Fuzzy C-Means clustering of incomplete data based on probabilistic information granules of missing values

Zhang, Liyong; Lu, Wei; Liu, Xiaodong; Pedrycz, Witold; Zhong, Chongquan

doi:10.1016/j.knosys.2016.01.048

Cited by 105 publications

(37 citation statements)

References 33 publications

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“…We randomly select ClusterNum different objects as initial cluster modes, and set the number of iterations of all algorithms is no more than 500. We use optimal completion strategy [24] to deal with the very few missing values in Mushroom data set.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

An Improved K-modes Clustering Algorithm Based on Intra-cluster and Inter-cluster Dissimilarity Measure

Zhou¹,

Zhang²,

Liu³

2017

Proceedings of the 2nd International Conference on Computer Engineering, Information Science &Amp; Application Technology (ICCI

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Abstract. Categorical data clustering has attracted much attentions recently because most practical data contains categorical attributes. The k-modes algorithm, as the extension of the k-means algorithm, is one of the most widely used clustering algorithms for categorical data. In this paper, we firstly analyzed the limitations of two existing dissimilarity measures. Based on this, we proposed a novel dissimilarity measure--IID. IID considers the relationship between the object and all clusters as well as that within clusters. Finally the experiments are made on six benchmark data sets from UCI. And the corresponding results show that IID achieves better performance than two existing ones used in k-modes and KBGRD algorithms.

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Section: Experimental Results and Analysismentioning

confidence: 99%

An Improved K-modes Clustering Algorithm Based on Intra-cluster and Inter-cluster Dissimilarity Measure

Zhou¹,

Zhang²,

Liu³

2017

Proceedings of the 2nd International Conference on Computer Engineering, Information Science &Amp; Application Technology (ICCI

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“…In the optimal completion strategy, the missing values in data set are viewed as additional variables [25, 26]. …”

Section: Experimental Analysismentioning

confidence: 99%

A Global-Relationship Dissimilarity Measure for thek-Modes Clustering Algorithm

Zhou

Zhang

Liu

2017

Computational Intelligence and Neuroscience

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The k-modes clustering algorithm has been widely used to cluster categorical data. In this paper, we firstly analyzed the k-modes algorithm and its dissimilarity measure. Based on this, we then proposed a novel dissimilarity measure, which is named as GRD. GRD considers not only the relationships between the object and all cluster modes but also the differences of different attributes. Finally the experiments were made on four real data sets from UCI. And the corresponding results show that GRD achieves better performance than two existing dissimilarity measures used in k-modes and Cao's algorithms.

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“…Before the data can be used to build the classification model and to classify the new instances, the data needs to be preprocessed. For example, there can be missing values [278][279][280][281] in the data that need to be imputed. Preprocessing can also be used to speed up or improve the classification process.…”

Section: Distribution Papers Based On Feature or Attribute Selectionmentioning

confidence: 99%

Recent Fuzzy Generalisations of Rough Sets Theory: A Systematic Review and Methodological Critique of the Literature

et al. 2017

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Rough set theory has been used extensively in fields of complexity, cognitive sciences, and artificial intelligence, especially in numerous fields such as expert systems, knowledge discovery, information system, inductive reasoning, intelligent systems, data mining, pattern recognition, decision-making, and machine learning. Rough sets models, which have been recently proposed, are developed applying the different fuzzy generalisations. Currently, there is not a systematic literature review and classification of these new generalisations about rough set models. Therefore, in this review study, the attempt is made to provide a comprehensive systematic review of methodologies and applications of recent generalisations discussed in the area of fuzzy-rough set theory. On this subject, the Web of Science database has been chosen to select the relevant papers. Accordingly, the systematic and metaanalysis approach, which is called "PRISMA," has been proposed and the selected articles were classified based on the author and year of publication, author nationalities, application field, type of study, study category, study contribution, and journal in which the articles have appeared. Based on the results of this review, we found that there are many challenging issues related to the different application area of fuzzy-rough set theory which can motivate future research studies.

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Fuzzy C-Means clustering of incomplete data based on probabilistic information granules of missing values

Cited by 105 publications

References 33 publications

An Improved K-modes Clustering Algorithm Based on Intra-cluster and Inter-cluster Dissimilarity Measure

An Improved K-modes Clustering Algorithm Based on Intra-cluster and Inter-cluster Dissimilarity Measure

A Global-Relationship Dissimilarity Measure for thek-Modes Clustering Algorithm

Recent Fuzzy Generalisations of Rough Sets Theory: A Systematic Review and Methodological Critique of the Literature

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