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MNV for Clustering Based on Non Symmetric Symbolic Proximity

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Executive Summary

In this paper, the authors bring out the importance of non-symmetric proximity values among symbolic objects in simulating the reality during clustering. The concept of Mutual Neighborhood Value (MNV) has been exploited on non-symmetric proximity values. The results of the experiments conducted reveal that the approaches based on non-symmetric proximity measures best suite the reality than the symmetric proximity measures. A symbolic object is defined by its intent which contains a way of finding its extent. For instance, the description of the inhabitant of a region and the way of allocating an individual to this region is called intent, the set of individual which satisfies this intent is called extent.

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