Learning feature weights for K-Means clustering using the Minkowski metric
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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...
متن کاملMinkowski metric, feature weighting and anomalous cluster initializing in K-Means clustering
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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Distance metric has a key role in many machine learning and computer vision algorithms so that choosing an appropriate distance metric has a direct effect on the performance of such algorithms. Recently, distance metric learning using labeled data or other available supervisory information has become a very active research area in machine learning applications. Studies in this area have shown t...
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Clustering is the unsupervised learning problem. Better Clustering improves accuracy of search results and helps to reduce the retrieval time. Clustering dispersion known as entropy which is the disorderness that occur after retrieving search result. It can be reduced by combining clustering algorithm with the classifier. Clustering with weighted k-mean results in unlabelled data. This paper pr...
متن کاملApplying subclustering and Lp distance in Weighted K-Means with distributed centroids
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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تاریخ انتشار 2011