ACM Computing Surveys (CSUR)
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
ACM Computing Surveys (CSUR)
A neighborhood-based clustering by means of the triangle inequality
IDEAL'10 Proceedings of the 11th international conference on Intelligent data engineering and automated learning
A neighborhood-based clustering algorithm
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Survey of clustering algorithms
IEEE Transactions on Neural Networks
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Discovering clusters of arbitrary shape with variable densities is an interesting challenge in many fields of science and technology. There are few clustering methods, which can detect clusters of arbitrary shape and different densities. However, these methods are very sensitive with parameter settings and are not scalable with large datasets. In this paper, we propose a clustering method, which detects clusters of arbitrary shapes, sizes and different densities. We introduce a parameter termed Nearest Neighbor Factor (NNF) to determine relative position of an object in its neighborhood region. Based on relative position of a point, proposed method expands a cluster recursively or declares the point as outlier. Proposed method outperforms a classical method DBSCAN and recently proposed TI-k-Neighborhood-Index supported NBC method.