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SIGMOD
2002
ACM

Clustering by pattern similarity in large data sets

10 years 8 months ago
Clustering by pattern similarity in large data sets
Clustering is the process of grouping a set of objects into classes of similar objects. Although definitions of similarity vary from one clustering model to another, in most of these models the concept of similarity is based on distances, e.g., Euclidean distance or cosine distance. In other words, similar objects are required to have close values on at least a set of dimensions. In this paper, we explore a more general type of similarity. Under the pCluster model we proposed, two objects are similar if they exhibit a coherent pattern on a subset of dimensions. For instance, in DNA microarray analysis, the expression levels of two genes may rise and fall synchronously in response to a set of environmental stimuli. Although the magnitude of their expression levels may not be close, the patterns they exhibit can be very much alike. Discovery of such clusters of genes is essential in revealing significant connections in gene regulatory networks. E-commerce applications, such as collabora...
Haixun Wang, Wei Wang 0010, Jiong Yang, Philip S.
Added 08 Dec 2009
Updated 08 Dec 2009
Type Conference
Year 2002
Where SIGMOD
Authors Haixun Wang, Wei Wang 0010, Jiong Yang, Philip S. Yu
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