On the Improvement of Weighted Page Content Rank

Kadry, Seifedine; Kalakech, Ali

doi:10.7763/jacn.2013.v1.23

Cited by 3 publications

(1 citation statement)

References 10 publications

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“…The popularity from the number of in links and outlinks is recorded as Win and Wout respectively. Win (v, u) is the weight of link (v, u) calculated based on the number of in links of page u and the number of in lin ks of all reference pages of page v. [7] …”

Section: Weighted Pagerank Algorithmmentioning

confidence: 99%

Review of Various Web Page Ranking Algorithms in Web Structure Mining

2015

IJAERD

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Abstract:The World Wide Web contains large amount of data. These data is stored in the form of web pages .All these pages can be accessed using search engines. These search engines need to be very efficient as there are large number of Web pages as well as queries are submitted to the search engines. Page ranking algorithms are used by the search engines to present the search results by considering the relevance, importance and content score. Several web mining techniques are used to order them according to the user interest. In this paper such page ranking techniques are discussed.

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Section: Weighted Pagerank Algorithmmentioning

confidence: 99%

Review of Various Web Page Ranking Algorithms in Web Structure Mining

2015

IJAERD

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The Classification System of Literary Works Based on K-Means Clustering

et al. 2021

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The plots in certain literary works are very complicated and hinder readers from understanding them. Therefore tools should be proposed to support readers; comprehension of complex literary works supports their understanding by providing the most important information to readers. A human reader must capture multiple levels of abstraction and meaning to formulate an understanding of a document. Hence, in this paper, an Improved [Formula: see text]-means clustering algorithm (IKCA) has been proposed for literary word classification. For text data, the words that can express exact semantic in a class are generally better features. This paper uses the proposed technique to capture numerous cluster centroids for every class and then select the high-frequency words in centroids the text features for classification. Furthermore, neural networks have been used to classify text documents and [Formula: see text]-mean to cluster text documents. To develop the model based on unsupervised and supervised techniques to meet and identify the similarity between documents. The numerical results show that the suggested model will enhance to increases quality comparison of the existing Algorithm and [Formula: see text]-means algorithm, accuracy comparison of ALA and IKCA (95.2%), time is taken for clustering is less than 2 hours, success rate (97.4%) and performance ratio (98.1%).

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