Algorithms for clustering data
Algorithms for clustering data
A simulated annealing algorithm for the clustering problem
Pattern Recognition
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
A Branch and Bound Clustering Algorithm
IEEE Transactions on Computers
K-Means-Type Algorithms: A Generalized Convergence Theorem and Characterization of Local Optimality
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM Computing Surveys (CSUR)
A Global Optimization RLT-based Approach for Solving the Hard Clustering Problem
Journal of Global Optimization
Stochastic Voting Algorithms for Web Services Group Testing
QSIC '05 Proceedings of the Fifth International Conference on Quality Software
Computational Statistics & Data Analysis
A tabu search approach for the minimum sum-of-squares clustering problem
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
Evaluating a branch-and-bound RLT-based algorithm for minimum sum-of-squares clustering
Journal of Global Optimization
A reactive GRASP with path relinking for capacitated clustering
Journal of Heuristics
Clustering large scale of XML documents
GPC'06 Proceedings of the First international conference on Advances in Grid and Pervasive Computing
XML document clustering by independent component analysis
KDXD'06 Proceedings of the First international conference on Knowledge Discovery from XML Documents
Iterative random projections for high-dimensional data clustering
Pattern Recognition Letters
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In this paper, we consider the problem of clustering m objects in c clusters. The objects are represented by points in n-dimensional Euclidean space, and the objective is to classify these m points into c clusters such that the distance between points within a cluster and its center is minimized. The problem is a difficult optimization problem due to the fact that it posseses many local minima. Several algorithms have been developed to solve this problem which include the k-means algorithm, the simulated annealing algorithm, the tabu search algorithm, and the genetic algorithm. In this paper, we study the four algorithms and compare their computational performance for the clustering problem. We test these algorithms on several clustering problems from the literature as well as several random problems and we report on our computational experience.