Full-time faculty positions at Harbin Institute of Technology (data mining, statistics, psychology, design…)

Full-time faculty positions at Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, China


The Center of Innovative Industrial Design is currently looking to hire full-time faculty members at the rank of assistant professor, associate professor or professor, with expertise in one of the four following research directions:


1: Industrial product innovation and user perception Detection (HITSZ16-1601001)

The applicants should have a degree in computer science (e.g. artificial intelligence, data mining, machine learning, etc.), psychology (such as cognitive psychology, engineering psychology, quantitative research or psychology measurement, etc.), statistics, or other major related with quantitative research. The applicant should have a doctorate degree from a well-known university. If the applicant has a doctor degree from a domestic university, postdoctoral work experience overseas is preferred. Applicants who have the following professional or interdisciplinary background are preferred: physiological signal sensing and networks construction, physiological and psychological parameters testing and modeling, human-computer interaction and virtual reality, data visualization design, natural language processing, cognitive science and Neurology, etc. Candidates should have an excellent scientific research capability and be fluent in English teaching and communication.

2: Industrial Product Design of human-computer interface (HITSZ16-1601002)

The applicants should have a doctorate degree in mechanical or industrial design, computer science -ergonomics, digital media design or some related fields. The degree should have been obtained from a well-known university at home or abroad. If the doctorate degree has been obtained from a domestic university, postdoctoral work experience overseas is preferred. The candidate should have research experience in -human-computer interface design, including virtual or reality product interface design. The candidate should be proficient in computer programming or utilizing digital product design software. The candidate should have research experience in user research, interface research, and product-related sociological research. The candidate should be familiar with academic frontiers in human-computer interaction area, have excellent scientific research abilities, preferably have experience in interdisciplinary projects, and be fluent in English teaching and communication.

3: Industrial Environmental Design and Digital Research (HITSZ16-1601003)

The candidate should hold a doctorate degree in design from a well-known university. If the candidate holds a doctorate degree from a domestic university, postdoctoral work experience abroad will be preferred. The applicant should have interests in urban and industrial space design, architectural and spatial environment design, transport, equipment operation and other interior design, furniture, lighting, furnishing design and display design, etc.. The applicant should have a solid scientific research capability, be able to work in an interdisciplinary research environment, and be fluent in English teaching and communication.

4: Industrial product modeling and convey visual and auditory research (HITSZ16-1601004)

The candidate should hold a doctorate degree in design, or a related professional doctorate degree in fine arts from a well-known university at home or abroad. If the candidate holds a doctorate degree from a domestic university, postdoctoral work experience abroad will be preferred. The candidate should have professional interest in audio-visual communication design and practice, industrial product design, web design and digital media interface design, etc. The candidate should have excellent scientific research capability, be able to work in an interdisciplinary environment, be fluent in English for teaching and communication, and have strong creative design capabilities.


1. Teaching: The selected candidates will participate in the development of the discipline. S/he will develop and teach graduate and undergraduate courses, and supervise graduate students.

2. Research: The selected candidate should have a clear research direction, and be an active researcher for managing national, provincial, or municipal research projects or business research projects. He/she should have publications such as monographs, textbooks or papers in top journals as first author or corresponding author.

3. Discipline Construction: Work actively in discipline Construction, chair or participate in the discipline or teaching team.

4. Experiment: the candidate should actively participate in the development of the center

5. Other: it is expected that the selected candidate will carry out international cooperation and academic exchange activities, will chair or participate in international academic conferences, to expand the visibility and influence of the center and school; and that the selected candidate will actively participate in community service work, and perform other tasks stipulated in his/her contract.


To apply for the position(s), the candidate should submit the following application materials:

1. Application Letter
a) self-evaluation of professional quality, ability, and character
b) personal statement about research and teaching ability
c) work plan for this position
2. The Teaching and Research Job Application Form
3. CV in English and Chinese (if possible)

IMPORTANT NOTE: Please email the contacts below with application materials as attachments and specify the applied position ID + your name.

