An extension to rough c-means clustering algorithm based on boundary area elements discrimination

Authors:
Fan Li;Qihe Liu
Affiliations:
School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, P.R. China;School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, P.R. China
Venue:
Transactions on Rough Sets XVI
Year:
2013

Citing 32
Cited 1

Efficient Implementation of the Fuzzy c-Means Clustering Algorithms

IEEE Transactions on Pattern Analysis and Machine Intelligence
Algorithms for clustering data

Algorithms for clustering data
Local learning algorithms

Neural Computation
Data clustering: a review

ACM Computing Surveys (CSUR)
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
A Generalized Definition of Rough Approximations Based on Similarity

IEEE Transactions on Knowledge and Data Engineering
Dynamic Reducts as a Tool for Extracting Laws from Decisions Tables

ISMIS '94 Proceedings of the 8th International Symposium on Methodologies for Intelligent Systems
Rough Neurocomputing: A Survey of Basic Models of Neurocomputation

TSCTC '02 Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing
Reduction and axiomization of covering generalized rough sets

Information Sciences: an International Journal
Multiclass Spectral Clustering

ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Pattern Classification (2nd Edition)

Pattern Classification (2nd Edition)
Case Generation Using Rough Sets with Fuzzy Representation

IEEE Transactions on Knowledge and Data Engineering
Interval Set Clustering of Web Users with Rough K-Means

Journal of Intelligent Information Systems
A set theory for rough sets: toward a formal calculus of vague statements

Fundamenta Informaticae - Special issue on theory and applications of soft computing (TASC04)
Some refinements of rough k-means clustering

Pattern Recognition
On Three Types of Covering-Based Rough Sets

IEEE Transactions on Knowledge and Data Engineering
Three-way decisions with probabilistic rough sets

Information Sciences: an International Journal
Shadowed c-means: Integrating fuzzy and rough clustering

Pattern Recognition
Constructive and algebraic methods of the theory of rough sets

Information Sciences: an International Journal
Rough sets and near sets in medical imaging: a review

IEEE Transactions on Information Technology in Biomedicine - Special section on body sensor networks
Decision-theoretic rough set models

RSKT'07 Proceedings of the 2nd international conference on Rough sets and knowledge technology
Applications of rough set based K-means, Kohonen SOM, GA clustering

Transactions on rough sets VII
Rough-fuzzy clustering: an application to medical imagery

RSKT'08 Proceedings of the 3rd international conference on Rough sets and knowledge technology
Evolutionary rough k-medoid clustering

Transactions on rough sets VIII
An extension to rough c-means clustering

RSKT'11 Proceedings of the 6th international conference on Rough sets and knowledge technology
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
Least squares quantization in PCM

IEEE Transactions on Information Theory
Test-cost-sensitive attribute reduction

Information Sciences: an International Journal
Attribute reduction of data with error ranges and test costs

Information Sciences: an International Journal
Dynamic rough clustering and its applications

Applied Soft Computing

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

International Journal of Approximate Reasoning

Quantified Score

Hi-index	0.00

Visualization

Abstract

Rough c-means algorithm has gained increasing attention in recent years. However, the original Rough c-means algorithm does not distinguish data points in the boundary area while computing the new centroid of each cluster. In this paper, we consider the distinction between data points in the boundary area and present an extended Rough c-means algorithm which benefits from this information. The distinction is reflected by the degree of the data point in the boundary area being close to its corresponding lower approximation. This information is utilized in the step of calculating the new centroid of each cluster. The algorithm is tested on four UCI machine learning repository data sets. Experimental results indicate that the proposed algorithm yields more desirable clustering results than the original Rough c-means algorithm.