A simulated annealing algorithm for the clustering problem
Pattern Recognition
ACM Computing Surveys (CSUR)
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
An evolutionary technique based on K-means algorithm for optimal clustering in RN
Information Sciences—Applications: An International Journal
A Hybrid Optimization Method for Fuzzy Classification Systems
HIS '08 Proceedings of the 2008 8th International Conference on Hybrid Intelligent Systems
Music-Inspired Harmony Search Algorithm: Theory and Applications
Music-Inspired Harmony Search Algorithm: Theory and Applications
Differential evolution and particle swarm optimisation in partitional clustering
Computational Statistics & Data Analysis
Selection mechanisms in memory consideration for examination timetabling with harmony search
Proceedings of the 12th annual conference on Genetic and evolutionary computation
The variants of the harmony search algorithm: an overview
Artificial Intelligence Review
Clustering with a genetically optimized approach
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Optimization of clustering criteria by reformulation
IEEE Transactions on Fuzzy Systems
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Being one of the main challenges to clustering algorithms, the sensitivity of fuzzy c-means (FCM) and hard c-means (HCM) to tune the initial clusters centers has captured the attention of the clustering communities for quite a long time. In this study, the new evolutionary algorithm, Harmony Search (HS), is proposed as a new method aimed at addressing this problem. The proposed approach consists of two stages. In the first stage, the HS explores the search space of the given dataset to find out the near-optimal cluster centers. The cluster centers found by the HS are then evaluated using reformulated c-means objective function. In the second stage, the best cluster centers found are used as the initial cluster centers for the c-means algorithms. Our experiments show that an HS can minimize the difficulty of choosing an initialization for the c-means clustering algorithms. For purposes of evaluation, standard benchmark data are experimented with, including the Iris, BUPA liver disorders, Glass, Diabetes, etc. along with two generated data that have several local extrema.