User subjectivity in change modeling of streaming itemsets

Abstract

Online mining of changes from data streams is an important problem in view of growing number of applications such as network flow analysis, e-business, stock market analysis etc. Monitoring of these changes is a challenging task because of the high speed, high volume, only-one-look characteristics of the data streams. User subjectivity in monitoring and modeling of the changes adds to the complexity of the problem. This paper addresses the problem of i) capturing user subjectivity and ii) change modeling, in applications that monitor frequency behavior of item-sets. We propose a three stage strategy for focusing on item-sets, which are of current interest to the user and introduce metrics that model changes in their frequency (support) behavior. © Springer-Verlag Berlin Heidelberg 2005.