An Evolutionary Approach to Multiobjective Clustering

Handl, Julia; Knowles, Joshua

doi:10.1109/tevc.2006.877146

Cited by 641 publications

(475 citation statements)

References 40 publications

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“…Such clustering quality measures have also been used in many other MOEA, e.g. MOCK [13] and MOCLE [14]. Finally, work by Molina et al [15] employ scatter tabu search for non-linear multi-objective optimization which can potentially be utilized for multi-objective clustering.…”

Section: Related Workmentioning

confidence: 99%

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

Jiamthapthaksin

Eick

Vilalta

2009

Advanced Data Mining and Applications

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Abstract. 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 supply.

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Section: Related Workmentioning

confidence: 99%

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

Jiamthapthaksin

Eick

Vilalta

2009

Advanced Data Mining and Applications

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“…The reduction of a well-known difficulty in the genetic programming studies, called the 'bloating', by minimizing the size of programs as an additional objective helped find high-performing solutions with a smaller size of the code [2]. Minimizing the intra-cluster distance and maximizing inter-cluster distance simultaneously in a bi-objective formulation of a clustering problem is found to yield better solutions than the usual single-objective minimization of the ratio of the intra-cluster distance to the inter-cluster distance [25]. A recent edited book [27] describes many such interesting applications in which EMO methodologies have help shown problems which are otherwise (or traditionally) not treated as multi-objective optimization problems.…”

Section: Multi-objectivizationmentioning

confidence: 99%

Current trends in evolutionary multi-objective optimization

Deb

2007

Int. J. Simul. Multidisci. Des. Optim.

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-In a short span of about 14 years, evolutionary multi-objective optimization (EMO) has established itself as a mature field of research and application with an extensive literature, many commercial softwares, numerous freely downloadable codes, a dedicated biannual conference running successfully four times so far since 2001, special sessions and workshops held at all major evolutionary computing conferences, and full-time researchers from universities and industries from all around the globe. In this paper, we make a brief outline of EMO principles, some EMO algorithms, and focus on current research and application potential of EMO. Besides, simply finding a set of Pareto-optimal solutions, EMO research has now diversified in hybridizing its search with multi-criterion decision-making tools to arrive at a single preferred solution, in utilizing EMO principle in solving different kinds of single-objective optimization problems efficiently, and in various interesting application domains which were not possible to be solved adequately due to the lack of a suitable solution technique.

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“…Morita et al [23] make use of a multi-objective genetic algorithm where the minimization of the number of features and a validity index that measures the quality of clusters have been used to guide the search towards the more discriminant features and the best number of clusters. Recently Handl et al in [24] presented MOCK, a multi-objective clustering algorithm with automatic determination of the number of clusters (K). In this work authors discussed the conceptual advantages of multi-objective clustering and demonstrated that these translate into a performance advantage in practice: the proposed evolutionary approach has been shown to outperform traditional single-objective clustering techniques and an ensemble method across a diverse range of benchmark data sets.…”

Section: Previous Workmentioning

confidence: 99%