Human resource contact person:

Ms. Wang, e-mail: hr@hitsz.edu.cn.

Innovative Industrial Design Research Center Contact:
Dr. Yao, e-mail: julie_j_yao@163.com

An Introduction to Sequence Prediction

In this blog post, I will give an introduction to the task of sequence prediction,  a popular data mining/machine learning task, which consist of predicting the next symbol of a sequence of symbols. This task is important as it have many real-life applications such as webpage prefetching and product recommendation.


What is a sequence?

Before defining the problem of sequence prediction, it is necessary to first explain what is a sequence. A sequence is an ordered list of symbols. For example, here are some common types of sequences:

  • A sequence of webpages visited by a user, ordered by the time of access.
  • A sequence of words or characters typed on a cellphone by a user, or in a text such as a book.
  • A sequence of products bought by a customer in a retail store
  • A sequence of proteins in bioinformatics
  • A sequence of symptoms observed on a patient at a hospital

Note that in the above definition, we consider that a sequence is a list of symbols and do not contain numeric values.  A sequence of numeric values is usually called a time-series rather than a sequence, and the task of predicting a time-series is called time-series forecasting. But this is another topic.

What is sequence prediction?

The task of sequence prediction consists of predicting the next symbol of a sequence based on the previously observed symbols. For example, if a user has visited some webpages A, B, C, in that order, one may want to predict what is the next webpage that will be visited by that user to prefetch the webpage.


An illustration of the problem of sequence prediction

There are two steps to perform sequence prediction:

  1. First,  one must train a sequence prediction model using some previously seen sequences called the training sequences.  This process is illustrated below:seq_model

    For example, one could train a sequence prediction model for webpage prediction using the sequences of webpages visited by several users.

  2. The second step is to use a trained sequence prediction model to perform prediction for new sequences (i.e. predict the next symbol of a new sequence), as illustrated below.seq_model2For example, using a prediction model trained with the sequences of webpages visited by several users, one may predict the next webpage visited by a new user.

An overview of state-of-the-art sequence prediction models

Having defined what are the main sequence prediction models, that could be used in an application?

There are actually numerous models that have been proposed by researchers such as DG, All-k-order Markov, TDAG, PPM, CPT and CPT+.  These models utilize various approaches. Some of them uses for example neural networks, pattern mining and a probabilistic approach.

How to determine if a sequence prediction model is good?

Various sequence prediction models have different advantages and limitations, and may perform more or less well on different types of data. Typically a sequence prediction model is evaluated in terms of criteria such as prediction accuracy, the memory that it uses and the execution time for training and performing predictions.

Several benchmark have been done in the literature to compare various models. For example, here is a recent benchmark performed by my team in our PAKDD 2015 paper about sequence prediction with CPT+accuracy

In this benchmark, we compare our proposed CPT+ sequence prediction model with several state-of-the-art models on various types of data.  Briefly, BMS, MSNBC, Kosarak and Fifa are sequences of webpages. SIGN is a sign-language dataset. Bible word and Bible char are datasets of sequences of Words and characters. As it can be seen, for this type of data at least, CPT+ greatly outperform other models.  There are several reasons. One of them is that several models such as DG assume the Markovian hypothesis that the next symbol only depends on the previous symbols. Another reason is that the CPT+ model use an efficient indexing approach to consider all the relevant data for each prediction (see details in the paper).

Where can I get open-source implementations of sequence prediction models?

Some open-source and Java implementation of the seven discussed sequence prediction models (DG, AKOM, TDAG, LZ78, PPM, CPT, CPT+) can be found in the SPMF open-source data mining library, which includes the implementations from the IPredict project.

There are extensive documentation about how to use these models on the SPMF website. Here I will provide a quick example that shows how the CPT model can be easily applied with just a few lines of code.

