Classification of Binary Vectors by Using ??SC-Distance
نویسندگان
چکیده
Stochastic complexity (SC) has been employed as a cost function for solving binary clustering problem Shannon code length (CL-distance) has been previously applied for the purpose of classifying the data vectors during the clustering process. The CL-distance, however, is defined for a given (static) clustering only, and it does not take into account of the changes in the class distribution during the clustering process. We propose a new SC-distance function based on a design paradigm, in which the distance function is derived directly from the difference of the cost function value before and after the classification. The SC is general in the sense that it does not depend on the algorithm in which it is applied. The effect of the new distance function is demonstrated by implementing it with the GLA and the RLS clustering algorithms.
منابع مشابه
Classification of binary vectors by using SC distance to minimize stochastic complexity
Stochastic complexity (SC) has been employed as a cost function for solving binary clustering problem using Shannon code length (CL distance) as the distance function. The CL distance, however, is defined for a given static clustering only, and it does not take into account of the changes in the class distribution during the clustering process. We propose a new DSC distance function, which is d...
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