2,000,000 visitors on this blog!

This blog has been created about 8 years ago (in 2013) for the purpose of talking about research, academia, and data mining.

Today, I just saw that the counter of visitors to this blog has passed 2,000,000. This is of course just a number, but still it is nice to see this. Thus, I would like to say thank you to all the readers of this blog.

Thanks for supporting the blog!


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

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Paying to be a keynote speaker?

Recently, I received an e-mail from the organizer of an event in the UK called “International Conference on Artificial Intelligence and Machine Learning ” from a website called UnitedResearchForum, where the organizers asked me to participate as a keynote speaker:

From that e-mail, I thought that it was some SPAM as I have never heard about this event before. But just to make sure, I sent an e-mail to ask how much they would pay me to give the keynote speech:

Then, I got their answer, which is quite amazing. They tell me that to be a keynote speaker at their event, I would need to pay 249$ USD:

This is quite ridiculous. In general, a keynote speaker must be paid by the conference. Not the other way around. A keynote speaker is supposed to be a special guest, and generally a conference will pay hotel and airplane plus some salary to their keynote speaker.

This conference should not call their speakers “keynote speakers” if they ask them to pay by themselves for the registration fee. This seems to just be a tactic to get attention.

Thus, I clicked the button in my e-mail inbox to report the e-mail as SPAM.


This is just another example of academic SPAM. I do not recommend publishing there.


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

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The “Top 2% most influential scientists of 2020” according to Stanford

In recent days, I see many posts on Linkedin about researchers that mention that they are in the list of the top 2% most influential scientists of 2020 according to Stanford. Here is an example:

I understand why people post about this. The reason is that they are happy to be in the ranking and to see that their research is impactful.

To see more about how this ranking works, I downloaded the data from : https://elsevier.digitalcommonsdata.com/datasets/btchxktzyw/3

I see that my name is also in that ranking, somewhere around rank #48,000. But this is not very important for me. Let’s talk instead about the data.

The data is provided as Excel files and there is also some Python code that was used to generate the ranking.

In the Excel files, it is interesting to see that each author is described by numerous metrics such as the number of citations as first author, the number of citation as corresponding author, the number of self-citations, the percentage of self-citations, etc.

Of course, all these metrics are not perfect. For example, in some fields it is easier to be cited than in some other fields and some researchers will try to manipulate these metrics for example by asking other researchers to cite their papers rather than deserving the citations. Thus, a higher rank does not necessarily mean that someone is a better researcher in real-life. But nonetheless, it is still interesting to look at this data.

By looking at the data for year 2020, I make a few observations that I find interesting.

  1. After sorting the authors by the year of their first paper, I find that some authors have published papers for more than 180 years. For example, below, Marshall, William S. had his first paper in 1834 and his last paper in 2020. I guess that it must be some error in the data and that two persons must have the same name.

2. The attribute that I perhaps find the most interesting is “self %” which indicates the percentage of self-citations. If I sort from largest to smallest, I can see that some persons have from 90% to 100% self-citations in year 2020, which appears to be very high. Some of these persons also have a quite high “rank”. There can be some reasons for these high percentages… It perhaps that these authors work in some smaller research communities or on very specialized research topics.

If I look more closely at the data (year 2020), I find that:
– about 0.6% of the researchers have more than 50% self-citations,
– about 4.2% have more than 30% citations
– about 13.7% have more than 20% self-citations
– about 48% have more than 10% self-citations

If I look at the top 30 persons in the ranking, the self-citations percentage is all below 15% and sometimes even below 1 %:

For me this makes sense. I think a young researcher will typically have more self-citations while a very famous researcher should have less self-citations and more from other people.

That is the most interesting thing that I have found so far in this data.

I did not do a very deep analysis of this data, as I am quite busy. But I just wanted to share a few observations. It would be interested to go beyond that and look for example at the data by countries or to draw some charts to see the distribution of values for different attributes, and to measure the correlation between attributes.. If you find something interesting in this data, you may share it 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.

