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JCST
2008

Clustering by Pattern Similarity

10 years 2 months ago
Clustering by Pattern Similarity
The task of clustering is to identify classes of similar objects among a set of objects. The definition of similarity varies from one clustering model to another. However, in most of these models the concept of similarity is often based on such metrics as Manhattan distance, Euclidean distance or other Lp distances. In other words, similar objects must have close values in 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. The new similarity concept models a wide range of applications. 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 significa...
Haixun Wang, Jian Pei
Added 13 Dec 2010
Updated 13 Dec 2010
Type Journal
Year 2008
Where JCST
Authors Haixun Wang, Jian Pei
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