Learning feature weights for K-Means clustering using the Minkowski metric
نویسنده
چکیده
منابع مشابه
Minkowski Metric , Feature Weighting and Anomalous Cluster Initializing in K - Means Clustering Renato
This paper represents another step in overcoming a drawback of K-Means, its lack of defense against noisy features, by using feature weights in the criterion. The Weighted K-Means method by Huang et al. is extended to the corresponding Minkowski metric for measuring distances. Under Minkowski metric the feature weights become intuitively appealing feature rescaling factors in a conventional K-M...
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This paper represents another step in overcoming a drawback of K-Means, its lack of defense against noisy features, using feature weights in the criterion. The Weighted K-Means method by Huang et al. (2008, 2004, 2005) [5–7] is extended to the corresponding Minkowski metric for measuring distances. Under Minkowski metric the feature weights become intuitively appealing feature rescaling factors...
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We consider the weighted K-Means algorithm with distributed centroids aimed at clustering data sets with numerical, categorical and mixed types of data. Our approach allows given features (i.e., variables) to have different weights at different clusters. Thus, it supports the intuitive idea that features may have different degrees of relevance at different clusters. We use the Minkowski metric ...
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