Today, I want to show you some new features in the upcoming version of SPMF 2.66. The visual pattern viewer is now improved to show larger set of patterns using pagination. This means that if you load an output file containing a very large number of patterns, they will be displayed using pages rather than all within the same window. To illustrate this, here is a screenshot of this new feature:
In this example, we are visualizing association rules, and there are 56 association rules. The user chooses to display 10 rules per page. Hence, there are 6 pages of association rules. The user can change the number of patterns displayed by page. And this works also for other pattern types such as itemsets and sequential patterns. Moreover, this also works when the user is applying filters or searching for specific items within the patterns.
This is just to show you a preview of upcoming features!
Today, I will continue to talk about improvements to the user interface of SPMF, for the upcoming version 2.66. I have just added a new Run history panel. To access it the user will click here:
And then, the run history is displayed:
And it is possible to run the same algorithm again with the same parameters, and see all algorithms that have been run in the current session, and to clear the history.
Another update(2026-06-05)
I have further improved the user interface today, here is a new version, where there are now buttons for selecting recently used algorithms, input, and output files. This is very useful for users.
Also, the combobox for selecting an algorithm has been replaced by a more intuitive algorithm selection dialog that displays algorithms by category and has a search bar as it was cumbersome to scroll through hundreds of algorithms. Here is a screenshot:
And finally, when an algorithm is run, there is a preview of the output file in the bottom tabs:
From this tab, using the button “Open output file“, it is possible to open the last generated output file using different viewers without having to run the algorithm again. And there is also a new button to open the folder containing the output file.
This is just to show you some new feature in the upcoming version of SPMF! Have a good day!
It is a long time that I did not update the main window in the user interface of SPMF. In the next version of SPMF that will be released this month (v.2.66), I have started to make some improvements. Here is a screenshot:
Can you see some improvements?
First, I have added icons for the file selection buttons instead of using text “…”.
Second, the parameter selection table is now visually more beautiful and the columns can be resized, and columns have clearer headers (“Parameter”, “Value” and “Example”).
Third, now it is possible to type the file path directly for the input and output files.
Fourth, the progress bar now show some text for more clarity:
And finally, the window can be resized.
Conclusion
I just wanted to show you this for today! I you have any suggestions, please leave me a message as a comment here or directly by email.
Today, I will talk briefly about a general topic related to research which is academic genealogy. Just as families pass knowledge and traditions across generations, researchers also inherit ideas, methods, and scientific values from their supervisors and mentors. In many fields, tracing academic genealogy offers a glimpse into how research communities evolve over time and how knowledge spreads across countries and institutions.
Usually academic genealogy relationships are defined by the PhD supervisor relationship with PhD students.
For instance, after searching online （there are different websites), my own academic genealogy is as follows:
Claude Frasson — Doctorat d’État, Université de Nice-Sophia Antipolis (devenue l’Université Côte d’Azur), France, 1981
Roger Nkambou — Doctorat, Université de Montréal, Canada, 1996
Philippe Fournier-Viger — Université du Québec à Montréal, Canada, 2010
This genealogy reflects an academic lineage spanning France and Canada, from various topics such as intelligent tutoring systems and artificial intelligence to data mining and pattern mining research.
May I could find more my own academic genelogy beyond 1981… But this would require a deeper search, and it is not so easy to go back in time.
That’s all for the blog post today. If you have time, you may ask your own PhD supervisor about his academic genealogy!
Today, I want to announce that I have included a compiled EXE version of SPMF.jar for Windows 64 bits on the Download page of SPMF. It is especially useful if you cannot or do not want to install Java on a computer.
This portable EXE version of SPMF is slightly bigger (55 mb instead of around 11 mb) because it includes the Java runtime environment.
You can download it from the download page on the website of SPMF:
Later, I might also include a compiled version for Linux and other platforms, if some people request it.
Over the years, I have developed the SPMF library with collaborators as a comprehensive toolkit for pattern mining, with the goal of making a large number of algorithms easily accessible for research and teaching. While SPMF has been widely used in its Java form and sometimes in other languages through wrappers or its command line interface, I have often felt the need to provide more ways to interact with SPMF.
Hence, I have started a new project a few months ago, but just released a first version today: SPMF-Server. The idea is to provide a REST server implemented in Java that allows to query the SPMF library remotely or on the same computer from any language by submitting jobs to the server. The idea behind SPMF-Server is simply to decouple the mining engine from the client code. Instead of embedding SPMF directly, a user can run a server and interact with it through HTTP requests. This is an early work and comments are welcomed. In this blog post, I will give an overview
General Idea
SPMF-Server is a small REST API that exposes the algorithms of SPMF. A client sends can query it by sending the name of an algorithm, its parameters, and some input data, and the server executes the task and returns the result. Each job is executed in a separate Java process, and the client can query its status, retrieve the output, and inspect the console logs if needed. The interaction is intentionally simple and based on JSON. The overall architecture can be seen as follows:
SPMF-Server 1.0 and SPMF-Server-Clients 1.0
This is an initial release. The core features are implemented, including: submitting mining tasks, monitoring their execution, retrieving results and execution logs, configuration through a properties file. At this stage, the project should be seen as a working prototype and I will do further testing and improvements to fix bugs and improve performance. SPMF-server can run as a headless server or through a GUI:
SPMF-Server 1.0
Alongside with the server, I have also started a separate project providing two simple Python clients to connect to the SPMF-Server. The first one offers a commandline interface while the second one provides a GUI. Below are a few screenshots of the GUI. This is the algorithm selection tab:
This is the tab for running a job by selecting a file and algorithm parameters:
This is the tab for viewing results from any job that was run on the server:
How to try ?
