Hashing for Fast Pattern Set Selection
This work addresses efficiency in pattern set selection for data mining applications like tiling databases and Boolean matrix factorization, representing an incremental improvement over existing methods.
The paper tackles the problem of efficiently selecting a good set of patterns in data mining by using reconstruction error as a measure, proposing a bottom-k hashing method that is significantly faster than standard greedy algorithms while achieving nearly equivalent results on synthetic and real-world datasets.
Pattern set mining, which is the task of finding a good set of patterns instead of all patterns, is a fundamental problem in data mining. Many different definitions of what constitutes a good set have been proposed in recent years. In this paper, we consider the reconstruction error as a proxy measure for the goodness of the set, and concentrate on the adjacent problem of how to find a good set efficiently. We propose a method based on bottom-k hashing for efficiently selecting the set and extend the method for the common case where the patterns might only appear in approximate form in the data. Our approach has applications in tiling databases, Boolean matrix factorization, and redescription mining, among others. We show that our hashing-based approach is significantly faster than the standard greedy algorithm while obtaining almost equally good results in both synthetic and real-world data sets.