               // Phase 1:  Training
                // Load a file containing the training sequences into memory
		SequenceDatabase trainingSet = new SequenceDatabase();
		trainingSet.loadFileSPMFFormat("training_sequences.txt", Integer.MAX_VALUE, 0, Integer.MAX_VALUE);

		// Train the prediction model
                String optionalParameters = "splitLength:6 recursiveDividerMin:1 recursiveDividerMax:5";
		CPTPredictor predictionModel = new CPTPredictor("CPT", optionalParameters);

		// Phase 2: Sequence prediction
                // We will predict the next symbol after the sequence <1,4>
		Sequence sequence = new Sequence(0);
		sequence.addItem(new Item(1));
		sequence.addItem(new Item(4));
		Sequence thePrediction = predictionModel.Predict(sequence);
		System.out.println("For the sequence <(1),(4)>, the prediction for the next symbol is: +" + thePrediction);

Thus, without going into the details of each prediction models, it can be seen that it is very easy to train a sequence prediction model and use it to perform predictions.


This blog post has introduced the task of sequence prediction, which has many applications. Furthermore, implementations of open-source sequence prediction models have been presented.

Philippe Fournier-Viger is a full professor  and the founder of the open-source data mining software SPMF, offering more than 110 data mining algorithms. If you like this blog, you can tweet about it and/or subscribe to my twitter account @philfv to get notified about new posts.

Six important skills to become a succesful researcher

Today, I will discuss how to become a good researcher and what are the most important skills that a researcher should have. This blog post is aimed at young Master degree students and Ph.D students, to provide some useful advice to them.

1) Being humble and open to criticism

An important skill to be a good researcher is to be humble and to be able to listen to others. Even when a researcher works very hard and think that his/her project is “perfect”, there are always some flaws or some possibilities for improvement.

A humble researcher will listen to the feedback and opinions of other researchers on their work, whether this feedback is positive or negative, and will think about how to use this feedback to improve their work. A researcher that works alone can do an excellent work. But by discussing research with others, it is possible to get some new ideas. Also, when a researcher present his/her work to others, it is possible to better understand how people will view your work. For example, it is possible that other people will misunderstand your work because something is unclear. Thus, the researcher may need to make adjustments to his research project.

2) Building a social network

A second important thing to work on for young researchers is to try to build a social network. If a researcher has opportunities to attend international conferences, s/he should try to meet other students/professors to establish contact with other researchers. Other ways of establishing contact with other researchers are to send e-mails to ask questions or discuss research, or it could also be at a regional or national level by attending seminars at other universities.


Building a social network is very important as it can create many opportunities for collaborations. Besides, it can be useful to obtain a Ph.D position at another university (or abroad), a post-doctoral research position or even a lecturer position or professor position in the future, or to obtain some other benefits such as being invited to give a talk at another university or being part of the program committee of conferences and workshops. A young researcher has to often work by herself/himself. But he should also try to connect with other researchers.

For example, during my Ph.D. in Canada, I established contact with some researchers in Taiwan, and I then applied there for doing my postdoc. Then, I used some other contacts recently to find a professor position in China, where I then applied and got the job. Also, I have done many collaborations with other researchers that I have met at conferences.

3) Working hard, working smart

To become a good researcher, another important skill is to spend enough time on your project. In other words, a successful researcher will work hard. For example, it is quite common that good researchers will work more than 10 hours a day. But of course, it is not just about working hard, but also about working “smart”, that is a researcher should spend each minute of his time to do something useful that will make him/her advance toward his goals. Thus, working hard should be done also with a good planning.


When I was a MSc and Ph.D. student, I could easily work more than 12 hours a day. Sometimes, I would only take a few days off during the whole year. Currently, I still work very hard every day but I have to take a little it more time off due to having a family. However, I have gained in efficiency. Thus, even by working a bit less, I can be much more productive than I was a few years ago.