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Brief report about EITCE 2021

Today, I attended the EITCE 2021 conference, which was held in Xiamen, China from the 22nd to 24th October 2021. The conference was held virtually and I participated as an invited keynote speaker. In this blog post, I will talk about the confernce.

About the conference

This is the 5th International Conference on Electronic Information Technology and Computer Engineering (EITCE 2021). It is a conference focused on computer science, that has been held in different cities in China such as Xiamen, Shanghai and Zhuhai.

The proceedings are published by ACM, and all papers are indexed by EI Compendex.

The conference was well organized, in part by a company called GSRA, and professors from Jimei University and Shanghai University of Engineering Science.

The website of the EITCE conference is : http://eitce.org/

Schedule of the first day

On the first day, there was five keynote speeches followed by paper presentations.

eitce 2021 schedule
eitce 2021 attendance online

The first keynote was by Prof. Sun-Yuan Kung from Princeton University (USA) and was about deep learning, and in particular neural architecture search (NAS). He discussed techniques to search for a good neural network architecture using reinforcement learning or other techniques.

The second keynote speaker was Prof. Dong Xu, from University of Missouri. It was about using graph neural networks for single cell analysis. This talk was more about bioinformatics, which is a bit far from what I do, but was interesting as an application of machine learning.

The third keynote speaker was Prof. Yuping Wang fromTulane University, USA. The talk was about Interpretable multimodal deep learning for brain imaging and genomics data fusion. A highlight of this talk was to say that interpretability is a challenge but is also very important for research on neural networks applied to real applications.

Then, there was my keynote (Prof. Philippe Fournier-Viger) about discovering interesting patterns in data using pattern mining algorithms.

The fifth keynote was by Prof. Yulong Bai.


That was an interesting conference, and I was happy to participate to it.

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

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CFP: The 1st Workshop on Pattern Mining and Machine Learning in Big Complex Database

This is to let you know about the upcoming 1st Workshop on Pattern mining and Machine learning in Big complex Databases (PMDB 2022) workshop that I co-organize at the DASFAA 2022 conference!

PMDB 2022 aims at providing a place for researchers from the fields of machine learning, pattern mining and database to present and exchange ideas about how to adapt and develop techniques to process and analyze big complex data.

The scope of PMDB 2022 encompasses many topics that revolves around database technology, machine learning, data mining and pattern mining. These topics include but are not limited to:

  • Artificial intelligence, machine learning and pattern mining models for analyzing big complex data
  • Database engines for storing and querying big complex data
  • Distributed database systems
  • Data models and query languages
  • Distributed and parallel algorithms
  • Real-time processing of big data
  • Nature-inspired and metaheuristic algorithms
  • Multimedia data, spatial data, biomedical data, and text
  • Unstructured, semi-structured and heterogeneous data
  • Temporal data and streaming data
  • Graph data and multi-view data
  • Uncertain, fuzzy and approximate data
  • Visualization and evaluation of big complex data
  • Predictive models for big complex data
  • Privacy-preservation and security issues for big complex data
  • Explainable models for big complex data
  • Interactive data analysis
  • Open-source software and platforms
  • Applications in domains such as finance, healthcare, e-commerce, sport and social media

The deadline for submiting papers is the 30th November 2021.

All accepted papers of PMDB 2022 will be published in Springer LNCS together with other DASFAA workshops. This will ensure good indexing in DBLP etc.

Hope to see your papers!

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

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Today, I will talk about something that I like very much, which is travelling. Being a researcher has allowed me to travel to numerous places around the world that I may not have discovered otherwise. In fact, I have attended many conferences around the world since I was a gradudate student and also visited many research labs, and gave invited talks in many locations. Besides, I have also travelled to many places as a tourist.

Totally, I have visited 25 countries. And in China, I have visited four territories that belong to China (mainland China, Taiwan, Hong Kong and Macau). Those 25 countries (including 4 territories of China) are pictured below:

And for more details, here are some of the cities that I have visited (the Map is generated by TripAdvisor, which allows users to create a map of visited cities).

