Applications of rough set based K-means, Kohonen SOM, GA clustering

Authors:
Pawan Lingras
Affiliations:
Department of Mathematics and Computing Science, Saint Mary's University, Halifax, Nova Scotia, Canada
Venue:
Transactions on rough sets VII
Year:
2007

Citing 12
Cited 7

Rough classification

International Journal of Man-Machine Studies
Self-organization and associative memory: 3rd edition

Self-organization and associative memory: 3rd edition
Unsupervised Rough Set Classification Using GAs

Journal of Intelligent Information Systems
Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence

Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control and Artificial Intelligence
Rough Sets: Theoretical Aspects of Reasoning about Data

Rough Sets: Theoretical Aspects of Reasoning about Data
Genetic Algorithms

Genetic Algorithms
Information Granules in Distributed Environment

RSFDGrC '99 Proceedings of the 7th International Workshop on New Directions in Rough Sets, Data Mining, and Granular-Soft Computing
Interval Set Clustering of Web Users with Rough K-Means

Journal of Intelligent Information Systems
Interval set clustering of web users using modified Kohonen self-organizing maps based on the properties of rough sets

Web Intelligence and Agent Systems
Constructive and algebraic methods of the theory of rough sets

Information Sciences: an International Journal
Fuzzy C-means clustering of web users for educational sites

AI'03 Proceedings of the 16th Canadian society for computational studies of intelligence conference on Advances in artificial intelligence
Analogy-based reasoning in classifier construction

Transactions on Rough Sets IV

A Dynamic Approach to Rough Clustering

RSCTC '08 Proceedings of the 6th International Conference on Rough Sets and Current Trends in Computing
Evolutionary and Iterative Crisp and Rough Clustering II: Experiments

PReMI '09 Proceedings of the 3rd International Conference on Pattern Recognition and Machine Intelligence
Rough sets and near sets in medical imaging: a review

IEEE Transactions on Information Technology in Biomedicine - Special section on body sensor networks
Interval set clustering

Expert Systems with Applications: An International Journal
Projected Gustafson-Kessel clustering algorithm and its convergence

Transactions on rough sets XIV
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

Quantified Score

Hi-index	0.00

Visualization

Abstract

Rough set theory provides an alternative way of representing sets whose exact boundary cannot be described due to incomplete information. Rough sets have been widely used for classification and can be equally beneficial in clustering. The clusters in practical data mining do not necessarily have crisp boundaries. An object may belong to more than one cluster. This paper describes modifications of clustering based on Genetic Algorithms, K-means algorithm, and Kohonen Self-Organizing Maps (SOM). These modifications make it possible to represent clusters as rough sets. Rough clusters are shown to be useful for representing groups of highway sections, Web users, and supermarket customers. The rough clusters are also compared with conventional and fuzzy clusters.