Abstract:
A popular algorithm for finding clusters in unlabeled data optimizes the k-means clustering model. This algorithm converges quickly but is sensitive to initialization. Tw...View moreMetadata
Abstract:
A popular algorithm for finding clusters in unlabeled data optimizes the k-means clustering model. This algorithm converges quickly but is sensitive to initialization. Two ways to overcome this drawback are fuzzification and harmonic means. We show that k-harmonic means is a special case of reformulated fuzzy k-means. The main focus of this paper is on partially supervised clustering. Partially supervised clustering finds clusters in data sets that contain both unlabeled and labeled data. We review partially supervised k-means, partially supervised fuzzy k-means, and introduce a partially supervised extension of k-harmonic means. Experiments with four benchmark data sets indicate that partially supervised k-harmonic means inherits the advantages of its completely unsupervised variant: It is significantly less sensitive to initialization than partially supervised k-means.
Date of Conference: 11-15 April 2011
Date Added to IEEE Xplore: 11 July 2011
ISBN Information: