Condensed representations of frequent sets : application to descriptive pattern discovery
Artur Bykowski · 2002
Interesting pattern discovery has recently seen an impressive progress, due to an increasing pressure from owners of large data sets and to the response of scientists by numerous theoretical and practical results. The most of data sets addressed in the beginning of the surge were sales data and the interesting patterns were in form of association rules. Very efficient solutions to this practical problem were elaborated, the root of them was the so-called APRIORI algorithm. Then, the owners of other types of data wondered if these basic methods could help them. Unfortunately, their data were different. Often, these applications could not take advantage of APRIORI. The research following the elaboration of the basic solution addressed the important application areas, where the basic solution could not be used. We addressed the problems with mining frequent patterns in different applicative contexts, especially the problems related to the large number of interesting frequent patterns present in data that are not similar to the sales data. Our methods mine a collection of patterns that may be quite different from the target pattern collection, and hopefully much more efficient to be mined in some types of data. Moreover, that different pattern collection must allow a subsequent regeneration of the target collection in a very efficient manner. Since the intermediate representation will be often smaller than the target collection, we call it a condensed representation. We obtained a significant improvement of the performances. The use of condensed representations is relatively novel in the field. Then new major condensed representations of simple frequent patterns are proposed, the algorithms to mine them and derive the target pattern collections. We show the practical advantages of the proposed condensed representations over the past methods, and provide an abstract view of the proposed representations in the unified structure for condensed representations.