A Generalized Enhanced Quantum Fuzzy Approach for Efficient Data Clustering

IEEE Access

Mukesh Prasad¹  Neha Bharill²  Aruna Tiwari³  Om Prakash Patel⁴  Manoranjan Mohanty⁵  Dong-Lin Li⁶  Lifeng Mu⁷  Omprakash Kaiwartya⁸ 

[1] Centre for Artificial Intelligence, Faculty of Engineering and Information Technology, School of Software, University of Technology Sydney, Ultimo, NSW, Australia;Department of Computer Science and Engineering, Indian Institute of Information Technology Dharwad, Hubli, India;Department of Computer Science and Engineering, Indian Institute of Information Technology Indore, Indore, India;Department of Computer Science and Engineering, KLE Technological University, Hubballi, India;Department of Computer Science, The University of Auckland, Auckland, New Zealand;Department of Electrical Engineering, National Chiao Tung University, Hsinchu, Taiwan;SHU-UTS SILC Business School, Shanghai University, Shanghai, China;School of Science and Technology, Nottingham Trent University at Clifton, Nottingham, U.K.;

关键词: Clustering;    quantum computing;    evolutionary algorithm;    fuzzy set theory;    bioinformatics;   

DOI  :  10.1109/ACCESS.2019.2891956

来源: DOAJ

【 摘 要 】

Data clustering is a challenging task to gain insights into data in various fields. In this paper, an Enhanced Quantum-Inspired Evolutionary Fuzzy C-Means (EQIE-FCM) algorithm is proposed for data clustering. In the EQIE-FCM, quantum computing concept is utilized in combination with the FCM algorithm to improve the clustering process by evolving the clustering parameters. The improvement in the clustering process leads to improvement in the quality of clustering results. To validate the quality of clustering results achieved by the proposed EQIE-FCM approach, its performance is compared with the other quantum-based fuzzy clustering approaches and also with other evolutionary clustering approaches. To evaluate the performance of these approaches, extensive experiments are being carried out on various benchmark datasets and on the protein database that comprises of four superfamilies. The results indicate that the proposed EQIE-FCM approach finds the optimal value of fitness function and the fuzzifier parameter for the reported datasets. In addition to this, the proposed EQIE-FCM approach also finds the optimal number of clusters and more accurate location of initial cluster centers for these benchmark datasets. Thus, it can be regarded as a more efficient approach for data clustering.

【 授权许可】

Unknown