review and write a journal paper, while another student will collect data, and so on.

The timeline can be represented visually. For example, I show below a timeline that I have used to apply for some national research funding. That project was a five year projects with three main tasks. I have divided the task clearly among several students.

Note that it is good to mention the names of the students or staff involved in the project, if the names are known. It can also be good to explain how the students will be recruited.


It is also useful to mention the equipment or facilities that are available at the institution where the researcher works, and that will help to carry the project.


Another very important aspect is to write clearly what will be the expected impact of the research project. The impact can be described in terms of advances in terms of knowledge, but also in terms of benefits to the society or economy. In other words, the proposal should explain why the project will be useful.


A project proposal needs to also include a budget that must follow the guidelines of the targeted funding source. It is important that the amounts of money are reasonable and justifications are provided to explain why the money is required.

Team, institution, and Individual

A proposal should also explain that the applicant and its team have the suitable background and skills required for successfully conducting the project. This is done by describing the background and skills of researchers, and how they fit the project.


In this blog post, I have discussed the key points that a good research proposal should include. I could say more about this topic, but I wanted to not make it too long for a blog post.

Philippe Fournier-Viger is a full professor working in China and founder of the SPMF open source data mining software.

How to become a journal or conference reviewer?

Today, I will write a blog post aimed a young researchers, who want to know what is the work of a reviewer in academia, how to become a reviewer of international journals or conferences, and what are the benefits of being a reviewer.

What is the work of a reviewer?

The main work of a reviewer is to read articles and evaluate if the content is suitable to be published. The role of reviewers is very important for the publication process in academia as it helps to filter bad papers and provide advice for improving other papers.

To review an article, a reviewer may spend a few minutes (e.g. when a paper is clearly bad or contain plagiarism, and is directly rejected by the reviewer) to several hours (when the paper is complex and read carefully by the reviewer) . Typically, in good journals and conferences, a paper will be evaluated by several reviewers as they may have different opinions and backgrounds, which help to take a fair decisions on whether to publish papers or not.

In general, the review process of top journals and conferences is quite effective at eliminating bad papers as those can recruit excellent reviewers. Smaller or less famous journals sometimes have more difficulty to find good reviewers related to the topics of papers. And sometimes, some bad manuscripts will once in a while pass through the review process due to various reasons. And for some predatory journals, they often do not have reviewers and will publish anything just to earn money.

What are the benefits of being a reviewer?

The main benefits is to help the academic community by providing feedback to publishers and authors.

The reviewers typically work for free.  But sometimes publishers provides some gift to reviewers. For example, Elsevier had been offering a free one month subscription to one of its online service named Scopus to reviewers, while some top journals of Springer offer to download a free e-book from the Springer library after completing a review on time. Such offers may help to convince researchers to work as reviewers.

Some other benefits of reviewing are:

  • Read the latest research and learn about topics that one would maybe not take the time to read otherwise.
  • Obtain some visibility in the research community. Some conferences will for example publish the names of reviewers in  conference proceedings or on their websites.
  • Learn to think like a reviewer, and become more familiar with the review process of journals. This help to write better papers and to know what to expect when submitting papers to the same conferences and journals.
  • Put this on a CV. Especially for those aiming to work in academia, being a reviewer of a good journal or conference can be useful on a CV.

How to become a reviewer?

A graduate student may start to review papers for his supervisor. In that case, the supervisor will let the student write the review and then the supervisor will check it carefully before the review is submitted to ensure that it is a good and fair review. This will give the opportunity to students to learn how to be a reviewer.

Sometimes, a PhD student may also find some opportunities to review papers by himself. For example, when I was a PhD student I visited some journal websites that advertised that they needed reviewers. I then sent an email with my CV to ask to be a reviewer. The journal was not famous, but it gave me some experience to start review some papers.

But generally, most journals will contact potential reviewers rather than the other way. Generally, when a paper is submitted, the journal editor will search for reviewers that have papers on the same topic or work in a related area, to ask them to review the paper. Thus, reviewers of a journal paper are often expert in the field. Often, editors will prefer to ask researchers who have previously published in the same journal, to review papers. Typically a reviewer, may have a PhD or at least be a PhD student with good publications. In some cases, a master degree students may be asked to review papers. I have seen it once or twice. But this is quite rare and these students had very strong publications.

