Fuzzy Clustering Method Based on Improved Weighted Distance
Open Access
- 6 March 2021
- journal article
- research article
- Published by Wiley in Mathematical Problems in Engineering
- Vol. 2021, 1-11
Abstract
As an essential data processing technology, cluster analysis has been widely used in various fields. In clustering, it is necessary to select appropriate measures to evaluate the similarity in the data. In this paper, firstly, a cluster center selection method based on the grey relational degree is proposed to solve the problem of sensitivity in initial cluster center selection. Secondly, combining the advantages of Euclidean distance, DTW distance, and SPDTW distance, a weighted distance measurement based on three kinds of reach is proposed. Then, it is applied to Fuzzy C-MeDOIDS and Fuzzy C-means hybrid clustering technology. Numerical experiments are carried out with the UCI datasets. The experimental results show that the accuracy of the clustering results is significantly improved by using the clustering method proposed in this paper. Besides, the method proposed in this paper is applied to the MUSIC INTO EMOTIONS and YEAST datasets. The clustering results show that the algorithm proposed in this paper can also achieve a better clustering effect when dealing with practical problems.Keywords
Funding Information
- National Key Research and Development Program of China (2019YFB1600405, 71774018, 2015M580128, 2020-HYLH-20)
- National Natural Science Foundation of China (2019YFB1600405, 71774018, 2015M580128, 2020-HYLH-20)
- China Postdoctoral Science Foundation (2019YFB1600405, 71774018, 2015M580128, 2020-HYLH-20)
- Natural Science Foundation of Liaoning Province (2019YFB1600405, 71774018, 2015M580128, 2020-HYLH-20)
This publication has 10 references indexed in Scilit:
- An improved ant-based algorithm based on heaps merging and fuzzy c-means for clustering cancer gene expression dataSādhanā, 2020
- Two-Stage Topic Extraction Model for Bibliometric Data Analysis Based on Word Embeddings and ClusteringIEEE Access, 2019
- Hierarchical clustering of time series data with parametric derivative dynamic time warpingExpert Systems with Applications, 2016
- Time series k -means: A new k -means type smooth subspace clustering for time series dataInformation Sciences, 2016
- Time series clustering via community detection in networksInformation Sciences, 2016
- Time series modeling of paleoclimate dataEnvironmetrics, 2015
- Fuzzy clustering of time series data using dynamic time warping distanceEngineering Applications of Artificial Intelligence, 2015
- Optimal preprocessing and FCM clustering of MIR, NIR and combined MIR-NIR spectra for classification of maize roots2014
- Handling Big Data with Fuzzy Based Classification Approach2014
- A global averaging method for dynamic time warping, with applications to clusteringPattern Recognition, 2011