4) Having clear goals / being organized / having a good research plan

A researcher should also have clear goals. For a Ph.D or MSc student, this includes having a general goal of completing the thesis, but also some subgoals or milestones to attain his main goal. One should also try to set dates for achieving these goals. In particular, a student should also think about planning their work in terms of deadlines for conferences. It is not always easy to plan well. But it is a skill that one should try to develop.  Finally, one should also choose his research topic(s) well to work on meaningful topics that will lead to making a good research contribution.

5) Stepping out of the comfort zone

A young researcher should not be afraid to step out of his comfort zone. This includes trying to meet other researchers, trying to establish collaborations with other researchers, trying to learn new ideas or explore new and difficult topics, and also to study abroad.


For example, after finishing my Ph.D. in Canada, which was mostly related to e-learning, I decided to work on the design of fundamental data mining algorithms for my post-doctoral studies and to do this in Taiwan in a data mining lab. This was a major change both in terms of research area but also in terms of country. This has helped me to build some new connections and also to work in a more popular research area, to have more chance of obtaining a professor position, thereafter. This was risky, but I successfully made the transition. Then, after my postdoc I got a professor job in Canada in a university far away from my hometown. This was a compromise that I had to make to be able to get a professor position since there are very few professor positions available in Canada (maybe only 5 that I could apply for every year). Then, after working as a professor for 4 years in Canada, I decided to take another major step out of my comfort zone by selling my house and accepting a professor job at a top 9 university in China. This last move was very risky as I quit my good job in Canada where I was going to become permanent. Moreover, I did that before I actually signed the papers for my job in China. And also from a financial perspective I lost more than 20,000 $ by selling my house quickly to move out. However, the move to China has paid off, as in the next months, I  got selected by a national program for young talents in China. Thus, I now receive about 10 times the funding that I had in Canada for my research, and my salary is more than twice my salary as a professor in Canada, thus covering all the money that I had lost by selling my house. Besides, I have been promoted to full professor and will lead a research center. This is an example of how one can create opportunities in his career by taking risks.

6) Having good writing skills

A young researcher should also try to improve his writing skills. This is very important for all researchers, because a researcher will have to write many publications during his career. Every minute that one spends on improving writing skills will pay off sooner or later.

In terms of writing skills, there are two types of skills.

  • First, one should be good at writing in English without grammar and spelling errors.
  • Second, one should be able to organize his ideas clearly and write a well-organized paper (no matter if it is written in English or another language). For a student, is important to work to improve these two skills during their MSc and Ph.D studies.

These skills are acquired by writing and reading papers, and spending the time to improve yourself when writing (for example by reading the grammar rules when unsure about grammar).

Personally, I am not a native English speaker. I have thus worked hard during my graduate studies to improve my English writing skills.


In this brief blog post, I gave some general advice about important skills for becoming a successful researcher. I you think that I have forgotten something, please post it as a comment below.

Philippe Fournier-Viger is a full professor  and the founder of the open-source data mining software SPMF, offering more than 110 data mining algorithms. If you like this blog, you can tweet about it and/or subscribe to my twitter account @philfv to get notified about new posts.

Finding a Data Scientist Unicorn or building a Data Science Team?

In recent months/years, many blog posts have been trending on the social Web about what is a “data scientist“, as this term has become very popular.  As there is much hype about this term, some people have even jokingly said that a “data scientist is a statistician who lives in San Francisco“.

In this blog post, I will talk about this recent discussion about what is a data scientist, which has led some people to claim that there is some easy signs to recognize a bad data scientist or a fake data scientist. In particular, I will discuss the blog post “10 signs of a bad data scientists”  and explain why this discussion is going the wrong way.

10 signs of a bad data scientists
In this blog post, authors claim that a data scientists must be good a math/statistics, good at coding, good at business, and know most of the tools from Spark, Scala, Pyhthon, SAS to Matlab.

