An Entropy Weighting k-Means Algorithm for Subspace Clustering of High-Dimensional Sparse Data

Jing, Liping; Ng, Michael K.; Huang, Joshua Zhexue

doi:10.1109/tkde.2007.1048

Cited by 553 publications

(322 citation statements)

References 29 publications

Supporting

Mentioning

312

Contrasting

Unclassified

Order By: Relevance

“…Thus, the total feature vector per chunk contains 16 * 2 * 12 = 384 features. Experimental results for FCM feature weighting-based VQ can be found in the literature [4], [5], [6], [7]. In this paper, results for FE feature weighting-based VQ are presented.…”

Section: B Speech Processingmentioning

confidence: 99%

“…. , M [5], [6], [7]. Weight values were estimated using either F CM -based estimation technique [4], [5] or F E-based estimation technique [6].…”

Section: Fuzzy Feature Weightingmentioning

confidence: 99%

“…Therefore a weighting estimation technique can be applied to any clustering techniques provided that these clustering techniques employ an objective function-based optimisation method. For example, an F CM feature weighting has been applied to F CM clustering as seen in [5] and to K-means clustering in [4] and [6].…”

Section: Fuzzy Feature Weightingmentioning

confidence: 99%

“…Moreover there are also two kinds of weights which are weight vector and weight matrix as mentioned above, so there are totally 2 * 3 * 2 = 12 combinations found. Four of these 12 combinations have been used in [4], [5] and [6]. The other eight combinations are proposed in this paper.…”

Section: Fuzzy Feature Weightingmentioning

confidence: 99%

See 3 more Smart Citations

Fuzzy feature weighting techniques for vector quantisation

Tran

Sharma

et al. 2010

International Conference on Fuzzy Systems

View full text Add to dashboard Cite

Abstract-Vector quantization (VQ) is a simple but effective modelling technique in pattern recognition. VQ employs a clustering technique to convert a feature vector set in to a cluster center set to model the feature vector set. Some clustering techniques have been applied to improve VQ. However VQ is not always effective because data features are treated equally although their importance may not be the same. Some automated feature weighting techniques have been proposed to overcome this drawback. This paper reviews those weighting techniques and proposes a general scheme for selecting any pair of clustering and feature weighting techniques to form a fuzzy feature weighting-based VQ modelling technique. Besides the current techniques, a number of new feature weightingbased VQ techniques is proposed and their evaluations are also presented.

show abstract

Section: B Speech Processingmentioning

confidence: 99%

“…. , M [5], [6], [7]. Weight values were estimated using either F CM -based estimation technique [4], [5] or F E-based estimation technique [6].…”

Section: Fuzzy Feature Weightingmentioning

confidence: 99%

Section: Fuzzy Feature Weightingmentioning

confidence: 99%

Section: Fuzzy Feature Weightingmentioning

confidence: 99%

See 2 more Smart Citations

Fuzzy feature weighting techniques for vector quantisation

Tran

Sharma

et al. 2010

International Conference on Fuzzy Systems

View full text Add to dashboard Cite

show abstract

“…Clustering algorithms [27][28][29][30][31][32][33][34][35][36][37][38][39] can be applied to text mining to allow the automatic recognition of some sort of structure in the analyzed set of documents. In particular, clustering is designed to discover groups in the set of documents such that the documents within a group are more similar to one another than to documents of other groups.…”

Section: Text Clusteringmentioning

confidence: 99%

Text Clustering for Digital Forensics Analysis

Decherchi

Tacconi²,

Redi

et al. 2009

Advances in Intelligent and Soft Computing

View full text Add to dashboard Cite

Abstract:In the last decades digital forensics has become a prominent activity in modern investigations. Seized digital devices can provide precious information and evidences about facts and/or individuals on which the investigational activity is performed. Due to the complexity of this inquiring activity and to the large amount of the data to be analyzed, the choice of appropriate digital tools to support the investigation represents a central concern. In this paper an effective digital text analysis strategy, relying on clusteringbased text mining techniques, is introduced for investigational purposes. The proposed methodology is experimentally applied to the publicly available Enron dataset that well fits a plausible forensics analysis context.

show abstract