International Journal of Man-Machine Studies
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Unsupervised Rough Set Classification Using GAs
Journal of Intelligent Information Systems
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
A Rough Set Theoretic Approach to Clustering
Fundamenta Informaticae
Rough clustering of sequential data
Data & Knowledge Engineering
Modelling of rough-fuzzy classifier
WSEAS TRANSACTIONS on SYSTEMS
Transactions on rough sets VI
RSFDGrC'03 Proceedings of the 9th international conference on Rough sets, fuzzy sets, data mining, and granular computing
Multiscale roughness measure for color image segmentation
Information Sciences: an International Journal
A Rough Set Theoretic Approach to Clustering
Fundamenta Informaticae
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Typical clustering operations in data mining involve finding natural groupings of resources or users. Conventional clusters have crisp boundaries, i.e. each object belongs to only one cluster. The clusters and associations in data mining do not necessarily have crisp boundaries. An object may belong to more than one cluster. Researchers have studied the possibility of using fuzzy sets in data mining clustering applications. Recently, two different methodologies based on properties of rough sets were proposed for developing interval representations of clusters. One approach is based on Genetic Algorithms, and the other is an adaptation of K-means algorithm. Both the approaches have been successful in generating intervals of clusters. The efficiency of the clustering algorithm is an important issue when dealing with a large dataset. This paper provides comparison of the time complexity of the two rough clustering algorithms.