A simulated annealing algorithm for the clustering problem
Pattern Recognition
In search of optimal clusters using genetic algorithms
Pattern Recognition Letters
ACM Computing Surveys (CSUR)
Dynamic agglomerative clustering of gene expression profiles
Pattern Recognition Letters
Image segmentation by clustering of spatial patterns
Pattern Recognition Letters
A recommender system using GA K-means clustering in an online shopping market
Expert Systems with Applications: An International Journal
Pattern Recognition Letters
A hybridized approach to data clustering
Expert Systems with Applications: An International Journal
An efficient hybrid approach based on PSO, ACO and k-means for cluster analysis
Applied Soft Computing
Dynamic hierarchical algorithms for document clustering
Pattern Recognition Letters
A document clustering algorithm for discovering and describing topics
Pattern Recognition Letters
Data clustering: 50 years beyond K-means
Pattern Recognition Letters
Evolutionary clustering based vector quantization and SPIHT coding for image compression
Pattern Recognition Letters
A novel clustering approach: Artificial Bee Colony (ABC) algorithm
Applied Soft Computing
Clustering ellipses for anomaly detection
Pattern Recognition
Application of gravitational search algorithm on data clustering
RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
K-Means-Type Algorithms: A Generalized Convergence Theorem and Characterization of Local Optimality
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Black hole: A new heuristic optimization approach for data clustering
Information Sciences: an International Journal
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Data clustering is an important technique in data mining. It is a method of partitioning data into clusters, in which each cluster must have data of great similarity and different clusters must have data of high dissimilarity. A lot of clustering algorithms are found in the literature. In general, there is no single algorithm that is suitable for all types of data, conditions and applications. Each algorithm has its own advantages, limitations and shortcomings. Therefore, introducing novel and effective approaches for data clustering is an open and active research area. This paper presents a novel binary search algorithm for data clustering that not only finds high quality clusters but also converges to the same solution in different runs. In the proposed algorithm a set of initial centroids are chosen from different parts of the test dataset and then optimal locations for the centroids are found by thoroughly exploring around of the initial centroids. The simulation results using six benchmark datasets from the UCI Machine Learning Repository indicate that proposed algorithm can efficiently be used for data clustering.