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Title of article :
Statistical modeling of dissimilarity increments for d-dimensional data: Application in partitional clustering
Author/Authors :
Aidos، نويسنده , , Helena and Fred، نويسنده , , Ana، نويسنده ,
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روزنامه با شماره پیاپی سال 2012
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Abstract :
This paper addresses the use of high order dissimilarity models in data mining problems. We explore dissimilarities between triplets of nearest neighbors, called dissimilarity increments (DIs). We derive a statistical model of DIs for d-dimensional data (d-DID) assuming that the objects follow a multivariate Gaussian distribution. Empirical evidence shows that the d-DID is well approximated by the particular case d=2. We propose the application of this model in clustering, with a partitional algorithm that uses a merge strategy on Gaussian components. Experimental results, in synthetic and real datasets, show that clustering algorithms using DID usually outperform well known clustering algorithms.
Keywords :
Dissimilarity increments , Likelihood-ratio test , Minimum Description Length , Gaussian mixture decomposition , Partitional clustering
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