Shadowed c-means: Integrating fuzzy and rough clustering

Authors:
Sushmita Mitra;Witold Pedrycz;Bishal Barman
Affiliations:
Machine Intelligence Unit, Indian Statistical Institute, Kolkata 700 108, India;Electrical and Computer Engineering Department, University of Alberta, Edmonton, Canada T6G 2G7;Electrical Engineering Department, S.V. National Institute of Technology, Surat 395 007, India
Venue:
Pattern Recognition
Year:
2010

Citing 14
Cited 13

Silhouettes: a graphical aid to the interpretation and validation of cluster analysis

Journal of Computational and Applied Mathematics
A Validity Measure for Fuzzy Clustering

IEEE Transactions on Pattern Analysis and Machine Intelligence
Fuzzy logic, neural networks, and soft computing

Communications of the ACM
Rough Sets: Theoretical Aspects of Reasoning about Data

Rough Sets: Theoretical Aspects of Reasoning about Data
Pattern Recognition with Fuzzy Objective Function Algorithms

Pattern Recognition with Fuzzy Objective Function Algorithms
Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values

Data Mining and Knowledge Discovery
Data Mining: Concepts and Algorithms From Multimedia to Bioinformatics

Data Mining: Concepts and Algorithms From Multimedia to Bioinformatics
Interval Set Clustering of Web Users with Rough K-Means

Journal of Intelligent Information Systems
Interpretation of clusters in the framework of shadowed sets

Pattern Recognition Letters
Some refinements of rough k-means clustering

Pattern Recognition
RFCM: A Hybrid Clustering Algorithm Using Rough and Fuzzy Sets

Fundamenta Informaticae
Shadowed sets: representing and processing fuzzy sets

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Some new indexes of cluster validity

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Rough–Fuzzy Collaborative Clustering

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Shadowed sets in the characterization of rough-fuzzy clustering

Pattern Recognition
Satellite image segmentation with Shadowed C-Means

Information Sciences: an International Journal
An extension to rough c-means clustering

RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
Ant based clustering of time series discrete data --- a rough set approach

SEMCCO'11 Proceedings of the Second international conference on Swarm, Evolutionary, and Memetic Computing - Volume Part I
Partitive clustering (K-means family)

Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Dynamic rough clustering and its applications

Applied Soft Computing
Generalized rough fuzzy c-means algorithm for brain MR image segmentation

Computer Methods and Programs in Biomedicine
Soft clustering -- Fuzzy and rough approaches and their extensions and derivatives

International Journal of Approximate Reasoning
Rough clustering using generalized fuzzy clustering algorithm

Pattern Recognition
A method for building prototypes in the nearest prototype approach based on similarity relations for problems of function approximation

MICAI'12 Proceedings of the 11th Mexican international conference on Advances in Artificial Intelligence - Volume Part I
An extension to rough c-means clustering algorithm based on boundary area elements discrimination

Transactions on Rough Sets XVI
An extension to Rough c-means clustering based on decision-theoretic Rough Sets model

International Journal of Approximate Reasoning
Ant-Based Clustering in Delta Episode Information Systems Based on Temporal Rough Set Flow Graphs

Fundamenta Informaticae - Concurrency, Specification and Programming

Quantified Score

Hi-index	0.01

Visualization

Abstract

A new method of partitive clustering is developed in the framework of shadowed sets. The core and exclusion regions of the generated shadowed partitions result in a reduction in computations as compared to conventional fuzzy clustering. Unlike rough clustering, here the choice of threshold parameter is fully automated. The number of clusters is optimized in terms of various validity indices. It is observed that shadowed clustering can efficiently handle overlapping among clusters as well as model uncertainty in class boundaries. The algorithm is robust in the presence of outliers. A comparative study is made with related partitive approaches. Experimental results on synthetic as well as real data sets demonstrate the superiority of the proposed approach.