OPTICS: ordering points to identify the clustering structure
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Influence sets based on reverse nearest neighbor queries
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
High dimensional reverse nearest neighbor queries
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Efficient reverse k-nearest neighbor search in arbitrary metric spaces
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
A Simple Yet Effective Data Clustering Algorithm
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Clustering Using a Similarity Measure Based on Shared Near Neighbors
IEEE Transactions on Computers
Reverse kNN search in arbitrary dimensionality
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Reverse k-nearest neighbor search in dynamic and general metric databases
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
A clustering algorithm based absorbing nearest neighbors
WAIM'05 Proceedings of the 6th international conference on Advances in Web-Age Information Management
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Clustering algorithms for data with varying densities have been investigated in the past and there are some data situations and clustering needs that are not handled well by these algorithms. We present here an algorithm for such situations in which multiple, possibly overlapping, clusters exist and need to be identified by their density as the defining characteristic. In this paper, we define the idea of mutual K-nearest neighbors (MKNN) relationship based on inter-point affinities and use it as a basis for discovering the above-mentioned types of clusters. With a synthetic and two real-world datasets we show that our algorithm delivers the type of clustering results and robustness that we seek to achieve, and the performance is better than what is achievable by other algorithms. Statistical Analysis and Data Mining 2012 © 2012 Wiley Periodicals, Inc.