Trusted Software Construction Model Based on Trust Shell

Tian, Jun; Zhu, Ye

doi:10.4028/www.scientific.net/amr.186.251

Cited by 1 publication

(1 citation statement)

References 8 publications

(7 reference statements)

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“…But the corresponding optimization problem is NP hard, so many algorithms resort to heuristics (e.g., in the k-means algorithm using only local optimization procedures potentially ending in local minima). The main point is that clustering is a difficult problem for which nding optimal solutions is often not possible [7]. For that same reason, selection of the particular clustering algorithm and its parameters (e.g., similarity measure) depend on many factors, including the characteristics of the data.…”

Section: B Clustering Based On Uncertain Interestsmentioning

confidence: 99%

Method of Collaborative Filtering Based on Uncertain User Interests Cluster

Cui¹,

Yin²,

Zhang³

et al. 2013

JCP

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Recommender systems have been proven to be valuable means for Web online users to cope with the information overload and have become one of the most powerful and popular tools in electronic commerce. The suggestions provided are aimed at supporting their users in various decision-making processes, such as what items to buy, what music to listen, or what news to read. In the paper, we introduce the uncertain interests of users because computer logs take down the data that have uncertain features. Consequently, user interest data recorded in computer logs is uncertain data. First of all the definition of uncertain interests are put forward. Then, some method using a clustering algorithm can solve the uncertain feature. In the algorithm, we compute the between-class entropy of any two clusters and get the stable classes. Secondly, because calculation input is uncertainty, the results of clustering algorithm are latent non-determinacy. The trustworthy degree of uncertain interests is defined that it can measure the rationality of clustering algorithm results. Thirdly, improvement of collaborative filtering is presented as an advanced CF algorithm based on trustworthy degree of uncertain interests. At last, we provide some evaluation of the algorithms and propose the more improve ideas in the future.

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Section: B Clustering Based On Uncertain Interestsmentioning

confidence: 99%