Further improvements in Feature-Weighted Fuzzy C-Means

Xing, Hong-Jie; Ha, Ming-Hu

doi:10.1016/j.ins.2014.01.033

Cited by 37 publications

(23 citation statements)

References 18 publications

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“…in which the weight of each edge (x i , x j ) is ‖F ij ‖ calculated by equation (2). e smaller the value of k, the sparser the graph.…”

Section: Sparse Gravitationalmentioning

confidence: 99%

“…Clustering is one of the major unsupervised learning techniques and has been applied in many fields such as pattern recognition [1], image processing [2,3], community detection [4,5], bioinformatics [6,7], information retrieval [8,9], and so on. e main task of clustering is to classify a dataset into some nonoverlapping clusters based on a suitable similarity metric so that the elements in the same cluster are similar, while any elements from different clusters are dissimilar.…”

Section: Introductionmentioning

confidence: 99%

“…K-means [10] and its successors [2,11] are typical partition-based clustering approaches. ey need to be specified the number of clusters in advance.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Hierarchical Clustering Approach Based on Universal Gravitation

Zhang

She

2020

Mathematical Problems in Engineering

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The target of the clustering analysis is to group a set of data points into several clusters based on the similarity or distance. The similarity or distance is usually a scalar used in numerous traditional clustering algorithms. Nevertheless, a vector, such as data gravitational force, contains more information than a scalar and can be applied in clustering analysis to promote clustering performance. Therefore, this paper proposes a three-stage hierarchical clustering approach called GHC, which takes advantage of the vector characteristic of data gravitational force inspired by the law of universal gravitation. In the first stage, a sparse gravitational graph is constructed based on the top k data gravitations between each data point and its neighbors in the local region. Then the sparse graph is partitioned into many subgraphs by the gravitational influence coefficient. In the last stage, the satisfactory clustering result is obtained by merging these subgraphs iteratively by using a new linkage criterion. To demonstrate the performance of GHC algorithm, the experiments on synthetic and real-world data sets are conducted, and the results show that the GHC algorithm achieves better performance than the other existing clustering algorithms.

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“…in which the weight of each edge (x i , x j ) is ‖F ij ‖ calculated by equation (2). e smaller the value of k, the sparser the graph.…”

Section: Sparse Gravitationalmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Novel Hierarchical Clustering Approach Based on Universal Gravitation

Zhang

She

2020

Mathematical Problems in Engineering

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“…It's proposed to use "dissimilarity" between vectors (images) instead of the conventional Euclidean distance (which underlies the traditional k-means method) in [26][27][28][29][30][31] as well as to use mode values for some separate features instead of traditional mean values.…”

Section: Introductionmentioning

confidence: 99%

Possibilistic Fuzzy Clustering for Categorical Data Arrays Based on Frequency Prototypes and Dissimilarity Measures

Hu¹,

Bodyanskiy²,

Tyshchenko³

et al. 2017

IJISA

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Abstract-Fuzzy clustering procedures for categorical data are proposed in the paper. Most of well-known conventional clustering methods face certain difficulties while processing this sort of data because a notion of similarity is missing in these data. A detailed description of a possibilistic fuzzy clustering method based on frequency-based cluster prototypes and dissimilarity measures for categorical data is given.

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“…Thus, a weight is introduced in the objective function targeting data points and/or features. The weighting methods are on around prototypes [18] where information about the cluster size is incorporated, features where each feature has a specific weight [11] [12] [13] [14] , fuzzy membership degree [15] and distance [16].…”

Section: Introductionmentioning

confidence: 99%

Gravitational weighted fuzzy c-means with application on multispectral image segmentation

Said

Hadjidj

Foufou

2014

2014 4th International Conference on Image Processing Theory, Tools and Applications (IPTA)

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This paper presents a novel clustering approach based on the classic Fuzzy c-means algorithm. The approach is inspired from the concept of interaction between objects in physics. Each data point is regarded as a particle. A specific weight is associated with each data particle depending on its interaction with other particles. This interaction is induced by attraction forces between pairs of particles and the escape velocity from other particles. Classification experiments using two data sets from UCI repository demonstrate the outperformance of the proposed approach over other clustering algorithms. In addition, results demonstrate the effectiveness of the proposed scheme for segmentation of multispectral face images.

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Further improvements in Feature-Weighted Fuzzy C-Means

Cited by 37 publications

References 18 publications

A Novel Hierarchical Clustering Approach Based on Universal Gravitation

A Novel Hierarchical Clustering Approach Based on Universal Gravitation

Possibilistic Fuzzy Clustering for Categorical Data Arrays Based on Frequency Prototypes and Dissimilarity Measures

Gravitational weighted fuzzy c-means with application on multispectral image segmentation

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