Using Global Optimization to Explore Multiple Solutions of Clustering Problems

Authors:
Ida Bifulco;Loredana Murino;Francesco Napolitano;Giancarlo Raiconi;Roberto Tagliaferri
Affiliations:
NeuRoNe Lab, DMI, University of Salerno, Fisciano, Italy 84084;NeuRoNe Lab, DMI, University of Salerno, Fisciano, Italy 84084;NeuRoNe Lab, DMI, University of Salerno, Fisciano, Italy 84084;NeuRoNe Lab, DMI, University of Salerno, Fisciano, Italy 84084;NeuRoNe Lab, DMI, University of Salerno, Fisciano, Italy 84084
Venue:
KES '08 Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part III
Year:
2008

Citing 7
Cited 3

A New Version of the Price‘s Algorithm for Global Optimization

Journal of Global Optimization
Genetic approach helps to speed classical Price algorithm for global optimization

Soft Computing - A Fusion of Foundations, Methodologies and Applications
Clustering Ensembles: Models of Consensus and Weak Partitions

IEEE Transactions on Pattern Analysis and Machine Intelligence
Evaluation of Stability of k-Means Cluster Ensembles with Respect to Random Initialization

IEEE Transactions on Pattern Analysis and Machine Intelligence
Meta Clustering

ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Clustering aggregation

ACM Transactions on Knowledge Discovery from Data (TKDD)
PCA Based Feature Selection Applied to the Analysis of the International Variation in Diet

WILF '07 Proceedings of the 7th international workshop on Fuzzy Logic and Applications: Applications of Fuzzy Sets Theory

Robust Clustering by Aggregation and Intersection Methods

KES '08 Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part III
Metaclustering and Consensus Algorithms for Interactive Data Analysis and Validation

WILF '09 Proceedings of the 8th International Workshop on Fuzzy Logic and Applications
Multiple clustering solutions analysis through least-squares consensus algorithms

CIBB'09 Proceedings of the 6th international conference on Computational intelligence methods for bioinformatics and biostatistics

Quantified Score

Hi-index	0.01

Visualization

Abstract

A classical approach to clustering consists in running an algorithm aimed to minimize the distortion. Apart from very limited and simple cases such problem cannot be solved by a local search algorithm because of multiple local minima. In this paper a Global Optimization (GO) algorithm is used to overcome such difficulty. The proposed algorithm (Controlled Random Search) iterates by maintaining a population of solutions which tends to concentrate around the most "promising" areas. From Data Mining point of view such an approach enables to infer deep information about the underlying structure of data. Collecting and presenting such information in a human understandable manner can help the choice between several possible alternatives. Numerical experiments are carried out on a real dataset, showing that GO produces solutions with much better distortion values than the classical approach, while graphical representation of the whole solution set can be useful to data exploration.