What is wrong with this? The obvious is that it is not really necessary to know all these technologies to analyze data. For example, a person may never have to use Spark to analyze data and will rarely use all these technologies in the same environment. But more importantly, this blog post seems to imply that a single person should replace a team of three persons: (1)  a coder with data mining experience, (2) a statistician, and (3) someone good at business.  Do we really need to have a person that can replace these three persons? The problem with this idea is that a person will always be stronger on one of these three dimensions and weaker on the two other dimensions. Having a person that possess skills in these three dimensions, and is also excellent in these three dimensions is quite rare. Hence, I here call it the  data scientist unicorn, that is a person that is so skilled that he can replace a whole team.

The data scientist unicorn

The data scientist unicorn

In my opinion, instead of thinking about finding that unicorn, the discussion should rather be about creating a good data science team, consisting of the three appropriate persons that are respectively good at statistics, computer sciences, and business, and also have a little background/experience to be able to discuss with the other team members. Thus, perhaps that we should move the discussion from what is a good data scientist to what is a good data science team.

A data science team

A data science team

An example

I will now discuss my case as an example to illustrate the above point that I am trying to make. I am a researcher in data mining. I have a background in computer science and I have worked for 8 years on designing highly efficient data mining algorithms to analyze data.  I am very good at this, ( I am even the founder of the popular Java SPMF data mining library). But I am less good at statistics, and I have almost no knowledge about business.

But this is fine because I am an expert at what I am doing, in one of these three dimensions, and I can still collaborate with a statistician or someone good at business, when I need.  I should not replace a statistician. And it would be wrong to ask a statistician to do my job of designing highly efficient algorithms, as it requires many years of programming experience and excellent knowledge of algorithmic .

A risk with the vision of the “data scientist unicornthat is good at everything is that it may imply that the person may not be an expert at any of those things.

Perhaps that a solution for training good data scientist is those new “data science” degrees that aim at teaching a little bit of everything. I would not say whether these degrees are good or not, as I did not look at these programs. But there is always the risk of training people who can do everything but are not expert at anything.  Thus, why not trying to instead build a strong data science team?


Philippe Fournier-Viger is a full professor  and also the founder of the open-source data mining software SPMF, offering more than 110 data mining algorithms.

If you like this blog, you can tweet about it and/or subscribe to my twitter account @philfv to get notified about new posts.

Brief report about the ACM SAC 2016 conference

Last week, I have attended the ACM SAC 2016 conference (31st ACM Symposium on Applied Computing). It was held in Pisa, Italy from the 4th to the 8th April 2016. In this blog post, I will briefly discuss this conference.

ACM SAC 2016

ACM SAC 2016

About the SAC conference

It is not the first time that I attend the SAC conference. I had papers published in this conference in 2011, 2013, 2015 and now 2016. This conference is a conference where it is possible to submit papers in multiple tracks, where each track has its own topic. The topics are quite varied ranging from programming languages, to intelligent virtual learning environment to data mining.  Personally, I am particularly in the data mining and data stream tracks, which are somewhat competitive and publish some high quality papers.  Last year, for example, the acceptance rate of the data mining track for full papers was about 20 %.

One may think that a  drawback of this conference is that it is not as specialized as a conference on data mining, for example. However, it is also possible to see this as advantage, as it is allows to talk and exchange ideas with people from other fields. Also, an interesting aspect of this conference is that it is friendly toward research with applications (hence the name “symposium on applied computing”).

Also, this conference is a good place for meeting European researchers, as it was held in Europe. But it was quite international, with a great number of people coming from other continents. I have also had good discussions with some well-known researcher.

Finally, another good thing about this conference is that it is published by the ACM and the proceedings are indexe by DBLP and other important indexes in computer science.

SAC 2016

SAC 2016 – Opening

About the organization

The conference was well-organized in general. There might always be something to complain about. But in general the organizers did a very good job, as well as the student volunteers.

The location was also quite good. Italy has good weather and the people were friendly. Even had the occasion to take a few pictures with the leaning Pisa tower.

The data mining track

I have attended most of the talks from the data mining track.  The main topics were:  clustering, scalable algorithms for big data, graph sampling, associative classifiers, classification, itemset mining, and vizualization.

