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ADMA
2009
Springer

A Framework for Multi-Objective Clustering and Its Application to Co-Location Mining

10 years 6 months ago
A Framework for Multi-Objective Clustering and Its Application to Co-Location Mining
The goal of multi-objective clustering (MOC) is to decompose a dataset into similar groups maximizing multiple objectives in parallel. In this paper, we provide a methodology, architecture and algorithms that, based on a large set of objectives, derive interesting clusters regarding two or more of those objectives. The proposed architecture relies on clustering algorithms that support plug-in fitness functions and on multi-run clustering in which clustering algorithms are run multiple times maximizing different subsets of objectives that are captured in compound fitness functions. MOC provides search engine type capabilities to users, enabling them to query a large set of clusters with respect to different objectives and thresholds. We evaluate the proposed MOC framework in a case study that centers on spatial co-location mining; the goal is to identify regions in which high levels of Arsenic concentrations are co-located with high concentrations of other chemicals in the Texas water s...
Rachsuda Jiamthapthaksin, Christoph F. Eick, Ricar
Added 25 May 2010
Updated 25 May 2010
Type Conference
Year 2009
Where ADMA
Authors Rachsuda Jiamthapthaksin, Christoph F. Eick, Ricardo Vilalta
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