Two new shopping datasets with taxonomy

This is just to let you know that I have added two new transaction datasets to the SPMF datasets webpage:

Those are two customer transaction datasets obtained by transforming the data from the instacart competition that was held on Kaggle in 2017. They can directly be used for frequent itemset mining and association rule mining.

I have also included a file giving a taxonomy of the items in categories and sub-categories and a file indicating the full names of the items.

This can be useful for interpreting the results of itemset mining and association rule mining algorithms. All the files are in SPMF format for use with the SPMF software.

Posted in Database, Pattern Mining, spmf | Leave a comment

How to deal with unethical reviewers? The good example of the EAAI journal

Today, I will talk about a problem that has been plaguing many academic journals in recent years. It is that several unethical reviewers are asking authors to cite several of their papers to boost their citation count. I previous wrote a few blog posts about this: post1, post2, post3 but today, I want to talk about solutions.

I see this problem happening very often in academic journals, especially as an author. In some case, I for example, received reviews for a journal paper where the reviewer was asking to cite 10 papers (!!!) by the same author, which were very remotely relevant to the topic. In this case, it is obvious that the reviewer is just trying to boost his citation count to game the system, and perhaps obtain a promotion or something else.

Usually, when this happen, I will complain to the editor because this behavior is unethical, and I dont want add useless citations in my paper, which would degrade the quality of the paper. But I know several authors that are afraid of such unethical reviewers and that will just do what the reviewers ask and add the citations rather than reporting them for their unethical behavior. But I also understand the authors who do not want to report this problem because the reviewers are in a position of power. So it is hard to fight this problem, and sometimes the editor will not even pay attention to those reports.

So how to handle this problem? I think that a good solution is to follow the approach of the EAAI journal, and this is the topic of this blog for today. I want to praise that journal for the form that they provide to reviewers. The first question of that form is as follows:

In that question, the reviewer is required to clearly indicate all the references that they are asking the authors to cite and to provide the full author list and DOI of each reference. This is important because many unethical reviewers will ask to cite papers but remove their names from the author list, hoping that the editor would not detect the conflict of interest.

The second question of the review form ask the reviewer to clearly indicate any citations from themselves and the reason for asking to cite such papers. There is also a clear warning that this is generally considered unethical.

These two questions in the review form are very simple, but they show that the journal cares and this form tells the reviewers to stay away from this type of unethical behavior. It can certainly have a dissuasive effect.

I think that this is a good approach and other journals should take this as an example and do the same. I know that some other journals do this already but still many journals do not, and this problem is still very common in academia.

And also, a major issue is that several editors just don’t pay attention to this problem as they are sometimes handling hundreds of papers. Thus, some editor will just read the reviews from reviewers very quickly and will not see the problem. And it also happens in some cases that the editor is the problem itself. I have ever seen some suspicious behavior where reviewers where asking me to cite papers from the handling editor. In this case, it is rather obvious that the editor is editing the reviews from reviewers to boost his citation count or the impact factor of his own journal!

So that is all for this post. I just wanted to praise the approach adopted by the EAAI journal as a good example of how to deal with unethical reviewers by adding questions to the review form. Do you have any other suggestions? You may share them in the comment section below.


Philippe Fournier-Viger is a computer science professor and founder of the SPMF open-source data mining library, which offers more than 170 algorithms for analyzing data, implemented in Java.

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CSRankings: still a biased ranking

Today, I will write about a ranking called CSRankings, which is used by some people to rank computer science programs from different universities. That ranking has some interesting aspects such that its code and data is open to the public, but it is in my opinion biased for several reasons. I previously wrote a blog post about the shortcomings of CSRankings.

Some shortcomings are that it only considers conference papers, while journal papers are more important in several countries, and that CSRankings is US-centric in its methodology. But a bigger problem, as I explained last time, is that CSRankings is biased towards some subfields of computer science. For example, that ranking has a category for robotics with some conferences such as ICRA that have a very high acceptance rate (~49%), while some other categories have conferences with a much lower acceptance rate (~10%), and some subfields of computer science are basically ignored such as data mining.

