The Data Blog

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:

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 two Github repositories:

• SPMF Server: https://github.com/philfv9/spmf-server
• SPMF Server clients in Python (GUI and CLI interfaces): https://github.com/philfv9/spmf-server-pythonclient

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!

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.

In November, I released the version 2.64 of SPMF with 5 new algorithms and 2 new tools, among other improvements, but I still had several other pattern mining algorithms waiting to be released after more tests.

Thus, today, I make a new release of SPMF for December 2025, called version 2.64b. This release has several new algorithms:

• The HMP-SA algorithm for discovering compressing itemsets in a transaction database using a simulated annealing approach (Chen, E. et al., 2026).
• The HMP-HC algorithm for discovering compressing itemsets in a transaction database using a hill-climbing approach (Chen, E. et al., 2026).
• the GENMAX algorithm for mining frequent maximal itemsets from a transaction database (Gouda et al., 2005)
• the DIC algorithm for mining frequent itemsets in a transaction database using dynamic itemset counting (Brin et al. 1997)
• the Talky-G and Talky-G-Diffset algorithms for mining frequent generator itemsets in a transaction database (Szathmary, 2009)
• the iMEFIM algorithm for high utility itemset mining, a variation of EFIM that can be faster but can consume more memory (Nguyen et al., 2019)
• the PUCPMiner algorithm for high utility itemset mining, a variation of FHM with a potentially tighter upper bound (Patel et al., 2022

Moreover, there is a new Pattern Diff Analyzer tool that allows to compare two pattern files to find contrast patterns. This tool works letting the user first select two files containing patterns:

And then we can compute the differences between these files using different contrast methods. For example, we can identify all patterns that are in the first file but not in the second, among multiple other options for identifying contrast patterns:

That is all I wanted to share about this new version! Hope that you will enjoy this new release of the SPMF pattern mining software. So far we have released 18 new algorithms this year! And more features are coming soon…

Today, I want to talk to you about something quite incredible. I recently attended a keynote speech at an international conference. The keynote speaker was presenting online and was scheduled for a 35 minute presentation. However, he had prepared 180 slides. Yes, you have read that correctly: 180 slides!

This is a ridiculous number of slides for a talk. Generally, a good rule of thumb for a presentation is to prepare one slide per minute. If we follow this rule, a 35 minute presentation should have 35 slides. So, as I would expect in such case, the talk went overtime…

After 1 hour (already 25, the speaker was at slide 140, while going at a speed of 2.3 slides per minute!

Then, a few minutes later, the organizer intervened to stop the talk. And the speaker said sorry for going overtime…. But seriously, how can you be sorry when you prepare 180 slides.

I have seen some speaker exceed the time allowed at several conferences. However, usually, it is by 5 or 10 minutes, rarely by over half an hour. And if he had not been stopped, at this speed, it would have taken almost 1 hour and half to finish the presentation.

Personally, I do not understand this. If you are invited to give a talk at a conference, I think it is important to follow the schedule, so as to show respect to the organizers, to the audience and to the speakers that will talk after you. Because of that speaker, other speakers had to shorten their presentations, and the coffee break had to be shortened, as well.

What’s more, it seems that the professor was well aware that he had too many slides as the title of his presentation was ironically titled “The too short keynote speech of Prof (…. ) “, as you can see in the picture below:

I will not say the name of the speaker. But he is a senior Jordanian professor with over 20,000 citations.

So my goal by telling this story is just to remind everyone that it is important to follow the time constraints at conferences. It shows respect and professionalism. That is all I wanted to say for today! If you want to share your opinion, please leave a comment below.