As it can be seen, I particularly like Asia and Europe.

This is just a short blog post, to talk a little about something different. I miss very much travelling internationally due to the pandemic. But in the mean time, I have been travelling to several places around China, which is also very interesting for me, as it is a big country with many different things to see. I am looking forward to start travelling internationally again as there are still so many places to see around the world. Do you also like to travel? You may let me know 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.

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Towards SPMF v3.0…

Today, I will talk a little bit about the recent improvements and future direction for the SPMF data mining library.

towards SPMF 3.0

How SPMF started?

SPMF is a software project that I started around 2008 when I was a Ph.D student in Montreal, Canada. The short story of that software is as follows. I was taking a Ph.D course on data mining at University of Quebec at Montreal. For that course, I had to implement a few data mining algorithms as homework. I implemented some simple algorithms in Java such as Apriori and some code for discovering association rules. Then, I decided to clean the code, and add more algorithms during my free time, including those made for my PhD research. My idea was to make something for the pattern community in Java. In fact, most of the code that I could see online was written in C++… I wanted to change this so as to use my favorite language, Java. Besides, I wanted to share pattern mining code so that other researchers could save time by not having to implement again the same code. This is why all the code is open-source. Thus, it is around that time, in early 2009 that I created the website for SPMF and put the first version online. That version was simple. The code was not so efficient. Then, over the years the code has been optimized and more algorithms have been added, and luckily many researchers have joined this effort by providing code for many other algorithms such that today there are over 200 algorithms, many not available in other software programs. Besides, many other researchers have reported bugs and provided feedback to improve the software, which has been very useful to make the software very stable and bug-free. It is thanks to all contributors and SPMF users that the software is what it is today! Thanks!

What is the future?

The SPMF software is still very active. Just in the first eight months of 2021, about 20 algorithms have been added already. But there is many things to do to further improve the software:

  • I have been working on a plugin system that is not finished but will likely appear in a future version of SPMF when it is stable enough. This will allow to download plugins as jar files from online repositories and integrate them with SPMF. I have some version that is almost working but I want to make sure it is well-tested before it is released.
  • I also want to integrate some additional tools to automatically run experiments in SPMF to make it more convenient for researchers who want to compare algorithms.
  • There is also a need to add more algorithms to SPMF. Initially, SPMF was focused on frequent itemset mining, association rules and sequential patterns. In recent years, I have added code for many other related topics such as periodic patterns, sequential rules, frequent episodes, class association rules, pattern mining with a taxonomy, quantitative itemset mining, sequence prediction, high utility itemset mining and subgraph mining. But there are still several interesting topics that are not covered in SPMF and I would like to add in the future. For this, I need your help 😊 If anyone has code of algorithms in Java that is well implemented, you may let me know, and I may try to integrate your code in SPMF. It is not something difficult and you can become a contributor of SPMF too 😊.
  • I will eventually redesign the user interface to further improve it with more capabilities. The user interface has always been quite simple as the focus of the software is to provide an extensive library of algorithms. But it is perhaps time to add more functionalities to the user interface such to allow the user to combine several algorithms as a pipeline to process data, and to save that pipeline to a file.

Here is a picture of the system architecture of SPMF, including the planned plugin system:

Next step: SPMF 3.0

It is already a few years that SPMF 2.0 was released.  The next major version shall be SPMF 3.0 and hopefully it will be released early in 2022. 

For SPMF 3.0, I will also publish a new research paper about SPMF. For the version 0.9, a paper on SPMF was published in the Journal of Machine Learning Research. For the version 2.0, I published a paper in PKDD 2016. For version 3.0, I will also make a paper for another top journal or conference. The people who have contributed the most to SPMF in recent years will be invited to co-author that paper (as much as possible due to limitations on the number of authors).

