Mining frequent similar patterns on Mixed Data

Abstract

Frequent Pattern Mining is an important task due to the relevance of repetitions on data, also it is a fundamental step in the Association Rule Mining. Most of the current algorithms for mining frequent patterns assume that two object subdescriptions are similar if and only if they are equal, but in soft sciences some other similarity functions are used. In this work, we focus on the search of frequent patterns on Mixed Data, incorporating similarity between objects. We propose a novel and efficient algorithm to mine frequent similar patterns for a family of similarity functions that fulfill Downward Closure property and we also propose another algorithm for the remaining families of similarity functions. Some experiments over mixed datasets are done, and the results are compared against the ObjectMiner algorithm. © 2008 Springer-Verlag Berlin Heidelberg.