An entropy weighting mixture model for subspace clustering of high-dimensional data

Peng, Liuqing; Zhang, Junying

doi:10.1016/j.patrec.2011.03.003

Cited by 18 publications

(9 citation statements)

References 20 publications

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“…Two-way clustering approaches under merit functions to produce the clusters on both dimensions of the data matrix and to derive biclusters from their combinations [49,120,47]; Stochastic approaches that model data with a multivariate distribution [105,112,17,113] and learn a parametric model that maximizes a merit function. This model is used to derive biclusters.…”

Section: Paradigm Optimality Guaranteesmentioning

confidence: 99%

A structured view on pattern mining-based biclustering

Henriques

Antunes

Madeira

2015

Pattern Recognition

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Section: Paradigm Optimality Guaranteesmentioning

confidence: 99%

A structured view on pattern mining-based biclustering

Henriques

Antunes

Madeira

2015

Pattern Recognition

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“…In 2011, Peng el al. proposed a new Gaussian mixture model (GMM) type algorithm for discovering clusters with various shape volumes in subspaces (Peng and Zhang 2011). They extend the GMM clustering method to calculate a local weight vector as well as a local variance within each cluster, and use the weight and variance values to capture the main properties that discriminate different clusters, including subsets of relevant dimensions and shape volumes.…”

Section: Granular Computing For Subspace Clusteringmentioning

confidence: 99%

Survey on granularity clustering

Ding

Zhu

2015

Cogn Neurodyn

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With the rapid development of uncertain artificial intelligent and the arrival of big data era, conventional clustering analysis and granular computing fail to satisfy the requirements of intelligent information processing in this new case. There is the essential relationship between granular computing and clustering analysis, so some researchers try to combine granular computing with clustering analysis. In the idea of granularity, the researchers expand the researches in clustering analysis and look for the best clustering results with the help of the basic theories and methods of granular computing. Granularity clustering method which is proposed and studied has attracted more and more attention. This paper firstly summarizes the background of granularity clustering and the intrinsic connection between granular computing and clustering analysis, and then mainly reviews the research status and various methods of granularity clustering. Finally, we analyze existing problem and propose further research.

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“…In contrast, multiple subspaces are needed because each cluster may exist in a different subspace. To overcome the problem of relevance, a commonly recent approach consists of finding clusters in subspaces as described in [27,34,37].…”

Section: Clustering and High Dimensionmentioning

confidence: 99%

Density estimation of high dimensional data using ICA and Bayesian networks

Rouigueb

Chitroub

Bouridane

2014

IDA

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This paper proposes a semi-non parametric density estimation framework for high-dimensional data. Dimensionality reduction is achieved by reorganizing the domain variables set into a junction tree of cliques each containing a small number of variables where factorization of the joint density into a tree is carried out by learning the Bayesian Network (BN) structure graph and by searching the maximum spanning tree over the moralized-triangulated graph of the obtained BN. To estimate the density of the junction tree elements, we propose a novel technique using local Independent Component Analysis (ICA) method based on fuzzy clustering. The main contribution relates to the development of a generic framework through a combination of three complimentary modules: (1) BN structure learning, (2) fuzzy clustering, and (3) linear ICA method. This allows us to exploit the separation power of recently developed ICA tools. Hence, depending on the data characteristics, the user can choose among a wide range of ICA and BN tools the most suitable one. We experimentally evaluated our approach in a supervised classification problem and the obtained results indicate an improvement in accuracy.

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An entropy weighting mixture model for subspace clustering of high-dimensional data

Cited by 18 publications

References 20 publications

A structured view on pattern mining-based biclustering

A structured view on pattern mining-based biclustering

Survey on granularity clustering

Density estimation of high dimensional data using ICA and Bayesian networks

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