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IEEE International Conference on Wireless Communications, Signal Processing and Networking (IEEE WiSPNET)

作者： Manju, S. Thenmozhi, M. Pondicherry Engn Coll Dept Comp Sci & Engn Pondicherry India

ISBN: (纸本)9781538636244

Recommender Systems use collaborative filtering in order to make recommendations based on similar interest between users or items. In this process, Privacy of users is at severe risk because recommender servers may share user's private data with third parties to make personalized advertisements and in some cases user privacy may be exposed to the public or attacked by malicious users. The existing works are based on encryption based and randomization-based techniques, but they compromise accuracy for privacy and privacy for accuracy. In this project, Privacy Preserving Collaborative Filtering approach has been proposed which solves the limitations in the existing works. This work adopts fuzzy logic to deal with uncertainty among user's interest ratings. The fuzzified data is then perturbated by applying random rotation perturbation technique, thus the user's interest is not directly available for the recommendation server or the third-party. Using the perturbated data, item clusters are formed by utilizing ant-based clustering algorithm. These clusters help the recommendation server to apply item collaborative filtering for the recommendation process. In order to refine the cluster center provided by ant-based clustering process K-Means clustering algorithm is applied. The pheromone values obtained during the ant-based clustering is further utilized by the recommender server in order to provide accurate recommendation to the active user.

关键词： Recommender system Collaborative filtering Fuzzy logic ant-based clustering algorithm k-means clustering algorithm

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Finding groups in data: Cluster analysis with ants

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APPLIED SOFT COMPUTING 2009年第1期9卷 61-70页

作者： Boryczka, Urszula Univ Silesia Inst Comp Sci PL-41200 Sosnowiec Poland

We present in this paper a modification of Lumer and Faieta's algorithm for data clustering. This approach mimics the clustering behavior observed in real ant colonies. This algorithm discovers automatically clusters in numerical data without prior knowledge of possible number of clusters. In this paper we focus on ant-based clustering algorithms, a particular kind of a swarm intelligent system, and on the effects on the final clustering by using during the classification different metrics of dissimilarity: Euclidean, Cosine, and Gower measures. clustering with swarm-based algorithms is emerging as an alternative to more conventional clustering methods, such as e. g. k-means, etc. Among the many bio-inspired techniques, ant clustering algorithms have received special attention, especially because they still require much investigation to improve performance, stability and other key features that would make such algorithms mature tools for data mining. As a case study, this paper focus on the behavior of clustering procedures in those new approaches. The proposed algorithm and its modi. cations are evaluated in a number of well-known benchmark datasets. Empirical results clearly show that ant-based clustering algorithms performs well when compared to another techniques. (C) 2008 Elsevier B.V. All rights reserved.

关键词： ant-based clustering algorithm Data clustering Visual data clustering Classification

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