An algorithm for finding nearest neighbours in (approximately) constant average time
Pattern Recognition Letters
Robust Clustering with Applications in Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Vector quantization and signal compression
Vector quantization and signal compression
Equal-average hyperplane partitioning method for vector quantization of image data
Pattern Recognition Letters
BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
CACTUS—clustering categorical data using summaries
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient and Effective Clustering Methods for Spatial Data Mining
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Accelerating k-medoid-based algorithms through metric access methods
Journal of Systems and Software
Improved search strategies and extensions to k-medoids-based clustering algorithms
International Journal of Business Intelligence and Data Mining
A hybrid spatial data clustering method for site selection: The data driven approach of GIS mining
Expert Systems with Applications: An International Journal
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Clustering in data mining is a discovery process that groups similar objects into the same cluster. Various clustering algorithms have been designed to fit various requirements and constraints of application. In this paper, we study several k-medoids-based algorithms including the PAM, CLARA and CLARANS algorithms. A novel and efficient approach is proposed to reduce the computational complexity of such k-medoids-based algorithms by using previous medoid index, triangular inequality elimination criteria and partial distance search. Experimental results based on elliptic, curve and Gauss-Markov databases demonstrate that the proposed algorithm applied to CLARANS may reduce the number of distance calculations by 67% to 92% while retaining the same average distance per object. In terms of the running time, the proposed algorithm may reduce computation time by 38% to 65% compared with the CLARANS algorithm.