A Classification EM algorithm for clustering and two stochastic versions
Computational Statistics & Data Analysis - Special issue on optimization techniques in statistics
A near-optimal initial seed value selection in K-means algorithm using a genetic algorithm
Pattern Recognition Letters
Feature Selection: Evaluation, Application, and Small Sample Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM Computing Surveys (CSUR)
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Artficial Immune Systems and Their Applications
Artficial Immune Systems and Their Applications
Artificial Immune Systems: A New Computational Intelligence Paradigm
Artificial Immune Systems: A New Computational Intelligence Paradigm
A GRASP Algorithm for Clustering
IBERAMIA 2002 Proceedings of the 8th Ibero-American Conference on AI: Advances in Artificial Intelligence
Ant Colony Optimization
Expanding Neighborhood GRASP for the Traveling Salesman Problem
Computational Optimization and Applications
A hybridized approach to data clustering
Expert Systems with Applications: An International Journal
A stochastic nature inspired metaheuristic for clustering analysis
International Journal of Business Intelligence and Data Mining
Optimization of nearest neighbor classifiers via metaheuristic algorithms for credit risk assessment
Journal of Global Optimization
A scalable artificial immune system model for dynamic unsupervised learning
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
A novel ant clustering algorithm with digraph
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part II
A combinational clustering method based on artificial immune system and support vector machine
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part I
An ACO-based clustering algorithm
ANTS'06 Proceedings of the 5th international conference on Ant Colony Optimization and Swarm Intelligence
A novel ant-based clustering approach for document clustering
AIRS'06 Proceedings of the Third Asia conference on Information Retrieval Technology
A mountain clustering based on improved PSO algorithm
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part III
A hybrid tabu search based clustering algorithm
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part II
HM'05 Proceedings of the Second international conference on Hybrid Metaheuristics
Survey of clustering algorithms
IEEE Transactions on Neural Networks
A hybrid particle Swarm optimization algorithm for clustering analysis
DaWaK'07 Proceedings of the 9th international conference on Data Warehousing and Knowledge Discovery
A Hybrid Bumble Bees Mating Optimization - GRASP Algorithm for Clustering
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
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This paper introduces a new hybrid algorithmic nature inspired approach based on the concepts of the Honey Bees Mating Optimization Algorithm (HBMO) and of the Greedy Randomized Adaptive Search Procedure (GRASP), for optimally clustering N objects into K clusters. The proposed algorithm for the Clustering Analysis, the Hybrid HBMO-GRASP, is a two phase algorithm which combines a HBMO algorithm for the solution of the feature selection problem and a GRASP for the solution of the clustering problem. This paper shows that the Honey Bees Mating Optimization can be used in hybrid synthesis with other metaheuristics for the solution of the clustering problem with remarkable results both to quality and computational efficiency. Its performance is compared with other popular stochastic/metaheuristic methods like particle swarm optimization, ant colony optimization, genetic algorithms and tabu search based on the results taken from the application of the methodology to data taken from the UCI Machine Learning Repository.