NDLI: MONIC: modeling and monitoring cluster transitions

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MONIC: modeling and monitoring cluster transitions

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MONIC: modeling and monitoring cluster transitions

Content Provider	ACM Digital Library
Author	Spiliopoulou, Myra Schult, Rene Theodoridis, Yannis Ntoutsi, Irene
Abstract	There is much recent work on detecting and tracking change in clusters, often based on the study of the spatiotemporal properties of a cluster. For the many applications where cluster change is relevant, among them customer relationship management, fraud detection and marketing, it is also necessary to provide insights about the nature of cluster change: Is a cluster corresponding to a group of customers simply disappearing or are its members migrating to other clusters? Is a new emerging cluster reflecting a new target group of customers or does it rather consist of existing customers whose preferences shift? To answer such questions, we propose the framework MONIC for modeling and tracking of cluster transitions. Our cluster transition model encompasses changes that involve more than one cluster, thus allowing for insights on cluster change in the whole clustering. Our transition tracking mechanism is not based on the topological properties of clusters, which are only available for some types of clustering, but on the contents of the underlying data stream. We present our first results on monitoring cluster transitions over the ACM digital library.
Starting Page	706
Ending Page	711
Page Count	6
File Format	PDF
ISBN	1595933395
DOI	10.1145/1150402.1150491
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2006-08-20
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Cluster transitions Temporal analysis Clusters Data streams Cluster change detection
Content Type	Text
Resource Type	Article

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