Summarizing association itemsets by pattern interestingness in a data stream environment

Abstract

In the age of Knowledge economy, people are paying more attention to data mining. However, the number of the mined association patterns often exceeds the capacity of human's mind. Therefore, it is necessary for effectively present patterns according to their interestingness. This approach focuses on continuously differentiating interesting and valuable patterns from data stream and proposes a new data structure, Pattern's Interestingness Tree (PI-Tree) for discovering frequent patterns and helping to distinguish interesting knowledge. Performance Analysis indicates that the proposed approach is efficient for IOKD. © 2011 Springer-Verlag.