A kernel-based intuitionistic weight fuzzy k-modes algorithm using coupled chained P system combines DNA genetic rules for categorical data

Jiang, Zhenni; Liu, Xiyu; Zang, Wenke

doi:10.1016/j.neucom.2023.01.020

Cited by 4 publications

(5 citation statements)

References 41 publications

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“…In this section, we give experimental results of the improved strategy based on the FKM algorithm on these datasets, as shown in Table 2, and conduct comparative analysis on the experimental results. To distinguish between them [10,20] , we use the labels Jiang-1 [10] and Jiang-2 [20] , respectively. The experimental results show that the FKM improvement algorithms mentioned have significantly improved accuracy (ACC) in processing all data sets compared to the FKM algorithm.…”

Section: Resultsmentioning

confidence: 99%

“…Jiang et al [20] proposed a new method based on the kernel method to estimate the distance between the data points, and used an attribute weighting method to improve the clustering effect of IWFKM algorithm in their study in 2023. They proposed a new KIWFKM-DCP algorithm, which used the coupling chain P system combined with DNA genetic rules (DCP system) instead of genetic algorithm to perform the clustering process, and utilized the uncertainty and implicit parallelism of the DCP system to help the algorithm break out local optimal solutions and improved the accuracy and efficiency of the algorithm.…”

Section: Attribute Weightingmentioning

confidence: 99%

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Research on improvement of fuzzy K-modes clustering algorithm for categorical data

Wang,

Yu,

et al. 2024

International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024)

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Cluster analysis is an important method in data mining and has been widely used in various aspects of real life. The Fuzzy K-Modes algorithm is one of the most commonly used algorithms for clustering categorical data due to its simplicity and effectiveness. However, this algorithm has three main problems: sensitivity of initial cluster centers, vulnerability of distance measurement methods to noise, and failure to consider different contributions of attributes during the clustering process. In this paper, we overview some existing improvement algorithms based on the three problems in detail and conduct experimental analysis of these algorithms on real data sets. Furthermore, we summarize the challenges faced in the process of improving the Fuzzy K-Modes algorithm and give possible research directions in future.

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Section: Resultsmentioning

confidence: 99%

Section: Attribute Weightingmentioning

confidence: 99%

Research on improvement of fuzzy K-modes clustering algorithm for categorical data

Wang,

Yu,

et al. 2024

International Conference on Image Processing and Artificial Intelligence (ICIPAl 2024)

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“…Two types of evolution rules are introduced to modify the velocity of objects in elementary membranes, based on the velocity updating model of classic PSO and modified PSO with environmental factors. Thus, V i (t + 1) of u i in σ o at t + 1 is defined by (6) in the following: (6) Another evolution rule is defined by (7) in the following: (7) And ω is determined by (8) in the following:…”

Section: Evolution Rulesmentioning

confidence: 99%

“…Therefore, the substructure of TPs is vividly depicted by an undigraph. The study of TPs has been broadly divided into theoretical and application studies [8]. In theoretical studies, many kinds of TPs based on various biological motivations have been constructed and developed to expand the types of MC [9,10].…”

Section: Introductionmentioning

confidence: 99%

An Extended Membrane System with Monodirectional Tissue-like P Systems and Enhanced Particle Swarm Optimization for Data Clustering

Wang

Liu

et al. 2023

Applied Sciences

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In order to establish a highly efficient P system for resolving clustering problems and overcome the computation incompleteness and implementation difficulty of P systems, an attractive clustering membrane system, integrated with enhanced particle swarm optimization (PSO) based on environmental factors and crossover operators and a distributed parallel computing model of monodirectional tissue-like P systems (MTP), is constructed and proposed, which is simply named ECPSO-MTP. In the proposed ECPSO-MTP, two kinds of evolution rules for objects are defined and introduced to rewrite and modify the velocity of objects in different elementary membranes. The velocity updating model uses environmental factors based on partitioning information and randomly replaces global best to improve the clustering performance of ECPSO-MTP. The crossover operator for the position of objects is based on given objects and other objects with crossover probability and is accomplished through the hybridization of the global best of elementary membranes to reject randomness. The membrane structure of ECPSO-MTP is abstracted as a network structure, and the information exchange and resource sharing between different elementary membranes are accomplished by evolutional symport rules with promoters for objects of MTP, including forward and backward communication rules. The evolution and communication mechanisms in ECPSO-MTP are executed repeatedly through iteration. At last, comparison experiments, which are conducted on eight benchmark clustering datasets from artificial datasets and the UCI Machine Learning Repository and eight image segmentation datasets from BSDS500, demonstrate the effectiveness of the proposed ECPSO-MTP.

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“…In 2023, Jiang et al introduced an algorithm named the kernel-based intuitionistic weight fuzzy K-modes (KIWFKM), which integrates the IFS with kernel-trick and weighting mechanisms [93]. This algorithm aims to overcome noise and distinguish important attributes.…”

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confidence: 99%

Categorical Data Clustering: A Bibliometric Analysis and Taxonomy

Cendana,

Kuo

2024

MAKE

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Numerous real-world applications apply categorical data clustering to find hidden patterns in the data. The K-modes-based algorithm is a popular algorithm for solving common issues in categorical data, from outlier and noise sensitivity to local optima, utilizing metaheuristic methods. Many studies have focused on increasing clustering performance, with new methods now outperforming the traditional K-modes algorithm. It is important to investigate this evolution to help scholars understand how the existing algorithms overcome the common issues of categorical data. Using a research-area-based bibliometric analysis, this study retrieved articles from the Web of Science (WoS) Core Collection published between 2014 and 2023. This study presents a deep analysis of 64 articles to develop a new taxonomy of categorical data clustering algorithms. This study also discusses the potential challenges and opportunities in possible alternative solutions to categorical data clustering.

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A kernel-based intuitionistic weight fuzzy k-modes algorithm using coupled chained P system combines DNA genetic rules for categorical data

Cited by 4 publications

References 41 publications

Research on improvement of fuzzy K-modes clustering algorithm for categorical data

Research on improvement of fuzzy K-modes clustering algorithm for categorical data

An Extended Membrane System with Monodirectional Tissue-like P Systems and Enhanced Particle Swarm Optimization for Data Clustering

Categorical Data Clustering: A Bibliometric Analysis and Taxonomy

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