For a conference, there is typically a program committee that is established. It is a set of researchers that will review the submitted papers. Each reviewer may for example have to review 3 to 5 papers. To join the program committee of a conference, one may email the organizers to ask if they need additional reviewers. Then, the organizers may accept if the applicant has a good CV. But for famous conferences, joining the program committee typically require to be recommended by a conference organizer or some members of the program committee. It is generally not easy to join the program committee (to be a reviewer) of top conferences.

Drawback of being a reviewer

One of the drawback of being a reviewer is that it can require some considerable amount of time. For example, I receive numerous emails to ask me to review journal papers for various journals and on a wide range of topics. Although I do several reviews every month, I also decline several invitations because of time constraints, and that I just receive too many invitations. I will for instance often decline to review papers not in my research area or that are for unknown journals, or journals not related to my research area.

How to review a paper?

If you have been selected to review papers, you may be interested to read a blog post that I wrote about how to review papers.


 Today, I have discussed about the work of reviewers and how to become a reviewer. If you have comments, please share them in the comment section below. I will be happy to read them.

Philippe Fournier-Viger is a full professor working in China and founder of the SPMF open source data mining software.

The importance of explainable data science and machine learning models

In this blog post, I will talk about an important concept, which is often overlooked in data mining and machine learning: explainability.

To discuss this topic, it is necessary to first remember what is the goal of data mining and machine learning. The goal of data mining is to extract models, knowledge, or patterns from data that can help to understand the data and make predictions. There are various types of data mining techniques such as clustering, pattern mining, classification, and outlier detection. The goal of machine learning is more general. It is to build software that can automatically learn to do some tasks. For example, a program can be trained to recognize handwritten characters, play chess, or to explore a virtual world. Generally, data mining can be viewed as a field of research that is overlapping with machine learning and statistics.

Machine learning and data mining techniques can be unsupervised (do not require labelled data to learn models or extract patterns from data) or supervised (labelled data is needed).

In general, the outcome of data mining or machine learning can be evaluated to determine if something useful is obtained by applying these techniques. For example, a handwritten character recognition model may be evaluated in terms of its accuracy (number of characters correctly identified divided by the number of characters to be recognized) or using other measures. By using evaluation measures, a model can be fine-tuned or several models can be compared to choose the best one.

In data mining and machine learning, several techniques work as black-boxes. A black box model can be said to be a software module that takes an input and produces an output but does not let the user understand the process that was applied to obtain the output.

Some examples of blackbox models are neural networks. Several neural networks may provide a very high accuracy for tasks such as face recognition but will not let the user easily understand how the model makes predictions. This is not true for all models, but as neural networks become more complex, it becomes more and more difficult to understand them. The opposite is glassbox models, which let the user understand the process used to generate an output. An example of  glass box models are decision trees. If a decision tree is not too big, it can be easy to understand how it makes its predictions. Although such models may yield a lower accuracy than some blackbox models, glassbox models are easily understood by humans. In data mining, another example of explainable models are patterns extracted by pattern mining algorithms.

A glassbox model is thus said to be explainable.  Explanability means that a model or knowledge extracted by data mining or machine learning can be understood by humans. In many real world applications, explanability is important. For example, a marketing expert may want to apply data mining techniques on customer data to understand the behavior of customers. Then, he may use the learned knowledge to take some marketing decisions or to design a new product. Another example is when data mining techniques are used in a criminal case. If a model predicts that someone is the author of an anonymous text containing threats, then it may be required to explain how this prediction was made to be able to use this model as an evidence in a court.

On the other hand, there are also several applications where explanability is not important. For example, a software program that do face recognition can be very useful even though how it works may not be easily understandable.

Nowadays, many data mining or machine learning models are not explainable. There is thus an important research opportunity to build explainable models. If we build explainable models, a user can participate in the decision process of machines and learn from the obtained models. On the other hand, if a model is not explainable, a user may be left out of the decision process. This thus raises the question of whether machines should be trusted to make decisions without human intervention?


