Sublinear time approximate clustering
Sublinear time approximate clustering
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次线性时间近似聚类
DOI：
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发表时间：
2001
期刊：
影响因子：
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通讯作者：
L. Pitt
中科院分区：
文献类型：
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作者：
Nina Mishra;Daniel Oblinger;L. Pitt
聚类在机器学习、统计学和数据挖掘等许多学科中至关重要。本文有两个重点：（1）它描述了现有的聚类算法如何从统计工作中产生的简单采样技术中受益[Pol84]。 (2) 它激发并引入了一种新的聚类模型，该模型本着“PAC（可能近似正确）”学习模型的精神，并给出了高效 PAC 聚类算法的示例。
Clustering is of central importance in a number of disciplines including Machine Learning, Statistics, and Data Mining. This paper has two foci: (1) It describes how existing algorithms for clustering can benefit from simple sampling techniques arising from work in statistics [Pol84]. (2) It motivates and introduces a new model of clustering that is in the spirit of the “PAC (probably approximately correct)” learning model, and gives examples of efficient PAC-clustering algorithms.