For those who have observed, the convention for numbering versions of SPMF have changed quite a lot over the years. At the beginning, I started at 0.49, and incremented the numbers by 0.01. But I did not want to reach version 1.0 too early, so I then started to add letters like 0.96b, 0.96c,… 0.96r and then even some numbers after that like 0.96r2, 0.96r3, 0.96r4 to stay away longer from 1.0. The last version before 1.0 was 0.99j. Then after that I jumped to version 2.0 for the PKDD paper, and now I continued as 2.01, 2.02… 2.50. The next jump will be to 3.0 in the next few months.


In this blog post, I have talked a little bit about the early development and future direction of SPMF. Hope it has been interesting!

Thanks again to all contributors and users of SPMF for supporting the software through all these years. I really appreciate your support.

Philippe Fournier-Viger is a distinguished professor of computer science and founder of the SPMF open-source data mining library, which offers over 200 algorithms for pattern mining.

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Brief report about the DEXA 2021 and DAWAK 2021 conferences

In this blog post, I will talk about the DEXA 2021 and DAWAK 2021 conferences that I have attended, September 27–30, 2021. Those two conferences are co-located and co-organized every year in different countries of Europe. This year, these conferences were held virtually due to the COVID pandemic.

What is DEXA and DAWAK?

DEXA 2021 is the 32nd International Conference on Database and Expert Systems Applications. It is a conference oriented towards database technology and expert systems, but that also accepts data mining papers.

DAWAK 2021 is the 23rd International Conference on Big Data Analytics and Knowledge Discovery. The focus is similar to DEXA but more oriented towards data mining and machine learning. Several years ago, the DAWAK conference was named “Data Warehousing and Knowledge Discovery, hence DAWAK). But the name has changed in recent years.

The proceedings of DEXA and DAWAK are both published by Springer in the LNAI series, which ensures good visibility and indexing in EI, DBLP and other popular publication databases. The DEXA conference is older and viewed as a better conference than DAWAK by some researchers (e.g. in China, DEXA is ranked higher than DAWAK by the Chinese Computer Federation).

Personally, I enjoy the DEXA and DAWAK conferences. There are not so big but the paper are overall of good quality. Also, there is often some special journal issues associated with these conferences. I have previously attended these conferences several times. My report about previous editions can be found here: DEXA and DAWAK 2016, DEXA and DAWAK 2018, and DEXA and DAWAK 2019.

Acceptance rate

This year, 71 papers were submitted to DaWaK 2021. 12 papers were accepted as full papers and 15 as short papers. Thus, 16% is the acceptance rate for the full papers and 35% for both full and short papers.

The best papers of DAWAK were invited to submit an extended version in a special issue of the Data & Knowledge Engineering (DKE) journal.

For DEXA, I did not see the information about the number of submission in the front matter of the Springer proceedings. Usually this information is provided for conferences published by Springer. But this time, it is just said that “the number of submissions was similar to those of the past few years” and that “the acceptance rate this year was 27%. To estimate the number of submissions, I counted that there is about 67 papers in the proceedings. Thus, the number of submissions would be about 67 / 27 * 100 = 248 submissions.
Thus, this would be a 25% increase from last year, since in 2020, there was 197 submissions, in 2019, there was 157 submissions, and in 2018, there was 160 submissions.


On the first day, there was the opening session.

The program of the conference was presented, as well as the different organizers. It was said that this year there is a panel and five keynote speakers. Attendees were also asked to scan a QR during the opening to indicate their location, which generated the following word cloud:

Paper presentations

The paper presentations were done online using the Zoom software. There was a lot of interesting topics. Here is a screenshot of the first paper session on big data from DEXA 2021:

big data session at dexa 2021

I presented the paper of my student about episode rules. During that session, there about a dozen people and there was some interesting questions. Due to the schedule and time different, I was not able to attend all the paper presentations that I wished to attend, but I saw some interesting work.

