期刊名称：International Journal of Computer Science and Network Security
印刷版ISSN：1738-7906
出版年度：2011
卷号：11
期号：6
页码：232-236
出版社：International Journal of Computer Science and Network Security
摘要：Categorical data clustering is an interesting challenge for researchers in the data mining and machine learning, because of many practical aspects associated with efficient processing and concepts are often not stable but change with time. Typical examples of this are weather prediction rules and customers preferences, intrusion detection in a network traffic stream. Another example is the case of text data points, such as that occurring in Twitter/search engines. In this paper we propose a generalized framework that detects drifting concepts and try to show the evolving clustering results in the categorical domain. This scheme is based on the cosine measure that analyzes relationship between clustering results at different time stamps using POur-NIR method.
