Rough Sets: Theoretical Aspects of Reasoning about Data
Rough Sets: Theoretical Aspects of Reasoning about Data
A Rough Set Theoretic Approach to Clustering
Fundamenta Informaticae
Rough clustering of sequential data
Data & Knowledge Engineering
The Knowledge Engineering Review
A Rough Set Theoretic Approach to Clustering
Fundamenta Informaticae
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This paper presents a new indiscernibility-based clustering method called rough clustering, that works on relative proximity. Our method lies its basis on iterative refinement of N binary classifications, where N denotes the number of objects. First, for each of N objects, an equivalence relation that classifies all the other objects into two classes, similar and dissimilar, is assigned by referring to their relative proximity. Next, for each pair of the objects, we count the number of binary classifications in which the pair is included in the same class. We call this number as indiscernibility degree. If the indiscernibility degree of a pair is larger than a user-defined threshold value, we modify the equivalence relations so that all of them commonly classify the pair into the same class. This process is repeated until class assignment becomes stable. Consequently, we obtain the clustering result that follows given level of granularity without using geometric measures.