Using information retrieval techniques for supporting data mining

  • Authors:

  • Ioannis N. Kouris;Christos H. Makris;Athanasios K. Tsakalidis

  • Affiliations:

  • Department of Computer Engineering and Informatics, School of Engineering, University of Patras, 26500 Patras, Hellas, Greece and Computer Technology Institute, P.O. Box 1192, 26110 Patras, Hellas ...;Department of Computer Engineering and Informatics, School of Engineering, University of Patras, 26500 Patras, Hellas, Greece and Department of Applied Informatics in Management and Finance, Techn ...;Department of Computer Engineering and Informatics, School of Engineering, University of Patras, 26500 Patras, Hellas, Greece and Computer Technology Institute, P.O. Box 1192, 26110 Patras, Hellas ...

  • Venue:
  • Data & Knowledge Engineering
  • Year:
  • 2005

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Abstract

The classic two-stepped approach of the Apriori algorithm and its descendants, which consisted of finding all large itemsets and then using these itemsets to generate all association rules has worked well for certain categories of data. Nevertheless for many other data types this approach shows highly degraded performance and proves rather inefficient.We argue that we need to search all the search space of candidate itemsets but rather let the database unveil its secrets as the customers use it. We propose a system that does not merely scan all possible combinations of the itemsets, but rather acts like a search engine specifically implemented for making recommendations to the customers using techniques borrowed from Information Retrieval.