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http://hdl.handle.net/20.500.12358/24554
TitleClustering Using Optimized Gaussian Kernel Function
Untitled
Abstract

Clustering and segmentation algorithms that depend on Gaussian kernel function as a way for constructing affinity matrix, these algorithms like spectral clustering algorithms suffer from the poor estimation of parzen window . The final results depend on this parameter and differ on each time we change it.In this paper we present a new algorithm for estimation using optimization techniques, we construct a vector , each corresponding to i th row in a dissimilarity matrix which is used to construct an affinity matrix using Gaussian kernel function. Our algorithm shows that choosing as the formula 2 = ( , ) 2 ( , ) 2 is the opti-2 ( , ) 2 ( , ) 2 mum estimation, and we introduce more than one approach to calculate global value for from this vector. The affinity matrix which is produced using our algorithm is very informative and contains addition information like the number of clusters .

Authors
El-Bhissy, Kanaan
El-Faleet, Fadi
Ashour, Wesam M.
TypeJournal Article
Date2014
Subjects
kernel trick
eigen vectors
data clustering
adaptive sigma
mining algorithms
spectral clustering
gaussiankernel
Published inInternational Journal of Artificial Intelligence and Application for Smart Devices IJAIASD
SeriesVolume: 2, Number: 1
PublisherSERSC
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  • Staff Publications- Faculty of Engineering [906]
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The institutional repository of the Islamic University of Gaza was established as part of the ROMOR project that has been co-funded with support from the European Commission under the ERASMUS + European programme. This publication reflects the views only of the author, and the Commission cannot be held responsible for any use which may be made of the information contained therein.

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The institutional repository of the Islamic University of Gaza was established as part of the ROMOR project that has been co-funded with support from the European Commission under the ERASMUS + European programme. This publication reflects the views only of the author, and the Commission cannot be held responsible for any use which may be made of the information contained therein.

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