Papers about pattern mining

This year, again, there was several papers about pattern mining at DEXA and DAWAK. Since it is one of my research area, I will report about these papers:

  • P. Revanth Rathan, P. Krishna Reddy, Anirban Mondal: Improving Billboard Advertising Revenue Using Transactional Modeling and Pattern Mining. 112-118
  • Yinqiao Li, Lizhen Wang, Peizhong Yang, Junyi Li: EHUCM: An Efficient Algorithm for Mining High Utility Co-location Patterns from Spatial Datasets with Feature-specific Utilities. 185-191
  • Yangming Chen, Philippe Fournier-Viger, Farid Nouioua, Youxi Wu: Mining Partially-Ordered Episode Rules with the Head Support. 266-271 [ppt] (paper from my team)
  • Xin Wang, Liang Tang, Yong Liu, Huayi Zhan, Xuanzhe Feng: Diversified Pattern Mining on Large Graphs. 171-184
  • So Nakamura, R. Uday Kiran, Likhitha Palla, Penugonda Ravikumar, Yutaka Watanobe, Minh-Son Dao, Koji Zettsu, Masashi Toyoda: Efficient Discovery of Partial Periodic-Frequent Patterns in Temporal Databases. 221-227
  • Amel Hidouri, Saïd Jabbour, Badran Raddaoui, Mouna Chebbah, Boutheina Ben Yaghlane: A Declarative Framework for Mining Top-k High Utility Itemsets. 250-256


On overall, it was a good conference. It is not so big but well-organized and with some good papers. I will certainly continue to send papers to that conference in the following years, and hopefully I can attend that conference in person next time. That would be much more interesting than a virtual conference because one of the best part about academic conferences is to be able to meet people and talk face to face.

Philippe Fournier-Viger is a distinguished professor of computer science and founder of the SPMF open-source data mining library, which offers over 200 algorithms for pattern mining.

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Finding short high utility itemsets!

Today, I will talk about pattern mining. I will explain a topic that is in my opinion very important but has been largely overlooked by the research community working on high utility itemset mining. It is to integrate length constraints in high utility itemset mining. The goal is to find patterns that have a maximum size, defined by the user (e.g. no more than two items).

Why do this?  There are two very important reasons.

First, from a practical perspective, it is often unnecessary to find the very long patterns. For example, let’s say that we analyze shopping data and find that a high utility pattern is that people buy {mapleSyrup, pancake, orange, cheese, cereal} together and that this yield a high profit. This may sound like an interesting discovery, but from a business perspective, it is not useful as this pattern contain too many items. For example, it would not be easy for a business to do marketing to promote buying 5 items together. This has been confirmed in my discussion with a business in real-life. I was told by someone working for a company that they are not interested in patterns with more than 2 or 3 items.

Second, finding the very long patterns is inefficient due to the very large search space. They are generally too many possible combinations of items. If we add a constraint on the length of patterns to be found, then we could save a huge amount of time to focus on the small patterns that are often more interesting for the user.

Based on these motivations, some algorithms like FHM+ and MinFHM have focused on finding the small patterns that have a high utility using two different approaches. In this blog post, I will give a brief introduction to the ideas from those algorithms, which could be integrated in other pattern mining problems.

First, I will give a brief introduction about high utility itemset mining for those who are not so familiar with this topic and then I will explain the solutions to find short patterns that are proposed in those algorithms.

High utility itemset mining

High utility itemset mining is a data mining task that aim at finding patterns in a database that have a high importance. The importance of a pattern is measured using a utility function. There can be many applications of high utility itemset mining, but the classical example is to find the sets of products purchased together by customers in a store that yield a high profit (utility). In that setting, the input is a transaction database, that is a set of records (transactions) indicating the items that some customers have bought at different times. For example, consider the following transaction database, which contains seven transactions called T1, T2, T3… T5:

transaction database for high utility mining

The second transaction T2 indicates that a customer has bought 4 units of an item “b” which stands for Bread and 3 units of an item “c”, which stands for Cake, 3 units of an item “d” which stands for Dates, and 1 unit of an item “e”, which stands for “Egg”. The second transaction contains 1 unit of an item “a”, denoting “Apple”, 1 cake and 1 unit of Dates.  Besides, that table, another table is provided indicating the relative importance of each item. In this example, that table indicate the unit profit of each item (how much money is earned by the sale of 1 unit):

unit profit table

This table for example indicates that the sale of 1 Apple yields a 5$ profit, the sale of 1 bread yields 2$ profit, and so on.

