Algorithms for clustering data
Algorithms for clustering data
The SEQUOIA 2000 storage benchmark
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Parallel algorithms for hierarchical clustering
Parallel Computing
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
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
ROCK: A Robust Clustering Algorithm for Categorical Attributes
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
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Clustering is an important data exploration task. A prominent clustering algorithm is agglomerative hierarchical clustering. Roughly, in each iteration, it merges the closest pair of clusters. It was first proposed way back in 1951, and since then there have been numerous modifications. Some of its good features are: a natural, simple, and non-parametric grouping of similar objects which is capable of finding clusters of different shape such as spherical and arbitrary. But large CPU time and high memory requirement limit its use for large data. In this paper we show that geometric metric (centroid, median, and minimum variance) algorithms obey a 90-10 relationship where roughly the first 90iterations are spent on merging clusters with distance less than 10the maximum merging distance. This characteristic is exploited by partially overlapping partitioning. It is shown with experiments and analyses that different types of existing algorithms benefit excellently by drastically reducing CPU time and memory. Other contributions of this paper include comparison study of multi-dimensional vis-a-vis single-dimensional partitioning, and analytical and experimental discussions on setting of parameters such as number of partitions and dimensions for partitioning.