About data mining, in my previous blog post, I pointed out that the KDD conference, which is arguably the #1 conference in data mining with an acceptance rate of around 10%, was deactivated by default in CSRankings. Here is a screenshot:

This is quite baffling given that KDD is highly regarded, even with top companies and universities regularly publishing in this conference. Other data mining conferences are also omitted from that ranking. So basically, we could say that the field of data mining does not exist in that ranking, and that would be somewhat true. There is a category called “database” but it also excludes ICDE by default.

On the Github page of CSRankings, several people have complained that KDD has been removed from CSRankings, as obviously several people are unhappy with this (source: github.com/emeryberger/CSrankings/issues/5397):

Some other supporting messages are as follows:

There was a message from one of the CSRankings owner announcing that this might change:

However, more than a year has passed and nothing has changed.

I wrote this blog post to raise awareness about this issue with CSRankings. So how to fix it? In my opinion, the best solution would be to add a “data mining” category with KDD and ICDM at least. Then, it would be more fair for the data mining community, which has been thriving for almost three decades. Data mining should not be wiped-out just like that from a ranking.

Or another solution would be that someone clone the code and data of CSRankings (since it is open-source) to start a more fair ranking that would fix these problems because CSRankings might not fix them. Of course, no ranking is perfect, but I think that some obvious improvements could be done in this case. Who is willing to do it?

That is all for today. By the way, note that the content of this blog post represents my personal opinion.

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The story of the most influential paper award of PAKDD 2024

Recently, I have attended the PAKDD 2024 conference, where I was happy to receive the most influential paper award with my co-authors. This award is a test of time type of award that is given to the paper from PAKDD 2014 that received the largest number of citations or had the largest impact over the last ten years. In this blog post, I will briefly talk about the story of this paper, and why it has been successful. Then, I will talk about some of the applications of the algorithms presented in this paper, and talk about how to get such award.

The paper

I have received the award with my co-authors for this paper:

Fournier-Viger, P., Gomariz, A., Campos, M., Thomas, R. (2014). Fast Vertical Mining of Sequential Patterns Using Co-occurrence InformationProc. 18th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2014) Part 1, Springer, LNAI, 8443. pp. 40-52. [ppt][source code]

This paper is about sequential pattern mining (SPM). Let me explain quickly what this is. SPM is a popular data mining task that is used to analyze sequence data. So what is a sequence? A sequence is an ordered list of symbols. For example, let me show you a few examples of sequences that we may find in some real-life applications.

In this first example, we have a sequence of customer purchases indicating that a customer has bought an apple, then some bread, and then some cake. But sequences can be found also in many other domains. Another example is a sequence of words in a text:

In that sequence the words are sequentially ordered. Another example of sequence from another domain is a sequence of locations visited by a person driving a car in a city:

If we have data represented as sequences, we can apply the task of sequential pattern mining to find patterns (subsequences) that appear frequently in those sequences. The idea is to discover patterns that could reveal something about those sequences. For example, we may want to analyze the sequences of purchases made by several customers to find some sequences of purchases common to multiple customers. Let me show you how sequential pattern mining works with a simple example. Consider a database of four sequences representing the purchases made by four different customers:

In that example, the letter a, b, c, and d represent the purchase of apple, bread, cake, and dattes, respectively. The first sequence called S_1 indicates that the first customer has bought apple and bread at the same time, followed by buying cake, and then by purchasing apple. The other three sequences (S_2, S_3 and S_4) have a similar meaning.

Now, if we want to do sequential pattern mining with this data, we must set a parameter called the minimum support threshold (abbreviated as minsup). Consider that we decide to set this parameter as minsup = 3. It means that we want to find all the sequential patterns (subsequences) that appear in at least 2 sequences of the inpput sequence database.

Let me show you what is the output for minsup = 3:

For the input sequence database on the left and minsup =3, the output is the list of sequential patterns that are presented on the right side of the above figure. Take the pattern <{a,b},{c}> as an example. This pattern is said to have a support of 3 (to appear three times) because it occurs in three sequences in the input database, as highlighted in yellow below:

As it can be seen in the above figure, apple and bread appear together and are followed by cake in the sequence S_1, S_2 and S_3. Hence, this pattern <{a,b},{c}> is said to have a support of 3, and since this value is no less than minsup, this pattern <{a,b},{c}> is also said to be a frequent sequential pattern and it is output.

