Dynamic rough clustering and its applications

Journal article


Peters, Georg, Weber, Richard and Nowatzke, René. (2012). Dynamic rough clustering and its applications. Applied Soft Computing Journal. 12(10), pp. 3193 - 3207. https://doi.org/10.1016/j.asoc.2012.05.015
AuthorsPeters, Georg, Weber, Richard and Nowatzke, René
Abstract

Dynamic data mining has gained increasing attention in the last decade. It addresses changing data structures which can be observed in many real-life applications, e.g. buying behavior of customers. As opposed to classical, i.e. static data mining where the challenge is to discover pattern inherent in given data sets, in dynamic data mining the challenge is to understand – and in some cases even predict – how such pattern will change over time. Since changes in general lead to uncertainty, the appropriate approaches for uncertainty modeling are needed in order to capture, model, and predict the respective phenomena considered in dynamic environments. As a consequence, the combination of dynamic data mining and soft computing is a very promising research area. The proposed algorithm consists of a dynamic clustering cycle when the data set will be refreshed from time to time. Within this cycle criteria check if the newly arrived data have structurally changed in comparison to the data already analyzed. If yes, appropriate actions are triggered, in particular an update of the initial settings of the cluster algorithm. As we will show, rough clustering offers strong tools to detect such changing data structures. To evaluate the proposed dynamic rough clustering algorithm it has been applied to synthetic as well as to real-world data sets where it provides new insights regarding the underlying dynamic phenomena.

Keywordsdynamic data mining; changing data structures; rough k-means clustering
Year2012
JournalApplied Soft Computing Journal
Journal citation12 (10), pp. 3193 - 3207
PublisherElsevier B.V.
ISSN1568-4946
Digital Object Identifier (DOI)https://doi.org/10.1016/j.asoc.2012.05.015
Scopus EID2-s2.0-84864759981
Page range3193 - 3207
Research GroupSchool of Arts
Publisher's version
File Access Level
Controlled
Place of publicationNetherlands
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