In this blog post, I have described the concept of explainability. What is your opinion about it? You can share your opinion in the comment section below.

Philippe Fournier-Viger is a full professor working in China and founder of the SPMF open source data mining software.

Five recent books on pattern mining

In this blog post, I will list a few interesting and recent books on the topic of pattern mining (discovering interesting patterns in data). This mainly lists books from the last 5 years.

High utility pattern mining: Theory, Applications and algorithms (2019). This is the most recent book, edited by me. It is about probably the hottest topic in pattern mining right now, which is high utility pattern mining. The book contains 12 chapters written by experts from this field about discovering different kinds of high utility patterns in data. It gives a good introduction to the field, as it contains five survey papers, and also describe some of the latest research. Link:

Supervised Descriptive Pattern Mining (2018). A book that focuses on techniques for mining descriptive patterns such as emerging patterns, contrast patterns, class association rules, and subgroup discovery, which are other important techniques in pattern mining.

Pattern Mining with Evolutionary Algorithms (2016). A book that focuses on the use of evolutionary algorithms to discover interesting patterns in data. This is another emerging topic in the field of pattern mining.

Frequent pattern mining (2014). This book does not cover the latest research as it is already almost five years old. But it gives an interesting overview of some popular techniques in frequent pattern mining.

Spatiotemporal Frequent Pattern Mining from Evolving Region Trajectories (2018). This is a recent book, which focus on spatio-temporal pattern mining. Adding the time and spatial dimension in pattern mining is another interesting research issue.

That is all I wanted to write for today. If you know about some other good books related to pattern mining that have been published in recent years, please let me know and I will add them to this list. Also, I am looking forward to edit another book related to pattern mining soon…. What would be a good topic? If you have some suggestions, please let me know in the comment section below!

Philippe Fournier-Viger is a professor of Computer Science and also the founder of the open-source data mining software SPMF, offering more than 150 data mining algorithms.

The High Utility Pattern Mining book is out!

This is to let you know that the new book on high utility pattern mining is out. It is a 337 pages book containing 12 chapters about various topics related to discovering patterns of high utility in databases. It contains several surveys, good for those new to the field, and also some chapters on more advanced topics. It is a good introduction and reference book!

This is the link for the book on the Springer website:

The book is available as PDF and also as hard copy. I received the hard copy yesterday:

Philippe Fournier-Viger is a professor of Computer Science and also the founder of the open-source data mining software SPMF, offering more than 150 data mining algorithms.

Plagiarism by Bhaskar Biswas, Shashank Sheshar Singh, K. Singh, et al. in IEEE Transactions on Knowledge an Data Engineering (TKDE)

Recently, I found that K. SinghShashank Sheshar SinghAjay Kumar,
Harish Kumar Shakya and Bhaskar Biswas  from the Indian Institute of Technology (BHU) (India) have plagiarized my papers in a paper published in the IEEE TKDE (Transactions on Knowledge and Data Engineering ) journal. I will explain this case of plagiarism below.

*** Important notice: Note that “Kuldeep Singh” is a very common name. This article refers to K. Singh working at BHU University in Varanasi, India. This is not about other people with the same name working in Europe or other locations ***

But before let me explain what is plagiarism. There are two types. First, some people will copy some text word for word from another paper without using quotation marks and a citation. Journal editors can easily detect this using automatic tools like CrossCheck. Second, some people will be more careful. They will copy the ideas of another paper without citations and will rewrite the text to avoid being detected. They will then take the credit for the ideas developed by another researcher. Most of the time reviewers of top journals will detect this but sometimes it will go undetected. This is what happened in the TKDE paper that I will talk about today. That paper is:

Kuldeep Singh, Shashank Sheshar Singh, Ajay Kumar,
Harish Kumar Shakya and Bhaskar Biswas (2018) CHN: an efficient algorithm for mining closed high utility itemsets with negative utility”, IEEE Transaction on Knowledge and Data Engineering. (downloaded from the IEEE website)

What is wrong with that paper?

