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Identifying Single Clusters in Large Data Sets
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Author(s): Frank Klawonn (University of Applied Sciences Braunschweig/Wolfenbuettel, Germany)and Olga Georgieva (Institute of Control and System Research, Bulgaria)
Copyright: 2005
Pages: 4
Source title:
Encyclopedia of Data Warehousing and Mining
Source Author(s)/Editor(s): John Wang (Montclair State University, USA)
DOI: 10.4018/978-1-59140-557-3.ch110
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Abstract
Most clustering methods have to face the problem of characterizing good clusters among noise data. The arbitrary noise points that just do not belong to any class being searched for are of a real concern. The outliers or noise data points are data that severely deviate from the pattern set by the majority of the data, and rounding and grouping errors result from the inherent inaccuracy in the collection and recording of data. In fact, a single outlier can completely spoil the least squares (LS) estimate and thus the results of most LS based clustering techniques such as the hard C-means (HCM) and the fuzzy C-means algorithm (FCM) (Bezdek, 1999).
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