To do the task of high utility itemset mining, the user must set a threshold called the minimum utility threshold (minutil). The goal is to find all the itemsets (sets of items) that have a utility (profit) that is no less than that threshold. For example, if the user set the threshold as minutil = 33$, there are four high utility itemsets:

The first itemset {b,d,e} means that customers buying Bread, Dates and Eggs together yield a total utility (profit) of 36$ in this database. It is a high utility itemset because 36$ is no less than minutil = 33$.  But how do we calculate the utility of an itemset in a database? It is not very complicated. Let me show you. Let’s say that we take the itemset {b,d,e} as example. These items are purchased together in the transactions T1 and T2 of the database, which are highlighted below:

quantitative transaction database with b,c,d highlighted

To calculate the utility of {b,d,e}, we need to multiply the quantities associated with b,d,e in T1 and T2 by their unit profit. This is done as follow:

In T1, we have: (5 x 2) + (3 x 2) + (1 x 3) = 19 $  because the customer bought 5 breads for 2$ each, 3 dates for 2 $ each and 1 egg for 1 $.

In T2, we have (4 x 2) + (3 x 2) + (1 x 3) = 17 $  because the customer bought 5 breads for 2$ each, 3 dates for 2 $ each and 1 egg for 1 $.

Thus, the total profit of {b,d,e} for T1 and T2 is 19$ + 17 $ = 36 $.

The problem of high utility itemset mining has been widely studied in the last two decades. Besides the example of shopping above, it can be applied to many other problems as the letters like a,b,c,d,e could represent for example webpages or words in a text. There has been many efficient algorithms that have been designed for high utility itemset mining such as IHUP, UP-Growth, HUI-Miner*, FHM, EFIM, ULB-Miner and REX to name a few. If you are interested by this topic, I wrote a good survey that introduce the problem in more details and it is easy to understand for beginners in this field.

Finding the Minimal High Utility Itemsets with MinFHM

As I said in the introduction, a problem with high utility itemset mining is that many high utility itemsets are very long and thus not useful in practice. This leads to finding too many patterns and to very long runtimes.

The first solution to this problem was proposed in the MinFHM algorithm. It is to find the minimal high utility itemsets. A minimal high utility itemset is simply a high utility itemset that is not a subset of a larger high utility itemset.  This definition allows to focus on the smallest sets of items that yield a high utility (e.g. profit in this example). For example, if we take the same database and minutil = 33$, there are only threeminimal high utility itemsets:

minimal high utility itemsets

The itemset {b,c,d,e} is not a minimal high utility itemsets because it has subsets such as {b,d,e} that are high utility itemsets.

To find the minimal high utility itemsets, MinFHM is a modified version of the FHM algorithm. It relies on search space reduction techniques that are specially designed to find the minimal high utility itemsets. This led to not only finding less patterns than FHM but also on having much faster runtimes. On some benchmark datasets, MinFHM was for example up to 800 times faster than FHM and could find up to 900,000 times less patterns.  

For researchers, something interesting about the problem of minimal high utility itemsets is the following two properties, which are somewhat special for this problem:

minimal high utility itemsets properties

I will not talk too much about the details of this as my goal is just to give some introduction. For more details about MinFHM, you can see the paper, powerpoint, video presentation and source code, below:

Fournier-Viger, P., Lin, C.W., Wu, C.-W., Tseng, V. S., Faghihi, U. (2016). Mining Minimal High-Utility Itemsets. Proc. 27th International Conference on Database and Expert Systems Applications (DEXA 2016). Springer, LNCS, pp. 88-101. [ppt] [source code]

DOI: 10.1007/978-3-319-44403-1_6

Finding the High Utility Itemsets with a length constraint with FHM+

Now, let me talk about another solution to find the short high utility itemsets. This solution consists of simply adding a new parameter that sets a maximum length on the patterns to be found. For example, if take the same example and say that minutil = 33$ and the maximum length is 3, then the following three high utility itemsets are found:

minimal high utility itemsets

In this example, the results is the same as the minimal high utility itemsets but it is not always the case.

