A decremental algorithm for maintaining frequent itemsets in dynamic databases

Authors:
Shichao Zhang;Xindong Wu;Jilian Zhang;Chengqi Zhang
Affiliations:
,Faculty of Information Technology, University of Technology Sydney, Australia;Department of Computer Science, University of Vermont;Department of Computer Science, Guangxi Normal University, China;Faculty of Information Technology, University of Technology Sydney, Australia
Venue:
DaWaK'05 Proceedings of the 7th international conference on Data Warehousing and Knowledge Discovery
Year:
2005

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Abstract

Data mining and machine learning must confront the problem of pattern maintenance because data updating is a fundamental operation in data management. Most existing data-mining algorithms assume that the database is static, and a database update requires rediscovering all the patterns by scanning the entire old and new data. While there are many efficient mining techniques for data additions to databases, in this paper, we propose a decremental algorithm for pattern discovery when data is being deleted from databases. We conduct extensive experiments for evaluating this approach, and illustrate that the proposed algorithm can well model and capture useful interactions within data when the data is decreasing.