It can be observed in the sequence S_2 that there can be a gap between {a,b} and {c}, and this is ok, since {a,b} still appears before {c}.

So to summarize, the task of sequential pattern mining is to find all the frequent sequential patterns in a database, given some minsup threshold set by the user. And a frequent sequential pattern is a subsequence that appears in at least minsup sequences.

In the above example, with minsup = 3, the output is 6 sequential patterns. For a task of sequential pattern mining such as in the above example, there is always only one solution, which is the set of patterns to be discovered. The challenge is in designing efficient algorithms to find this solution.

So what was the paper about? In that paper, we presented a new optimization called co-occurrence pruning that allows to considerably speed-up sequential pattern mining algorithms. The improvement obtained by our optimization can be for example to speed up an algorithm by up to 10 times. The improved algorithms in the paper are called CM-SPAM, CM-SPADE and CM-CLASP, which are improved versions of the classical SPAM, SPADE and CLASP algorithms.

The paper has won the most influential paper award by having over 340 citations, according to Google Scholar, as shown below:

Citations to the paper from 2014 to 2024

These citations are mainly of two types: (1) applications of the improved algorithms (CM-SPAM, CM-SPADE and CM-CLASP) in some real-life applications and (2) papers that have used the proposed optimization to develop other similar algorithms.

The story of this paper

So now, let me explain the story behind this paper by going back to 2013-2014. The paper was written by me and three co-authors, shown below:

At that time, I was a young professor working on pattern mining, and Antonio and Rincy were students, while Manuel was the supervisor of Antonio. But initially, we did not know each other.

From my side, I had started to develop the SPMF open-source pattern mining software in 2008, which is a free software in Java offering efficient implementations of many pattern mining algorithms. Then, around 2013, I received several e-mails from Rincy to discuss sequential pattern mining algorithms:

In particular, Rincy wanted to know which sequential pattern mining algorithm is the best. He really wanted to find the answer and did several experiments about this with SPMF, which were very interesting. But at that time, we did not have the source code of all the best algorithms to make an exhaustive comparison.

Then, I started to discuss by e-mail with Antonio from Spain, who had just published a paper at PAKDD 2023 about the CLASP algorithm for closed sequential pattern mining. Antonio accepted to share with me the code of many additional algorithms including GSP, SPADE, SPAM, PrefixSpan, CloSpan and CLASP.

So now, we had many algorithm implementations for comparing sequential pattern mining algorithms. I then continued discussing with Rincy through e-mails:

As I recall, he made an important observation, which is that vertical algorithms such as SPAM generate too many candidates, and the main cost of such algorithm is the join operation that is performed to calculate the support of each candidate. Thus, if we could find a way to reduce that number, we might be able to speed-up the algorithms…

Then, after that, based on that observation, I designed the new co-occurrence pruning optimization that is presented in the paper. The optimization was implemented by me and Antonio in several algorithms, and we have all participated together to the paper writing, and Manuel also helped for the paper. Then, we submitted it to PAKDD 2014…. and it got accepted!

At that time, as I remember, the paper was accepted but maybe had two accept and a weak accept recommendations. Thus, it was not regarded as the best paper of PAKDD 2014, but 10 years later, it is arguably the paper that had the biggest impact. From this, we may draw the conclusion that reviewers are not always right. 🙂

Why it was successful?

I believe that the main reasons why the paper was successful are the following:

  • – The paper is about a fundamental topic (sequential pattern mining) that can have applications in many fields.
  • – We have shown a clear performance improvement over the state-of-the-art algorithms and compared with many algorithms on several datasets.
  • – I promoted the paper by talking about it to other researchers in numerous occasions, by having a website, a blog, and also mentioning this paper in several of my own papers, including a survey on sequential pattern mining.
  • – I published the source code of the algorithms and datasets in the SPMF pattern mining software. Thus, it is easy for anyone to reuse my code, apply it to other domains or extend it.

