Genetic intuitionistic weighted fuzzy k-modes algorithm for categorical data

Kuo, R. J.; Nguyen, Thi Phuong Quyen

doi:10.1016/j.neucom.2018.11.016

Cited by 24 publications

(23 citation statements)

References 20 publications

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“…One of the first attempts proposed to handle uncertainty was by using the fuzzy sets theory. As an extension of the fuzzy k-means, the fuzzy k -modes [ 24 ] were proposed and many variants were also developed [ 10 ]. In this algorithm, each pattern or object can have membership functions to all clusters rather than having a strict membership to exactly one cluster.…”

Section: State Of the Art Of Categorical Clusteringmentioning

confidence: 99%

“…The DR k -M will implement the simple matching dissimilarity measure defined in Eqs. ( 4 ) and ( 5 ) during the assignments step of the observations to their closest clusters [ 4 , 24 ].…”

Section: The Dr K -M Paradigmmentioning

confidence: 99%

“…1 The k-modes algorithm and its variants compared to the DRk-M be assigned to the clusters according to the value of similarity computed using the simple matching dissimilarity metric between these points and the centroids. In this step, some variants of the k-modes, proposed using other distance metrics to enhance the algorithm [7,10,24,38]. Once all the observations are assigned to their corresponding clusters, the centroids are updated in the last step of the algorithm and the new mode is computed for each group.…”

Section: Introductionmentioning

confidence: 99%

“…This fact requires integrating uncertainty based models in the clustering process. The first attempts to handle uncertainty were proposed with fuzzy theory such as the fuzzy k -means [ 23 ], the fuzzy k -modes [ 24 ] and their variants. In fuzzy clustering, each object can have membership functions to more than one cluster instead of the hard assignment given in the k-modes based methods.…”

Section: Introductionmentioning

confidence: 99%

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A rough set based algorithm for updating the modes in categorical clustering

Salem

Naouali²,

Chtourou

2021

Int. J. Mach. Learn. & Cyber.

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The categorical clustering problem has attracted much attention especially in the last decades since many real world applications produce categorical data. The k-mode algorithm, proposed since 1998, and its multiple variants were widely used in this context. However, they suffer from a great limitation related to the update of the modes in each iteration. The mode in the last step of these algorithms is randomly selected although it is possible to identify many candidate ones. In this paper, a rough density mode selection method is proposed to identify the adequate modes among a list of candidate ones in each iteration of the k-modes. The proposed method, called Density Rough k-Modes (DRk-M) was experimented using real world datasets extracted from the UCI Machine Learning Repository, the Global Terrorism Database (GTD) and a set of collected Tweets. The DRk-M was also compared to many states of the art clustering methods and has shown great efficiency.

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Section: State Of the Art Of Categorical Clusteringmentioning

confidence: 99%

“…The DR k -M will implement the simple matching dissimilarity measure defined in Eqs. ( 4 ) and ( 5 ) during the assignments step of the observations to their closest clusters [ 4 , 24 ].…”

Section: The Dr K -M Paradigmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A rough set based algorithm for updating the modes in categorical clustering

Salem

Naouali²,

Chtourou

2021

Int. J. Mach. Learn. & Cyber.

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“…The GFKM algorithm [15] integrated the genetic algorithm into the fuzzy k-modes algorithm, aiming to find the global optimal solution. The IWFKM algorithm [16] replaced the Hamming distance with the frequency probability-based distance metric, which has been proved to improve the clustering results. Apart from this method, the multi-objective clustering algorithm was also considered to improve the performance of the categorical algorithm [17].…”

Section: Introductionmentioning

confidence: 99%

A Novel Consensus Fuzzy K-Modes Clustering Using Coupling DNA-Chain-Hypergraph P System for Categorical Data

Jiang

Liu

2020

Processes

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In this paper, a data clustering method named consensus fuzzy k-modes clustering is proposed to improve the performance of the clustering for the categorical data. At the same time, the coupling DNA-chain-hypergraph P system is constructed to realize the process of the clustering. This P system can prevent the clustering algorithm falling into the local optimum and realize the clustering process in implicit parallelism. The consensus fuzzy k-modes algorithm can combine the advantages of the fuzzy k-modes algorithm, weight fuzzy k-modes algorithm and genetic fuzzy k-modes algorithm. The fuzzy k-modes algorithm can realize the soft partition which is closer to reality, but treats all the variables equally. The weight fuzzy k-modes algorithm introduced the weight vector which strengthens the basic k-modes clustering by associating higher weights with features useful in analysis. These two methods are only improvements the k-modes algorithm itself. So, the genetic k-modes algorithm is proposed which used the genetic operations in the clustering process. In this paper, we examine these three kinds of k-modes algorithms and further introduce DNA genetic optimization operations in the final consensus process. Finally, we conduct experiments on the seven UCI datasets and compare the clustering results with another four categorical clustering algorithms. The experiment results and statistical test results show that our method can get better clustering results than the compared clustering algorithms, respectively.

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