Me and my colleagues have presented two papers respectively on (1) a new algorithm for mining recent weighted frequent itemsets, and (2) a new efficient algorithm for mining closed high utility itemsets named EFIM-Closed.  For that later paper, I have received a nomination for the best poster award.

Nomination for the best poster award for the EFIM-Closed algorithm at SAC 2016

Nomination for the best poster award for the EFIM-Closed algorithm at SAC 2016

SAC 2017 conference

It was announced that the SAC 2017 conference will be held in Marrakech, Morocco, March 27 – 31, 2017.  It may be very interesting to attend this conference next year, for the reasons above, but also for the location, as there are not so often some big international conferences in Morocco.

ACM SAC 2017 conference

ACM SAC 2017 conference

That is all for day (it was just intended to be a short blog post)!


Philippe Fournier-Viger is a full professor  and also the founder of the open-source data mining software SPMF, offering more than 110 data mining algorithms.

If you like this blog, you can tweet about it and/or subscribe to my twitter account @philfv to get notified about new posts.

News about SPMF

Some quick news about the SPMF project.

spmf data mining

  • First, this month I have made a few updates to SPMF.  Two new algorithms have been added: USpan (for high-utility sequential pattern mining) and FCHM (for correlated high utility itemset mining). Moreover, I have fixed a few bugs, and also added a new window for visualizing the patterns found. Hope you will enjoy that new version of SPMF.
  • Second , this week I have read a nice blog post from a user of SPMF that I would like to share with you. The title is: “Sequential pattern mining, the initial attempts” from the Gigantic Data blog by Ryan Panos.  You may want to read it.


Philippe Fournier-Viger is a professor of Computer Science and also the founder of the open-source data mining software SPMF, offering more than 110 data mining algorithms.

SPMF data mining library 0.98: new pattern visualization window

This blog post is to let you know that I have just published a new version of the SPMF open-source Java data mining library (0.98) that offers a new window for visualizing the patterns found by data mining algorithms. This window should be more convenient to use than the text editor for visualizing results as it offers sorting and filtering functions. Here is a picture:

This window is specifically designed for visualizing patterns found by pattern mining algorithms. But it also work with some algorithms such as clustering algorithms, also offered in SPMF.

How to access this new window? This window can be accessed when using the graphical interface of SPMF by selecting the checkbox “Open output file” “using SPMF viewer“. The new window show the patterns found by an algorithm in a table, and it let the user apply some filters to select patterns or to sort the patterns by ascending or descending orders using various measures such as support and confidence (depending on the algorithms) by clicking on the column headers.

This window for visualizing patterns should work with most algorithms offered in SPMF. If you find some bugs related to this new window for visualizing results, you may let me know. Also, if you have some ideas to improve, or want to participate to improve the user interface of SPMF, you may let me know.

Hope you will like it! Also, thanks again to all users of SPMF for your support.


Philippe Fournier-Viger is a professor and also the founder of the open-source data mining software SPMF, offering more than 100 data mining algorithms. Follow me on Twitter: @philfv

The failed ADMA 2015 conference

ADMA 2015 is a failed conference that was organized by a professor of Fudan University. People submitted papers, and then the website was shut downand the conference was cancelled without notifying the authors or answering e-mails. In this blog post I report this story.

Submitting papers

In October 2015, I had submitted two papers to the ADMA 2015 conference (11th conference on Advanced Data Mining and Applications), which was announced to be held at Fudan university, China. It is interesting to note that this conference was called 2015, although it was planned to be held in January 2016, perhaps because the organizer ran out of time to organize it in 2015.

The website of the ADMA 2015 conference  ( http://adma2015.fudan.edu.cn ) was online in October 2015. I had saved a screenshot of the website on my computer.

ADMA 2015 conference

The ADMA 2015 conference website

I had submitted two data mining papers to the conference using their EasyChair submission page, and some colleagues of mine also submitted papers. Here is a screenshot of the submission page:

ADMA2015 easychair

ADMA2015 easychair

According to the website of the ADMA 2015 conference, the deadline notification was supposed to be the 1st October. I sent an e-mail to the organizers of the conference on the 24th September 2015 to ask about the deadline and someone from Fudan University replied that there would be at least a two weeks deadline extension.