Applications

The algorithms from that paper and from the SPMF pattern mining software in general, have been used in a wide range of applications from multiple fields. Some are listed in the picture below:

In particular, two representative applications of the sequential pattern mining algorithm CM-SPAM are presented in those two papers written by my team:

Nawaz, M. S., Fournier-Viger, P., Nawaz, M. Z., Chen, G., Wu, Y. (2022) MalSPM: Metamorphic Malware Behavior Analysis and Classification using Sequential Pattern Mining. Computers & Security, Elsever, to appearDOI: 10.1016/j.cose.2022.102741

Nawaz, S. M., Fournier-Viger, P., He, Y., Zhang, Q. (2023). PSAC-PDB: Analysis and Classification of Protein Structures. Computers in Biology and Medicine, Elsevier, 158: 106814 (2023)
DOI: 10.1016/j.compbiomed.2023.106814

In the first paper above, we have applied sequential pattern mining to analyze the behavior of malware programs such as computer viruses, worms and trojans. In this case, the data are sequences of API calls made by programs, and we extract sequential patterns to detect (classify) different types of virus. More precisely, the sequential patterns were used as features to train different classifiers, and excellent performance was achieved over the state-of-the art approaches.

In the second paper above, a similar sequential pattern mining-based methodology is used but for analyzing biological viruses. The sequences are in this case genome sequences. Excellent results are also obtained.

Those two papers are examples, but in fact, sequential pattern mining can be applied in numerous other domains.

How to get such award?

So before concluding, how to get this award? I provide a summary in the picture below:

Conclusion

I hope that you have enjoyed this blog post! If you have any comments, you may write in the comment section below.


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

Posted in Big data, Data Mining, Data science, Pattern Mining | Tagged , , , , , , , | Leave a comment

A brief report about PAKDD 2024

This week, I have attended PAKDD 2024 in the city of Taipei. It was a great conference with good keynote speakers, activities and opportunities for learning and networking. In this blog post, I will give a brief overview of the conference and some news about what will happen in the following years.

What is PAKDD?

PAKDD (Pacific-Asia conference on Knowledge Discovery and Data Mining) is an international conference that focused on data mining but also machine learning, in recent years. PAKDD is the main data mining conference in the pacific-asia area. It is a long standing ocnference. This year was the 28th edition (PAKD 2024).

I like PAKDD conferences and have attended it many times. If you are interested, you may read also my previous reports about PAKDD 2014PAKDD 2015PAKDD 2017,  PAKDD 2018 and PAKDD 2019, and PAKDD 2020.

Conference proceedings

As usual, the conference proceedings of PAKDD are published in the Springer LNAI (Lectures Notes in Computer Science) series. As the number of papers has been increasing over the years, nowadays, the proceedings are published as six books:

The proceedings was made available on the conference website and was not given as a book or USB as it was done in the past. I assume that this is to be environmentally friendly, which is reasonable.

Acceptance rate at PAKDD 2024

This year, there was 720 submissions. From those 175 papers were accepted, including 133 and 42 for poster presentations. Thus, the overall acceptance rate is 23 %. The papers have been evaluated by a program committee consisting of 595 researchers.

Location

This year, the location is the city of Taipei, on the island of Taiwan. This city is a nice modern city, and the conference was held in the Taipei International Convention Center (TICC), which is well-located in the center of the city. It is also quite easy to access for the aiport. So, for this, it was a good location.

Workshops

At the conference, there was also six workshops on a variety of topics, including Fintech, affective computing, clustering, robust machine learning, temporal analytics, and pattern mining.

And in particular, I have co-organized the UDML 2024 workshop on Utility-Driven Mining and Learning (see my report about UDML 2024 here). At this workshop, we had an excellent keynote speech with Prof. Jian Pei:

The talk was about the role of data valuation in federated learning. A key point was that we cannot expect different actors to collaborate in federated learning if their own interests (e.g. in terms of money) are not taken into account. Some models were described to solve this issue.

Other activities

There was also an industry exhibition with several companies, which has been refreshing. Several companies were from Taiwan and using machine learning and data science techniques. There was also a company offering cloud services.

Some other interesting activities were the poster session, tutorials and keynote speeches. I have talked with several interesting people at the poster session. For the keynote speeches of the main conference, there was a keynote by a researcher from Google Deep Mind Ed H. Chi, talking about LLMs (Large Language Models). Another keynote was by Prof. Vipin Kumar about environmental data science. And another keynote by Prof. Huan Liu, also about LLMs.

