Compact Tree for Associative Classification of Data Stream Mining

The data streams have recently emerged to address the problems of continuous data. Mining with data streams is the process of extracting knowledge structures from continuous, rapid data records [1]. An important goal in data stream mining is generation of compact representation of data. This helps in reducing time and space needed for further decision making process. In this paper we propose a new scheme called Prefix Stream Tree (PST) for associative classification. This helps in compact storage of data streams. This PSTree is generated in a single scan. This tree efficiently discovers the exact set of patterns from data streams using sliding window.