You can download both the SPMF-Server and SPMF-Server clients from these Github repositories:
Here is a screenshot of the SPMF Server Web client:
And here is a screenshot of the SPMF Server Python GUI Client:
Conclusion
In the future, there will be also clients for other programming languages, and the overall idea will be improved. If you try it and encounter issues or have suggestions, I would be very interested in hearing your feedback. Any comments are welcome!
In this post, I want to talk about a special session on Knowledge Science and Intelligent Computing (KSIC) that I am co-organizing this year at the SOMET 2026 conference (25th Int. Conf. on Intelligent Software Methodologies, Tools, and Techniques). I would like to invite you to submit your research papers!
The conference proceedings will be published by IOS Press and indexed in SCOPUS. The important dates are:
Deadline:April 14, 2026 (updated)
Notification to authors: May 10, 2026.
Camera-Ready papers: June 15, 2026.
Relevant topics include, but are not limited to, the following:
Knowledge Reasoning and Representation
Knowledge-based software engineering.
Knowledge Representation and Reasoning
Knowledge engineering application.
Ontological engineering.
Symbolic reasoning in Large Language Models
Reality automated generation
Cognitive foundations of knowledge
Intelligent systems.
Intelligent Information Systems.
Robotics and Cybernetics.
Distributed and Parallel Processing.
Aspects of Data Mining.
Bio-informatics.
Knowledge extraction from text, video, signals and images.
Search and Mining of variety of data including scientific and engineering, social, sensor/IoT/IoE.
Intelligent Computational Modeling.
Mobility and Big Data.
Session Organizers ▪ Nhon V. Do, Hong Bang International University, Vietnam. ▪ Philippe Fournier-Viger, Shenzhen University, China. ▪ Hien D. Nguyen, University of Information Technology, VNU-HCM, Vietnam.
For more information about the special session and conference, click here.
Today, I want to announce that a new version of the SPMF data mining library and software has been released, which is version 2.65. This version bring several improvements, including 8 new algorithms, several optimizations, and new user interface tools for data analysis, and some tools for data processing. The details of this new version can be found on the download page of SPMF. Here is a brief overview.
Eight new algorithms:
the LinearTable algorithm for mining frequent itemsets, which can work especially well when the number of items is relatively small. This algorithm has very low memory usage in some cases (Lu et al. 2023)
The SAM algorithm for mining frequent itemsets (Borgelt et al., 2009)
The TM algorithm for mining frequent itemsets (Song et al., 2006)
The NEWCHARM algorithm for mining frequent closed itemsets (Ye et al., 2015)
The DBVMiner algorithm for mining frequent closed itemsets (Vo et al., 2012)
The FTARM algorithm for top-k association rule mining, which is a variation of ETARM with additional strategies (Liu et al., 2019)
The ETARM algorithm for top-k association rule mining, which is a variation of TopKRules with additional pruning strategies (Nguyen et al., 2017)
The AprioriTID_HD algorithm, a modification of AprioriTID for better performance (thanks to Harshil Damania for proposing this improvement )
Performance improvement
I have added several optimizations to improve the performance of algorithms such as Apriori, AprioriClose, Eclat, Relim, AprioriInverse, AprioriRare, AprioriTopK, dEclat, Charm, dCharm, TopKRules, TopKClassRules, etc. In some case, the speed can be improved by several times and the memory performance reduced considerably.
New user interface tools
One new user interface tools is the Itemset-Item Matrix Viewer, which allows to visualize the relationship and similarities between itemsets discovered by itemset mining algorithms. Here is a screenshot:
There is also a new Item Co-Occurrence HeatMap Viewer to visualize co-occurrences between items in transaction databases. For example, here is a visualiztion of the co-occurrences of the top 20 most frequent items in the Chess dataset:
I have also added panels in the dataset viewers to provide interesting statistics about datasets. For example, for the Transaction dataset viewer:
Bug fixes
I have also fixed various small bugs.
Conclusion
This is just a quick overview of this new version of the SPMF pattern mining software, version 2.65. Thanks again to all users of SPMF and contributors for your support!
Today, I will talk to you about a new for visualizing itemsets that will be released soon in SPMF 2.65. This tool called the Itemset-Item Matrix Viewer will be available for any algorithm that produce itemsets to visualize results in an intuitive way. To use it, the user will have to select an itemset mining algorithm and then select the Itemset-Item Matrix Viewer as the method for visualizing the result:
Then, after running the algorithm, the matrix viewer will be opened to display the results. For example, lets say that I run the Apriori algorithm to mine frequent itemsets in a simple dataset. The interface of the Matrix Viewer will present the itemsets found as follow:
I will explain the main features. First, on the right, there is a matrix view, where rows represent itemsets and columns represent items from the dataset. The presence of an item in an itemset is represented by a colored blue rectangle:
This matrix representation is useful as it allows to quickly see items that are common to different itemsets and to have a clear representation of the size of an itemset. Besides, on the left panel of the interace, it is possible to filter itemsets by item. For example, here I apply a filter to display only patterns with Apple:
It is also possible to filter patterns by size. For example, I here apply a filter to see only patterns with 2 to 3 items:
Besides that another interesting features is to highlight the subsets and supersets of the currently selected itemset. For example:
This matrix viewer is quite intuitive and can display larger set of patterns as well. For example, here is an example with high utility itemsets found in the foodmart dataset with hundreds of patterns:
Note that in SPMF, the itemset can also be displayed using the Visual Pattern Viewer, introduced last year in SPMF:
This is just a brief overview of what is upcoming in SPMF. More algorithms and features will be coming soon… I will give you more details later.
Thanks again to all users and contributors of SPMF.
Just a short blog post today to wish you happy holidays and Merry X-Mas to those who are celebrating it, among the users of SPMF! Thanks again for your support!
