期刊
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
卷 56, 期 2, 页码 83-92出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/S0169-7439(01)00111-3
关键词
pattern recognition; density-based clustering; outliers and inliers identification
A density-based unsupervised clustering approach for detecting natural patterns in data (further denoted as NP) is presented, and its performance is illustrated for data sets with different types of clusters. NP works for arbitrary clusters, is a single-scan technique, requires no presumptions regarding data distribution and requires only one input parameter, which describes the minimal number of objects, considered as cluster. Moreover, a comparison of NP with partitioning approaches is demonstrated. NP can be applied not only for data clustering, but also for the identification of outliers. (C) 2001 Elsevier Science B.V. All rights reserved.
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