Here is a picture of the poster session:

Social activities

The conference was on overall very well-organized. There was several social activities to allow researchers to talk together. On the first day, there was a welcome reception at the TICC in the evening:

There was also a tour of the National Palace Museum on the evening of the third day, followed by a banquet at the Silk Palace restaurant. Here is a picture from the banquet:

During the banquet, there was a good music performance, proposing music from around the world:

There was also a performance where some artist would draw different things using sand, such as this picture:

Several awards were also announced at the banquet. Here we can see Prof. Vincent S. Tseng, receiving the well-deserved Distinguished Service Award:

I also received the most influential paper award with my co-authors for a paper on sequential pattern mining that was published at PAKDD 2014 and received the most citations over 10 years from that year.

Some other important awards were given as follows:

  • Distinguished Research Contribution Award: Jiawei Han
  • Early Career Research Award: Yu-Feng LI
  • Best Paper Award: Interpreting Pretrained Language Models via Concept Bottlenecks by Zhen Tan, Lu Cheng, Song Wang, Bo Yuan, Jundong Li, Huan Liu
  • Best Student Paper Award: Towards Cost-Efficient Federated Multi-Agent RL with Learnable Aggregation by Yi Zhang, Sen Wang, Zhi Chen, Xuwei Xu, Stano Funiak, Jiajun Liu

It was also announced at the banquet that PAKDD 2025 will be held in Sydney, Australia:

That should be quite exciting. And there was some rumors that PAKDD 2026 might be in Hong Kong.

Conclusion

This was a brief overview of the PAKDD 2024 conference in Taipei. Hope you have enjoyed this blog post. Will be looking forward to PAKDD 2025.


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

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Report on the UDML 2024 workshop @ PAKDD 2024

Today, was the 6th International Workshop on Utility-Driven Mining and Learning (UDML 2024), held at the PAKDD 2024 conference. The workshop was a success. There was many people in attendance (around 20), which is good considering that PAKDD is not a very large conference and that there was about 6 workshops and tutorials running at the same time.

Keynote speech by Prof. Jian Pei

A highlight of the UDML workshop was the invited talk by Prof. Jian Pei from Duke University. He is a famous researcher in data science, which has made many very important contributions to the field. The talk was called “Data valuation in federated learning” and was very interesting. Prof. Pei first introduced the topic of federated learning, a popular topic, and explained that a key issue with many current models is that the monetary aspect is note taken into account. In fact, many researchers assume that different companies or organizations will want to share their data or collaborate to create models using federated learning but do not think that actors need a reward to do so, which could for example be in monetary form.

To solve this problem, his team proposed models for federated learning that would ensure some form of fairness and other desirable properties. This is just a brief summary of the idea of this talk. Here are a few slides from the presentation:

Research papers

This year, the UDML workshop was competitive with 23 submissions and 9 papers accepted. The papers were on various topic including machine learning, but mainly focused on pattern mining (high utility pattern mining, sequential pattern mining, itemset mining, and some applications).

The proceedings of the workshop can be downloaded from this page. Here is a few pictures from some of the speakers:

Best paper award

In the opening ceremony of UDML, it was announced that the best paper award of UDML was given to the following paper, which proposed a novel algorithm for finding co-location patterns in spatial data:

A detection of multi-level co-location patterns based on column calculation and DBSCAN clustering
Ting Yang, Lizhen Wang, Lihua Zhou and Hongmei Chen

Congratulations to the winners!

Group photo

At the end of the workshop, some photos were taken with some of the attendees

Conclusion

That was a brief overview of the UDML 2024 workshop for this year. In a follow-up blog post, I will try to talk to you more about the other activities of the PAKDD 2024 conference. PAKDD is a conference that is quite interesting especially for meeting researchers from the pacific-asia area from the data mining and machine learning community.


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

Posted in Conference, Pattern Mining, Utility Mining | Leave a comment

Upcoming SPMF features for v.2.62 – More Dataset Stats Tools

Today, I just want to talk to you about some upcoming features of the next SPMF version, which will be called 2.62. Some feature that I have currently adding is more tools to calculate statistics about datasets, as you can see on the picture below:

Previously, there was only a few tools of this type, only for sequence databases, graph databases, and transaction databases. In the next version 2.62, there will be a tool like this for all the most important dataset types that can be read by SPMF.