To find the high utility itemsets with a length constraint, a naïve solution is to filter out the high utility itemsets that are too long as a post-processing step after applying a traditional high utility itemset mining algorithm such as FHM. However, that would not be efficient. For this reason, I have proposed the FHM+ algorithm in previous work. It is a modified version of FHM.  The key idea is as follows. The FHM algorithm just like other high utility itemset mining algorithms uses upper bounds on the utility to reduce the search space such as the TWU and remaining utility (which I will not explain here). These upper bounds are defined by assuming that all items of a transaction could be used to create high utility itemsets. But if we have a length constraints and know that lets say we don’t want to find patterns with more than 3 items, then we can greatly reduce these upper bounds. This allows to reduce a much larger part of the search space and thus to have a much faster algorithm!

In the FHM+ paper, I have shown that using these ideas, the memory usage can be reduced by up to 50%, the speed can be increased by up to 4 times and up to 2700 times less patterns can be discovered, on benchmark datasets!

This is just a brief introduction, and these ideas could be used in other pattern mining problems. For more details, you may see the paper, powerpoint presentation and code below:

Fournier-Viger, P., Lin, C.W., Duong, Q.-H., Dam, T.-L. (2016). FHM+: Faster High-Utility Itemset Mining using Length Upper-Bound Reduction. Proc. 29th Intern. Conf. on Industrial, Engineering and Other Applications of Applied Intelligent Systems (IEA AIE 2016), Springer LNAI, pp. 115-127. [ppt] [source code]


In this blog post, I have explained why it is unnecessary to find the very long patterns in high utility itemset mining for some applications such as analyzing customer behavior. I have also shown that if we focus on short patterns, we can greatly improve the runtimes and also reduce the number of patterns shown to the user.  This can bring the algorithms for high utility itemset mining closed to what users really need in real-life. I have discussed two solutions to find short patterns, which are to find minimal high utility itemsets and using a length constraint.

That is all for today!

Philippe Fournier-Viger is a distinguished professor of computer science and founder of the SPMF open-source data mining library, which offers over 200 algorithms for pattern mining.

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

My webpage from 2006 to now…

Today, I will talk about the design of my personal research webpage, which has evolved over the years from 2006 (the first year of my PhD) til today (2021). It is around 2006 that I decided to buy a .com domain name to make a webpage. My goal at that time was to have a Web presence so that people could easily find the PDFs of my research papers and also read about my research. The design of my webpage did not change so much over the years, as you can see below:

On the top left, it is the first version of my webpage, with a white background. That webpage was HTML 4 compliant and had a few subsections like “Main”, “Publications”, “Software” and “About me”. From 2006 to 2009, I made minor changes to the website, mainly to update my list of papers, change my picture (a few times) and add some other information. Then, around 2012, a student from Algeria, Hanane Amirat, gently offered to redesign my website, with a colored background as can be seen on the top right, which made it look better. At that time, I was also starting to work as professor and added more sections to my website, including a link to this blog. Then, around 2020, I redesigned the website again to make the site suitable for mobile devices, as search engines started to take this into account. This version can be seen at the bottom left. That version from 2020 looks almost the same as the 2017 version but under the hood, I have modified the website to use a responsive design template so that the menu can be dynamically resized on mobile devices.

Do you like the latest version of the website? If not, or if you have some suggestions to improve it, please leave a comment below 🙂 Maybe it is time to change the design again 🙂 In fact, I feel that the website colors are a little bit dark. Maybe it would be time to change to another design…

That is all I wanted to share today. If you are a researcher and do not have a website yet, I recommend to make one , or at least to have a page on websites such as ResearchGate and LinkedIn. This will bring more visibility to your research work!

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

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