A New Approach for Mining Incrementally Closed Itemsets over Data Streams

Abstract

Incremental mining always requires an intermediate structure to store the results of the previous steps and update the results of the current step based on this structure. In particular, over data streams, the intermediate structure needs to be particularly effective because of the following characteristics of data streams: the size of input data is not limited; the use of main memory is limited; input data can only be processed once; the appearing speed of new data is fast; system can not control the appearing order of incoming data; analytical results generated by algorithms must be available immediately upon user request; errors of analysis results must be bounded in a range acceptable to users. In the previous study, the author proposed an intermediate structure called constructive set. In this paper, the author proposes applying the constructive set and two incremental algorithms to the problem of mining closed sets over data streams.