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KDD
2007
ACM

Constraint-driven clustering

9 years 3 months ago
Constraint-driven clustering
Clustering methods can be either data-driven or need-driven. Data-driven methods intend to discover the true structure of the underlying data while need-driven methods aims at organizing the true structure to meet certain application requirements. Thus, need-driven (e.g. constrained) clustering is able to find more useful and actionable clusters in applications such as energy aware sensor networks, privacy preservation, and market segmentation. However, the existing methods of constrained clustering require users to provide the number of clusters, which is often unknown in advance, but has a crucial impact on the clustering result. In this paper, we argue that a more natural way to generate actionable clusters is to let the application-specific constraints decide the number of clusters. For this purpose, we introduce a novel cluster model, Constraint-Driven Clustering (CDC), which finds an a priori unspecified number of compact clusters that satisfy all user-provided constraints. Two ...
Rong Ge, Martin Ester, Wen Jin, Ian Davidson
Added 30 Nov 2009
Updated 30 Nov 2009
Type Conference
Year 2007
Where KDD
Authors Rong Ge, Martin Ester, Wen Jin, Ian Davidson
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