A decremental algorithm of frequent itemset maintenance for mining updated databases

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.