Elysium Technologies Private Limited ISO 9001:2008 A leading Research and Development Division Madurai | Chennai | Kollam | Ramnad | Tuticorin | Singapore #230, Church Road, Anna Nagar, Madurai 625 020, Tamil Nadu, India : +91 452-4390702, 4392702, 4390651 Website: www.elysiumtechnologies.c om,www.elysiumtechnologies.in fo Email: info@elysiumtechnologie s.com Data Mining 2010 - 2011 01 Zipfs Trust Discovery in Structured P2P Network The use of peer-to-peer (P2P) applications is growing Dramatically. To finish transactions successfully, rust mechanism plays an important role, which not only compact the communication traffic but also the data discovery. In this paper we address the problem of trust discovery mechanism in structured P2P network we proposed before. The main contribution of this paper is addressing the Zipf’s law to trust discovery. At last, the experimentally evaluate the effectiveness of uniform and zipf trust distribution, the result shows that Zipf’s law performed advantages to uniform distribution 0 2 Personalized Web Search with Location Preferences As the amount of Web information grows rapidly, search engines must be able to retrieve information according to the user's preference. In this paper, we propose a new web search personalization approach that captures the user's interests and preferences in the form of concepts by mining search results and their click throughs. Due to the important role location information plays in mobile search, we separate concepts into content concepts and location concepts, and organize them into ontologies to create an ontology-based, multi-facet (OMF) pro_le to precisely capture the user's content and location interests and hence improve the search accuracy. Moreover, recognizing the fact that different users and queries may have different emphases on content and location information, we introduce the notion of content and location entropies to measure the amount of content and location information associated with a query, and click content and location entropies to measure how much the user is interested in the content and location information in the results. Accordingly, we propose to done personalization effectiveness based on the entropies and use it to balance the weights between the content and location facets. Finally, based on the derived ontologies and personalization effectiveness, we train an SVM to adapt a personalized ranking function for re-ranking of future search. We conduct extensive experiments to compare the precision produced by our OMF pro_les and that of a baseline method. Experimental results show that OMF improves the precision significantly compared to the baseline 0 3 Web Objects Clustering Using Transaction Log In this paper, we present a novel method for clustering web objects. Most of existing methods aren’t suffientto explore similar objects, because the basic data, which include attributes of objects, click-through data, and link data, are often sparse, carce or difficult to obtain. In contrast, the information we exploit is transaction log, which is more common, denser as well as noisier. To reduce the influence of the noises, we calculate the similarity in two steps. Firstly, we use a basic similarity to discover objects’ neighbors. The objects are represented by vectors consisting of their neighbors. Secondly, the cosine similarity of the object vectors is calculated for clustering. Experiments on synthetic data show that our method is robust against noises. Using noisy data, we increase the precision by 10%. Finally, we show real clustering results based on a movie dataset and achieve the coverage of 76% and the precision of 60%.
