Robust regression and outlier detection
Robust regression and outlier detection
The R*-tree: an efficient and robust access method for points and rectangles
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
Discriminant Adaptive Nearest Neighbor Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fuzzy Modeling for Control
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Handbook of Chemometrics and Qualimetrics
Handbook of Chemometrics and Qualimetrics
Learning from User Behavior in Image Retrieval: Application of Market Basket Analysis
International Journal of Computer Vision - Special Issue on Content-Based Image Retrieval
Clustering Incomplete Data Using Kernel-Based Fuzzy C-means Algorithm
Neural Processing Letters
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Attribute weighted mercer kernel based fuzzy clustering algorithm for general non-spherical datasets
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Hierarchical clustering of mixed data based on distance hierarchy
Information Sciences: an International Journal
On fuzzy cluster validity indices
Fuzzy Sets and Systems
Automatic image pixel clustering with an improved differential evolution
Applied Soft Computing
Robust fuzzy clustering-based image segmentation
Applied Soft Computing
Adaptive spatial information-theoretic clustering for image segmentation
Pattern Recognition
Rough-DBSCAN: A fast hybrid density based clustering method for large data sets
Pattern Recognition Letters
A new Kernelized hybrid c-mean clustering model with optimized parameters
Applied Soft Computing
Kernel-based fuzzy clustering and fuzzy clustering: A comparative experimental study
Fuzzy Sets and Systems
Data clustering: 50 years beyond K-means
Pattern Recognition Letters
APSCAN: A parameter free algorithm for clustering
Pattern Recognition Letters
A note on the Gustafson-Kessel and adaptive fuzzy clustering algorithms
IEEE Transactions on Fuzzy Systems
A new kernel-based fuzzy clustering approach: support vector clustering with cell growing
IEEE Transactions on Fuzzy Systems
Image Segmentation Based on Adaptive Cluster Prototype Estimation
IEEE Transactions on Fuzzy Systems
Learning a semantic space from user's relevance feedback for image retrieval
IEEE Transactions on Circuits and Systems for Video Technology
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In this paper we propose a new density based clustering algorithm via using the Mahalanobis metric. This is motivated by the current state-of-the-art density clustering algorithm DBSCAN and some fuzzy clustering algorithms. There are two novelties for the proposed algorithm: One is to adopt the Mahalanobis metric as distance measurement instead of the Euclidean distance in DBSCAN and the other is its effective merging approach for leaders and followers defined in this paper. This Mahalanobis metric is closely associated with dataset distribution. In order to overcome the unique density issue in DBSCAN, we propose an approach to merge the sub-clusters by using the local sub-cluster density information. Eventually we show how to automatically and efficiently extract not only 'traditional' clustering information, such as representative points, but also the intrinsic clustering structure. Extensive experiments on some synthetic datasets show the validity of the proposed algorithm. Further the segmentation results on some typical images by using the proposed algorithm and DBSCAN are presented in this paper and they are shown that the proposed algorithm can produce much better visual results in image segmentation.