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This paper presents a topic based web news recommendation method combining Affinity Propagation (AP) and Latent Dirichlet Allocation (LDA), which could automatically find the topics exist in the web pages and recommend the topic based news to Internet users. The topic distance is defined using LDA, which is used to generate the topic distance matrix. AP clustering is used to cluster the web page collections...
Topic model is an increasing useful tool to analyze the semantic level meanings and capture the topical features. However, there is few research about the comparative study of the topic models. In this paper, we describe our comparative study of three topic models in the extrinsic application of topic clustering. The topic model distance is defined on the converged parameters of topic models, which...
The standard affinity propagation clustering algorithm suffers from one limitation that it is hard to know the value of the parameter ??preference?? which can yield an optimal clustering solution. To overcome this limitation, in this paper we proposes an adaptive affinity propagation method. The method first finds out the range of ??preference??, then searches the space of ??preference?? to find a...
Classical text clustering algorithms are usually based on vector space model or its variants. Because of the high computing complexity and the difficulty of controlling clustering results, this kind of approaches are hard to be applied for the purpose of the large scale text clustering. Clustering algorithms based on frequent term sets make use of relationship among documents and their shared frequent...
Comprehensive evaluation on health care system of ten countries is discussed in this paper. In order to comprehensively assess and evaluate the health care system in an objective manner, five first-tier indicators and nineteen second-tier indicators are set up as evaluation criteria in the first place. Then entropy modeling and Matlab software are applied to compute the values for first-tier indicators...
Web page content extraction can be achieved by node-based and segmentation-based algorithms respectively on top of the document object model (DOM). However, the node-based algorithm often removes content embedded as anchor text; while the segmentation-based way can not distinguish irrelevant text from content text when they are divided into the same segment. The two kinds of algorithms don't keep...
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