Algorithms for clustering data
Algorithms for clustering data
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
Partitioning-based clustering for Web document categorization
Decision Support Systems - Special issue on WITS '97
Clustering Categorical Data: An Approach Based on Dynamical Systems
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
WaveCluster: A Multi-Resolution Clustering Approach for Very Large Spatial Databases
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Efficient and Effective Clustering Methods for Spatial Data Mining
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
STING: A Statistical Information Grid Approach to Spatial Data Mining
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
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Clustering in very large databases or data warehouses, with many applications in areas such as spatial computation, web information collection, pattern recognition and economic analysis, is a huge task that challenges data mining researches. Current clustering methods always have the problems: 1) scanning the whole database leads to high I/O cost and expensive maintenance (e.g., R*-tree); 2) pre-specifying the uncertain parameter k, with which clustering can only be refined by trial and test many times; 3) lacking high efficiency in treating arbitrary shape under very large data set environment. In this paper, we first present a new hybrid-clustering algorithm to solve these problems. This new algorithm, which combines both distance and density strategies, can handle any arbitrary shape clusters effectively. It makes full use of statistics information in mining to reduce the time complexity greatly while keeping good clustering quality. Furthermore, this algorithm can easily eliminate noises and identify outliers. An experimental evaluation is performed on a spatial database with this method and other popular clustering algorithms (CURE and DBSCAN). The results show that our algorithm outperforms them in terms of efficiency and cost, and even gets much more speedup as the data size scales up much larger.