That paper actually proposed a new algorithm called CHN for discovering closed high utility itemsets with negative utility values.  In that paper, they extended the EFIM-Closed algorithm that I had proposed at the MLDM 2016 conference, but they did not mention it in the paper. Basically, they copied several techniques from my EFIM and EFIM-Closed papers without mentioning that they were reusing these ideas. They even renamed some of these techniques (e.g. the “utility-bin”) with a different name (e.g. utility array) and rewrote the text. Thus, it appears as Kuldeep Singh et al. proposed several of the techniques of EFIM-Closed, which is unacceptable. Some of the techniques have been adapted in the paper for the different problem, there is a citation for some upper-bounds, but some techniques are exactly the same and not cited.

What has been plagiarized?

I will list the content that has been plagiarized in the paper and provide screenshots of a side-by-side comparison of the papers.

1) In page 4 of the paper of Kuldeep Singh et al., they copy several definitions such as Property 3.1 and Property 3.2 from our FHN paper in KBS 2016.

2) In Section 4.1, they present two techniques: (1) transaction merging and (2) database projection. But those are the same as in the EFIM-Closed paper. The authors rewrote the text. They mentioned that they could reuse a sorting technique from EFIM-Closed but failed to explain that basically all the idea in this section is copied from EFIM-Closed and unchanged from our paper!

3) In Section 4.2, they pretend to use a new technique called “utility-array” to calculate the utility, support and upper-bound of patterns. But basically, they just renamed the “utility-bin” technique of EFIM-Closed to “utility array” and rewrote the text. They copied the idea without citation and then used it to calculate utility and support in the same way, but also some other upper-bounds.

4) In Section 4.4, the techniques for finding closed patterns are all copied from the EFIM-Closed papers without modifications. EFIM-Closed proposed to use backward/forward extension checking in utility mining, by drawing inspiration from sequential pattern mining. Kuldeep Singh et al. rewrote the text and claimed that they were the first to do that and just cited the sequential pattern mining paper that we cited in our paper.

5) In Section 4.5, they present their CHN algorithm that incorporates the copied techniques and also some other modifications. But the pseudocode is very similar to EFIM-Closed since they extend that algorithm. But they never explain that they extend EFIM-Closed as the basis of their algorithm.

6) The following figure look quite familiar?

7) Besides, it is interesting that in Section 4.2, the authors claimed to have proposed a new RTWU upper-bound, while in Section 3 they had already acknowledged that it was from another paper! It is actually from our FHN paper.

So is there any new contribution in that TKDE paper?

To answer that question, I decide to search a little bit more, and I found that the authors had proposed an algorithm for high utility mining with negative utility called EHIN in the Expert Systems journal also in 2018:

Singh, K., Shakya, H. K., Singh, A., & Biswas, B. (2018). Mining of high-utility itemsets with negative utility. Expert Systems, e12296. doi:10.1111/exsy.12296

So what is the difference between the two papers of
Kuldeep Singh, Bhaskar Biswas et al. ? The only difference is the technique for checking that an itemset is closed using forward and backward extensions. But as I have shown before, this technique is copied from our EFIM-Closed paper in section 4.4 without citations. Thus, there is basically nothing new in the TKDE paper.

Now another question is whether Kuldeep Singh, Bhaskar Biswas et al. cite their Expert System paper correctly? They put a citation (see below), but they also do not explain that the TKDE paper is almost the same as their Expert System paper.

Who are the authors?

Kuldeep Singh, Shashank Sheshar Singh, Ajay Kumar, Harish Kumar Shakya, and Bhaskar Biswas are working for the Computer Science and Engg, of the Indian Institute of Technology (BHU), Varanasi, India 2210

Kuldeep Singh, Shashank Sheshar Singh, Ajay Kumar, and Bhaskar Biswas

Kuldeep SinghShashank Sheshar SinghAjay Kumar, and Bhaskar Biswas from the
Indian Institute of Technology (BHU)

Another paper retracted for plagiarism with Bhaskar Biswas

Some reader of this blog pointed out that another paper of Bhaskar Biswas was retracted (in 2011) while he was also affiliated with the Indian Institute of Technology (BHU):

Here, Bhaskar Biswas is the first author, while in the TKDE paper he seems to be the supervisor of some PhD students.

