Next Article in Journal
Digital Twin for Lyophilization by Process Modeling in Manufacturing of Biologics
Previous Article in Journal
CFD Hydrodynamics Investigations for Optimum Biomass Gasifier Design
Previous Article in Special Issue
A Representation of Membrane Computing with a Clustering Algorithm on the Graphical Processing Unit
Open AccessArticle

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

Business School, Academy of Management Science, Shandong Normal University, Jinan 250014, China
*
Author to whom correspondence should be addressed.
Processes 2020, 8(10), 1326; https://doi.org/10.3390/pr8101326
Received: 24 September 2020 / Revised: 14 October 2020 / Accepted: 15 October 2020 / Published: 21 October 2020
(This article belongs to the Special Issue Modeling, Simulation and Design of Membrane Computing System)
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. View Full-Text
Keywords: consensus clustering; fuzzy k-modes algorithm; chain P system; hypergraph structure consensus clustering; fuzzy k-modes algorithm; chain P system; hypergraph structure
Show Figures

Figure 1

MDPI and ACS Style

Jiang, Z.; Liu, X. A Novel Consensus Fuzzy K-Modes Clustering Using Coupling DNA-Chain-Hypergraph P System for Categorical Data. Processes 2020, 8, 1326.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop