A decremental algorithm of frequent itemset maintenance for mining updated databases

Authors:
Shichao Zhang;Jilian Zhang;Zhi Jin
Affiliations:
Department of Computer Science, Zhejiang Normal University, PR China;College of Computer Science and Information Technology, Guangxi Normal University, PR China;College of Information Technology, Peking University, PR China
Venue:
Expert Systems with Applications: An International Journal
Year:
2009

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Abstract

Data-mining and machine learning must confront the problem of pattern maintenance because data update 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 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.