A few days ago, I have read a post on LinkedIn showing that the number of Machine Learning (ML) papers has been increasing very quickly over the last few years to about 100 ML papers per day (on Arxiv, a popular public repository of research papers). That is about 33,000 papers per year. This shows the excitement about the new advances in that field in particular with respect to deep learning that has lead to obtaining good results for various applications. Some people on LinkedIn wondered if there are too many ML papers and how they could keep up with advances in that field. I will make a few comments about this.

In this blog post, I will give a brief overview of an important subfield of data mining that is called pattern mining. Pattern mining consists of using/developing data mining algorithms to discover interesting, unexpected and useful patterns in databases. Pattern mining algorithms can be applied on various types of data such as transaction databases, sequence databases, streams, strings, spatial data, graphs, etc. Pattern mining algorithms can be designed to discover various types of patterns: subgraphs, associations, indirect associations, trends, periodic patterns, sequential rules, lattices, sequential patterns, high-utility patterns, etc. But what is an interesting pattern? For example, some researchers define an interesting pattern as a pattern that appears frequently in a database. Other researchers wants to discover rare patterns, patterns with a high confidence, the top patterns, etc.