ADMA 2015 email

Email from the ADMA 2015 conference organizers

Waiting for the notification

Then,  the time passed and I did not receive any notification about the fate of my papers.

I have thus sent e-mails to the organizers again on the 28th October, 10th November, and 17th November, respectively, to ask when we would get the notification for our papers. But the organizers of the ADMA 2015 never answered the e-mails and also those of my colleagues.  I have tried the official e-mail address of the conference 11thadma@fudan.edu.cn and also to contact directly  the main organizer using his e-mail address from Fudan University. But no answer.

Disappearance of the website

Then, on the 30th November, I have noticed that the website of the ADMA 2015 conference had been taken down.

ADMA 2015 website on November 30th 2015

Thus, I concluded  that the conference had been cancelled. But why?  And why not answering the e-mails, or letting us know ?

It is a pity because I actually enjoyed previous ADMA conferences. But this is really unprofessional. 

In January 2016, I still had not received any news from that failed conference. It was now clear that the conference had been cancelled, although the organizers never bothered to inform the authors or answer their e-mails to tell us about the status of the conference.

The return of the ADMA conference in 2016

After the failed ADMA 2015 conference,  the ADMA conference was back in 2016 with a conference in Australia called ADMA 2016. Luckily that conference was not organized by  the organizers of the failed ADMA 2015 conference, and it has been a success to my knowledge.

Interestingly, the ADMA 2016 conference was called the 12th ADMA conference, although the 11th ADMA conference never happened. But it is perhaps a good idea to forget the bad memory of that failed ADMA 2015 conference.

In 2016, the website of the failed ADMA 2015 conference has suddenly come back online temporarily at: http://ndbc2011.fudan.edu.cn/

Some statistics about the ADMA conference until 2015

Here is some interesting statistics about the ADMA conference that I have obtained.

Conference Location Acceptance rate
ADMA 2005 Wuhan, China 602 submissions / 25 regular paper / 75 short papers
ADMA 2006 Xian, China 515 submissions / 41 full papers / 74 short papers
ADMA 2007 Harbin, China 200 submissions / 44 full papers / 15 short papers
ADMA 2008 Chengdu, China 304 submissions / 35 full papers / 44 short papers
ADMA 2009 Beijing, China 323 submissions / 34 full papers / 47 short papers
ADMA 2010 Chongqing, China 361 submissions / 63 full papers / 55 short paperd
ADMA 2011 Beijing, China 191 submissions / 35 full papers / 29 short papers
ADMA 2012 Nanjing, China 168 submissions / 33 full papers / 32 short papers
ADMA 2013 Hangzhou, China 222 submissions / 32 regular papers / 64 short papers
ADMA 2014
Guilin, China 89 submissions
ADMA 2015 Fudan University, Shanghai, China
10 submissions

Interview with the SPMF library founder

Today, I will just write a short blog post to let you know that I was recently interviewed on Rahaman’s blog.  The interview talks about various topics such as (1) why creating the SPMF data mining library, (2) why choosing to work in academia instead of in the industry, (3) what are the skills required to be a successful researcher,  and (4) how to improve writing skills for research. You can read the interview here:




Philippe Fournier-Viger is a professor of Computer Science and also the founder of the open-source data mining software SPMF, offering more than 100 data mining algorithms. Follow me on Twitter: @philfv


200,000 visitors on the SPMF website!

Today, I will just write a short blog post to mention that the SPMF open-source data mining library has recently passed the milestone of 200,000 visitors.  This is possible thanks to the support of all users of SPMF, and the contributors who have provided source code, suggested bug reports, and more. Here is a chart showing the growing popularity of SPMF.


Thanks for your support!

Philippe Fournier-Viger, Ph.D.
Founder of the SPMF data mining library