BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications
Data Mining and Knowledge Discovery
CLARANS: A Method for Clustering Objects for Spatial Data Mining
IEEE Transactions on Knowledge and Data Engineering
STING: A Statistical Information Grid Approach to Spatial Data Mining
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Iterative shrinking method for clustering problems
Pattern Recognition
A hybridized approach to data clustering
Expert Systems with Applications: An International Journal
Knowledge-based image retrieval system
Knowledge-Based Systems
A simple and fast algorithm for K-medoids clustering
Expert Systems with Applications: An International Journal
External validation measures for K-means clustering: A data distribution perspective
Expert Systems with Applications: An International Journal
An efficient hybrid data clustering method based on K-harmonic means and Particle Swarm Optimization
Expert Systems with Applications: An International Journal
Performance evaluation of density-based clustering methods
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
An artificial bee colony approach for clustering
Expert Systems with Applications: An International Journal
Particle Swarm Optimization and Intelligence: Advances and Applications
Particle Swarm Optimization and Intelligence: Advances and Applications
Computers in Biology and Medicine
Ant clustering algorithm with K-harmonic means clustering
Expert Systems with Applications: An International Journal
Integration of genetic fuzzy systems and artificial neural networks for stock price forecasting
Knowledge-Based Systems
Supporting image retrieval framework with rule base system
Knowledge-Based Systems
Expert Systems with Applications: An International Journal
A new and efficient k-medoid algorithm for spatial clustering
ICCSA'05 Proceedings of the 2005 international conference on Computational Science and Its Applications - Volume Part III
Clustering of the self-organizing map
IEEE Transactions on Neural Networks
Survey of clustering algorithms
IEEE Transactions on Neural Networks
Spatial interaction - modification model and applications to geo-demographic analysis
Knowledge-Based Systems
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Clustering analysis is the process of dividing a set of objects into none-overlapping subsets. Each subset is a cluster, such that objects in the cluster are similar to one another and dissimilar to the objects in the other clusters. Most of the algorithms in partitioning approach of clustering suffer from trapping in local optimum and the sensitivity to initialization and outliers. In this paper, we introduce a novel partitioning algorithm that its initialization does not lead the algorithm to local optimum and can find all the Gaussian-shaped clusters if it has the right number of them. In this algorithm, the similarity between pairs of objects are computed once and updating the medoids in each iteration costs O(kxm) where k is the number of clusters and m is the number of objects needed to update medoids of the clusters. Comparison between our algorithm and two other partitioning algorithms is performed by using four well-known external validation measures over seven standard datasets. The results for the larger datasets show the superiority of the proposed algorithm over two other algorithms in terms of speed and accuracy.