We study the problems of clustering with outliers in high dimension . Though a number of methods have been developed in the past decades, it is still quite challenging to design quality guaranteed algorithms with low complexities for the problems . Our idea is inspired by the greedy method, Gonzalez's algorithm, for solving the problem of ordinary k-center clustering. Based on some novel observations, we show that this greedy strategy actually can handle k-center /median/means clustering with outliers efficiently, in terms of qualities and complexities . We further show that the greedy approach yields small coreset for the problem in doubling metrics, so as to reduce the time complexity significantly. Moreover, a by-product is that the coreset construction can be applied to speedup the popular density-based clustering approach DBSCAN .
We study the problem of k-center clustering with outliers in arbitrary metrics and Euclidean space . Though a number of methods have been developed in the past decades, it is still quite challenging to design quality guaranteed algorithm with low complexity for this problem . Our idea is inspired by the greedy method, Gonzalez's algorithm, for solving the problem of ordinary k-center clustering. Based on some novel observations, we show that this greedy strategy actually can handle k-center clustering with outliers efficiently, in terms of clustering quality and time complexity . We further show that the greedy approach yields small coreset for the problem in doubling metrics, so as to reduce the time complexity significantly. Our algorithms are easy to implement in practice. We test our method on both synthetic and real datasets. The experimental results suggest that our algorithms can achieve near optimal solutions and yield lower running times comparing with existing methods .
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problems of
problem of k-center
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high dimension
arbitrary metrics and Euclidean space
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algorithms with low complexities for the problems
algorithm with low complexity for this problem
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/median/means
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qualities and complexities
clustering quality and time complexity
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Moreover, a by-product is that the coreset construction can be applied to speedup the popular density-based clustering approach DBSCAN
Our algorithms are easy to implement in practice. We test our method on both synthetic and real datasets. The experimental results suggest that our algorithms can achieve near optimal solutions and yield lower running times comparing with existing methods