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
Automatic subspace clustering of high dimensional data for data mining applications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
OPTICS: ordering points to identify the clustering structure
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values
Data Mining and Knowledge Discovery
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ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Parallel Fuzzy c-Means Clustering for Large Data Sets
Euro-Par '02 Proceedings of the 8th International Euro-Par Conference on Parallel Processing
STING: A Statistical Information Grid Approach to Spatial Data Mining
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
CDS-Tree: An Effective Index for Clustering Arbitrary Shapes in Data Streams
RIDE '05 Proceedings of the 15th International Workshop on Research Issues in Data Engineering: Stream Data Mining and Applications
PENS: an algorithm for density-based clustering in peer-to-peer systems
InfoScale '06 Proceedings of the 1st international conference on Scalable information systems
Density-based clustering for real-time stream data
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
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VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
A clustering algorithm based on an estimated distribution model
International Journal of Business Intelligence and Data Mining
Locally Scaled Density Based Clustering
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part I
A Spatial Overlapping Based Similarity Measure Applied to Hierarchical Clustering
FSKD '08 Proceedings of the 2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery - Volume 02
A new approach for distributed density based clustering on grid platform
BNCOD'07 Proceedings of the 24th British national conference on Databases
On cluster validity for the fuzzy c-means model
IEEE Transactions on Fuzzy Systems
Data Field for Hierarchical Clustering
International Journal of Data Warehousing and Mining
Weighted Fuzzy-Possibilistic C-Means Over Large Data Sets
International Journal of Data Warehousing and Mining
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Special clustering algorithms are attractive for the task of grouping an arbitrary shaped database into several proper classes. Until now, a wide variety of clustering algorithms for this task have been proposed, although the majority of these algorithms are density-based. In this paper, the authors extend the dissimilarity measure to compatible measure and propose a new algorithm ASCCN based on the results. ASCCN is an unambiguous partition method that groups objects to compatible nucleoids, and merges these nucleoids into different clusters. The application of cluster grids significantly reduces the computational cost of ASCCN, and experiments show that ASCCN can efficiently and effectively group arbitrary shaped data points into meaningful clusters.