Hi all, I have not written much on the blog in the last month because I have been very busy with numerous projects and deadlines. I thus took a small break to focus on other things. Now that I have … Continue reading

# Tag Archives: frequent pattern mining

In this blog post, I will share another talk that I have recorded recently. This time, I will explain a new paper from my team about discovering cost-effective patterns using some algorithms called CEPB and CEPN. Mining cost-effective patterns is … Continue reading

Today, I will share a short keynote talk (28 min) about discovering interpretable high utility patterns in data that I have presented at the CCNS 2020 conference. This talk gives an overview of techniques for finding interesting and useful patterns … Continue reading

This year, we are in 2019, and it is already 25 years since Agrawal wrote his seminal papers on frequent itemset mining and association rule mining in 1994. Since then, there has been thousands of papers published on this topic, … Continue reading

In this blog post, I will briefly discuss the fact that the popular CloSpan algorithm for frequent sequential pattern mining is an incomplete algorithm. This means that in some special situations, CloSpan does not produce the expected results that it has been designed for, and … Continue reading

In this blog post, I will give an introduction to sequential pattern mining, an important data mining task with a wide range of applications from text analysis to market basket analysis. This blog post is aimed to be a short … Continue reading

In this blog post, I will give an introduction about a popular problem in data mining, which is called “high-utility itemset mining” or more generally utility mining. I will give an overview of this problem, explains why it is interesting, and provide source code of … Continue reading

Today, I will do a quick post on how to automatically adjust the minimum support threshold of frequent pattern mining algorithms such as Apriori, FPGrowth and PrefixSpan according to the size of the data. The problem is simple. Let’s consider … Continue reading