A Model for Managing Collections of Patterns

TL;DR

This paper introduces a framework with an efficient data structure and algebraic operators to help end users manage, retrieve, and analyze large collections of patterns extracted from data mining processes.

Contribution

It presents a novel framework and data structure enabling end users to effectively manage and access pattern collections through algebraic operations.

Findings

Efficient data structure for pattern management

Algebraic operators for pattern retrieval

Enhanced user control over pattern analysis

Abstract

Data mining algorithms are now able to efficiently deal with huge amount of data. Various kinds of patterns may be discovered and may have some great impact on the general development of knowledge. In many domains, end users may want to have their data mined by data mining tools in order to extract patterns that could impact their business. Nevertheless, those users are often overwhelmed by the large quantity of patterns extracted in such a situation. Moreover, some privacy issues, or some commercial one may lead the users not to be able to mine the data by themselves. Thus, the users may not have the possibility to perform many experiments integrating various constraints in order to focus on specific patterns they would like to extract. Post processing of patterns may be an answer to that drawback. Thus, in this paper we present a framework that could allow end users to manage collections of patterns. We propose to use an efficient data structure on which some algebraic operators may be used in order to retrieve or access patterns in pattern bases.