Automatic subspace clustering of high dimensional data for data mining applications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Concept Formation and Knowledge Revision
Concept Formation and Knowledge Revision
Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
Interval Set Clustering of Web Users with Rough K-Means
Journal of Intelligent Information Systems
Temporal analysis of clusters of supermarket customers: conventional versus interval set approach
Information Sciences—Informatics and Computer Science: An International Journal
Some refinements of rough k-means clustering
Pattern Recognition
Temporal analysis of clusters of supermarket customers: conventional versus interval set approach
Information Sciences: an International Journal
Outliers in rough k-means clustering
PReMI'05 Proceedings of the First international conference on Pattern Recognition and Machine Intelligence
A partitive rough clustering algorithm
RSCTC'06 Proceedings of the 5th international conference on Rough Sets and Current Trends in Computing
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Rough Sets Theory has been applied to build classifiers by exploring symbolic relations in data. Indiscernibility relations combined with the concept notion, and the application of set operations, lead to knowledge discovery in an elegant and intuitive way. In this paper we argue that the indiscernibility relation has a strong appeal to be applied in clustering since itself is a sort of natural clustering in the n-dimensional space of attributes. We explore this fact to build a clustering scheme that discovers straight structures for clusters in the sub-dimensional space of the attributes. As the usual clustering process is a kind of search for concepts, the scheme here proposed provides a better description of such clusters allowing the analyst to figure out what cluster has meaning to be considered as a concept. The basic idea is to find reducts in a set of objects and apply them to any clustering procedure able to cope with discrete data. We apply the approach to a toy example of animal taxonomy in order to show its functionality.