# A Model For Managing Collections Of Patterns

Baptiste Jeudy, Christine Largeron, and Fran¸cois Jacquenet Laboratoire Hubert Curien, UMR CNRS 5516 Univ. of St-Etienne, France 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.

## 1 Introduction

The amount of information that has been stored in data bases all around the world has continously increased among the years. In order to explore these potential mines of knowledge, efficient data mining tools have been designed for many years. Hence, it is now possible to mine huge databases in order **to extract**
various kinds of patterns, modeling some knowledge. Depending on the algorithms used by end users for their needs, patterns may be varied, **we may cite**
for example decision trees, association rules, formal concepts, etc. While mining huge databases is becoming a common task for many users, those one are now faced with a new problem: how can they exploit the large amount of patterns that are commonly extracted by the data mining tools. Indeed, in the same way it was impossible to manually extract knowledge from huge databases, it is now impossible to manage large volumes of patterns and the end users are in need of new tools in order to do that.

In fact two approaches have been proposed to users in order to manage and explore what is commonly called Pattern Bases. The first one is based **on the**
concept of inductive databases [8, 2, 13, 12]. In Europe, the CInQ project1 has played a dynamic role in researches in that domain. An inductive database not 1 http://www.cinq-project.org/
arXiv:0902.1080v1 [cs.AI] 6 Feb 2009