Unsupervised clustering algorithm based on normalized Mahalanobis distances

Authors:
Jeng-Ming Yih;Sue-Fen Huang
Affiliations:
Department of Mathematics Education, National Taichung University, Taichung City, Taiwan, Taiwan;National Yunlin University of Science and Technology, Taiwan
Venue:
ACACOS'10 Proceedings of the 9th WSEAS international conference on Applied computer and applied computational science
Year:
2010

Citing 4
Cited 0

Unsupervised Optimal Fuzzy Clustering

IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition with Fuzzy Objective Function Algorithms

Pattern Recognition with Fuzzy Objective Function Algorithms
On the equivalence between Non-negative Matrix Factorization and Probabilistic Latent Semantic Indexing

Computational Statistics & Data Analysis
A note on the Gustafson-Kessel and adaptive fuzzy clustering algorithms

IEEE Transactions on Fuzzy Systems

Quantified Score

Hi-index	0.00

Visualization

Abstract

Some of the well-known fuzzy clustering algorithms are based on Euclidean distance function, which can only be used to detect spherical structural clusters. Gustafson-Kessel clustering algorithm and Gath-Geva clustering algorithm were developed to detect non-spherical structural clusters. However, the former needs added constraint of fuzzy covariance matrix, the later can only be used for the data with multivariate Gaussian distribution. Three improved Fuzzy C-Means algorithm based on different Mahalanobis distance, called FCM-M, FCM-CM and FCM-SM were proposed by our previous works, In this paper, an improved Fuzzy C-Means algorithm based on a Normalized Mahalanobis distance (FCM-NM) by taking a new threshold value and a new convergent process is proposed The experimental results of two real data sets show that our proposed new algorithm has the better performance.