What will happen if?

As usual, when I find some case of plagiarism, I report it to the journal. I have thus sent an e-mail to the editor of TKDE to report that case of plagiarism, and filled a formal complaint to IEEE to ask that they retract the paper, as soon as possible.

I also sent an e-mail to the dean of the Indian Institute of Technology (BHU) and the dean of the school of computer science and engineering to let them know about what happened.

Update 2019-01-20

The dean of the computer science and engineering school of IIT (BHU) has confirmed receiving my complaint, and told me that they will investigate this. I am waiting for them to tell me which actions they will take.
The editor-in-chief of TKDE has also informed that action will be taken quickly. Thus, I expect that the paper will be retracted soon.

Update 2019-01-23

The first author has communicated with me to tell me that the version on the TKDE website would not be the final version. But normally, it is the accepted version of the paper that the reviewers have read…

But anyway, all I want is that the problem is fixed in a satisfactory way, as I spent already a lot of time to deal with this. If the paper is retracted or fixed on the TKDE website to cite us properly and give the credit where the credit is due, I will be happy and also delete this page from the blog. Hope that this issue can be fixed quickly.


What is the lesson to be learned? In general, there is no problem for a researcher to extend the algorithm of another researcher. This is what Kuldeep Singh, Bhaskar Biswas et al. did in that TKDE paper. They have extended EFIM-Closed with a few ideas to support negative utility values. That would have been fine, if this had been explained. However, the authors rather chose to copy several techniques without citing them and mentioning that EFIM-Closed was extended.

Hope you have learned something from this blog post. That is all for today.

Philippe Fournier-Viger is a professor, data mining researcher and the founder of the SPMF data mining software, which includes more than 100 algorithms for pattern mining.

Analyzing the source code of SPMF (5 years later)

Five years ago, I had analyzed the source code of the SPMF data mining software using an open-source tool called CodeAnalyzer ( ). This had provided some interesting insights about the structure of the project, especially in terms of lines of codes and code to comment ratio. In 2013, for SPMF 0.93, the results were as follows:

Metric                Value
——————————-    ——–
    Total Files                     280
Total Lines                   53165
Avg Line Length                  32
    Code Lines                   25455
    Comment Lines               23208
Whitespace Lines                5803
Code/(Comment+Whitespace) Ratio        0,88
   Code/Comment Ratio                1,10
Code/Whitespace Ratio            4,39
Code/Total Lines Ratio            0,48
Code Lines Per File                  90
    Comment Lines Per File              82
Whitespace Lines Per File              20

Today, in 2018 I decided to analyze the code of SPMF again to get an overview of how the code has evolved over the last few years. Here are the result for the current version of SPMF (2.35):

Metric Value
——————————- ——–
Total Files 1385
Total Lines 238938
Avg Line Length 32
Code Lines 118117
Comment Lines 91241
Whitespace Lines 32797
Code/(Comment+Whitespace) Ratio 0,95
Code/Comment Ratio 1,29
Code/Whitespace Ratio 3,60
Code/Total Lines Ratio 0,49
Code Lines Per File 85
Comment Lines Per File 65
Whitespace Lines Per File 23

Many numbers remain more or less the same. But it is quite amazing to see that the number of lines of code has increased from 25,455 to 118,117 lines. The project is thus about four times larger now. This is in part due to contributions from many people, in recent years, while at the beginning the software was mainly developed by me. The total number of lines may still not seem very big for a software. However, most of the code is quite optimized and implement complex algorithms. Thus, many of these lines of code took quite a lot of time to write.

The number of comment lines has also increased, from 23,208 to 91,241 lines. But the ratio of code to comment lines has slightly increased. Thus, perhaps that adding some more comments is needed.

What is next for SPMF? Currently, I am preparing to release a new version of SPMF, which will include about 10 new algorithms. It should be released in about 1 or 2 weeks, as I need to finish other things first.

That is all for today! If you have comments or questions, please post them in the comment section below.

Philippe Fournier-Viger is a full professor working in China and founder of the SPMF open source data mining software.