Relational Generalizations of Cluster Validity Indices

Sledge, Isaac J.; Bezdek, James C.; Havens, Timothy C.; Keller, James M.

doi:10.1109/tfuzz.2010.2048114

Cited by 48 publications

(25 citation statements)

References 51 publications

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“…The most important problem of the adjustment of the clustering parameters was the selection of the final number of clusters. Multiple validity indices were studied [34] and the best results were obtained with the Xie-Beni Index, which was selected [35], It aims to quantify the ratio of the total variation within clusters to the separation among them:…”

Section: Influential Attribute Selectionmentioning

confidence: 99%

Social and Content Hybrid Image Recommender System for Mobile Social Networks

et al. 2012

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One of the advantages of social networks is the possibility to socialize and personalize the content created or shared by the users. In mobile social networks, where the devices have limited capabilities in terms of screen size and computing power, Multimedia Recommender Systems help to present the most relevant content to the users, depending on their tastes, relationships and profile. Previous recommender systems are not able to cope with the uncertainty of automated tagging and are knowledge domain dependant. In addition, the instantiation of a recommender in this domain should cope with problems arising from the collaborative filtering inherent nature (cold start, banana problem, large number of users to run, etc.). The solution presented in this paper addresses the abovementioned problems by proposing a hybrid image recommender system, which combines collaborative filtering (social techniques) with content-based techniques, leaving the user the liberty to give these processes a personal weight. It takes into account aesthetics and the formal characteristics of the images to overcome the problems of current techniques, improving the performance of existing systems to create a mobile social networks recommender with a high degree of adaptation to any kind of user.

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Section: Influential Attribute Selectionmentioning

confidence: 99%

Social and Content Hybrid Image Recommender System for Mobile Social Networks

et al. 2012

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“…When R is not an Euclidean matrix, the β spread transformation described in (Hathaway e Bezdek, 1994;Sledge et al, 2010) can be employed to turn R into an Euclidean one. This letter is intended to be a complement to those theoretical findings in (Sledge et al, 2010).…”

Section: F3 Conclusionmentioning

confidence: 99%

“…These dissimilarities can be conveniently disposed in a relational matrix R = [r i,j ] n×n , in which r i,j is a measure of distance between objects o i and o j . As in (Sledge et al, 2010), it is assumed here that r i,j ∈ R + (non-negative reals), r i,i = 0, and r i,j = r j,i , for all i, j ∈ N 1,n .…”

Section: F1 Introductionmentioning

confidence: 99%

“…In an interesting paper, Sledge et al (Sledge et al, 2010) generalized three well-known clustering validity indices to the context of relational data, thus making it possible the use of such indices to evaluate results obtained by relational clustering algorithms. In doing so, these APÊNDICE F. COMMENTS ON "REL.…”

Section: F1 Introductionmentioning

confidence: 99%

“…In the above paper (Sledge et al, 2010), the authors proposed an ingenious framework to generalize many clustering validity indices to a relational context in which the data to be clustered are described solely by pairwise dissimilarities. This letter is intended to complement that framework in such a way that some particular distances can now be exactly calculated.…”

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confidence: 99%

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Relational Generalizations of Cluster Validity Indices

Cited by 48 publications

References 51 publications

Social and Content Hybrid Image Recommender System for Mobile Social Networks

Social and Content Hybrid Image Recommender System for Mobile Social Networks

Algoritmos e técnicas de validação em agrupamento de dados multi-representados, agrupamento possibilístico e bi-agrupamento

A Real-Time Method for Detecting Temporary Process Variants in Event Log Data

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