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

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UDML 2024 Workshop program @ PAKDD 2024

The UDML 2024 workshop on Utility-Driven Mining and Learning is coming soon. This year is the 6th edition and it will be held next week at PAKDD 2024 in Taipei. And I am glad to announce that we have a very good program this year with a keynote speech from Prof. Jian Pei (a worldwide top-level computer scientist), as well as 9 papers accepted from 23 submissions:

UDML SESSION 1 (13:40-15:10)

Session chair: Vincent S. Tseng, Philippe Fournier-Viger

13:40: Opening

13:45: Keynote talk : Data valuation in federated learning (Prof. Jian Pei) ***
jian pei

2:25: PAPER: Explainability of Highly Associated Fuzzy Churn Patterns
Yu-Chung Wang, Jerry Chun-Wei Lin and Lars Arne Jordanger

2:40: Incremental skyline frequent-utility itemset mining
Xiaojie Zhang, Guoting Chen, Linqi Song and Wensheng Gan

2:55: A detection of multi-level co-location patterns based on column calculation and DBSCAN clustering
Ting Yang, Lizhen Wang, Lihua Zhou and Hongmei Chen

Coffee break (15:10-15:30)

UDML SESSION 2 (15:30-17:00)

Session chair: Philippe Fournier-Viger

15:30 An Effective Correlated High Utility Itemset Mining Algorithm
Priscilla Okai Owiredu, Vincent Mwintieru Nofong, Selasi Kwashie and Michael Bewong

15:45: Facial Landmark Detection: An Attentive Dropout-Based Occlusion-Adaptive Deep Network
Muhammad Sadiq, Liang Junwei, Geng Yu and Zhang Yunsheng

16:00: Learning Path Recommendation for MOOCs Using Sequential Patterns and Matching Similarity
Wei Song and Qihao Zhang

16:15: A New Closed High Utility Itemsets with Loose Restriction
Qinghua Zhang, Mu-En Wu, Chien-Ping Chung and Jimmy Ming-Tai Wu

16:30 Genetic Algorithm for Efficient Descriptive Pattern Mining
Muhammad Zohaib Nawaz, M. Saqib Nawaz and Philippe Fournier-Viger

16:45: Metamorphic Testing of High-Utility Itemset Mining
Tzung-Pei Hong, Rang Lee, Bay Vo and Shu-Min Lee

Special issue and best paper award

All the accepted papers are also invited for an extension in a special issue of the Expert System journal. And a best paper award will be announced next week!

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SPMF: bug fix about screen resolution

Hi all, this is just to let you know that I found that there was a problem with the user interface of SPMF on low resolution screens in the update 2.60. The table for setting the parameters of algorithms was not appearing properly. I fixed the bug and replace the files spmf.jar and spmf.zip on the website. If you had this issue, you may download the software again.

If you have any other issues with the new version of SPMF, please send me an e-mail to philfv AT qq DOT com.

In particular, if you try to compile the Java code, you may have to update your Java version to the latest version.


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

Posted in Pattern Mining, spmf | Tagged , , , , | Leave a comment

SPMF 2.60 is released!

This is a short message today to announce that the new version of SPMF 2.60 is finally released!

This is a major version as it contains many new things. The full lists of changes can be found on the download page. Some of the main improvements are 18 new algorithms, 21 new tools to visualize different types of data, several improvements to the user interface (some are less visible than others), and also several tools that are added like a workflow editor for running more than one algorithm one after the other, some new tools for data generation and transformation. Here is a picture of a few new windows in the graphical user interface among several:

Besides, for developers of algorithms, a collection of new data structures optimized for primitive types (int, double, etc.) are provided in the package ca.pfv.spmf.datastructures.collections, which can replace several standard Java data structures to speed up algorithms or reduce the memory usage. Here is a screenshot of some of those data structures:

I have also fixed several bugs in the software (thanks to all users who reported them). It is possible that some bugs remain, especially because there is a lot of new code. If you find any problems, please let me know at philfv AT qq DOT com. Also, you can let me know about your suggestions for improvements, if you have some ideas. 🙂 If you also want to contribute code to SPMF, please contact with me (for example, if you want that I integrate your algorithm in the software.

Thanks again to all users of SPMF and the contributors, who support this project and make it better.


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

Posted in Data Mining, Data science, Java, Pattern Mining, spmf | Tagged , , , , , , , , , | Leave a comment