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dc.contributor.author Shen, Qiang
dc.contributor.author Boongoen, Tossapon
dc.date.accessioned 2009-09-04T08:55:21Z
dc.date.available 2009-09-04T08:55:21Z
dc.date.issued 2009-08
dc.identifier.citation Shen , Q & Boongoen , T 2009 , ' Semi-Supervised OWA Aggregation for Link-Based Similarity Evaluation and Alias Detection ' pp. 288-293 . en
dc.identifier.other PURE: 77078
dc.identifier.other dspace: 2160/2910
dc.identifier.uri http://hdl.handle.net/2160/2910
dc.description T.Boongoen and Q. Shen. Semi-Supervised OWA Aggregation for Link-Based Similarity Evaluation and Alias Detection. Proceedings of the 18th International Conference on Fuzzy Systems (FUZZ-IEEE'09), pp. 288-293, 2009. Sponsorship: EPSRC en
dc.description.abstract Within the past decades, many fuzzy aggregationtechniques, ordered weighted averaging (OWA) in particular, have proven effective for a wide range of information processing tasks, such as decision making, image analysis, database and machine learning. Despite reported successes, their potentials have yet to be explored for the emerging problem of link analysis, which aims to discover similarity and relations amongst objects through their associations. Recently, several link-based similarity methods have been put forward to identifying similar objects in the Internet and publication domains. However, these techniques only take into account the cardinality property of a link structure that is highly sensitive to noise and causes a great number of false positives. In light of such challenge, this paper presents a novel OWA aggregation model that is capable of efficiently deriving a similarity measure through the integration of multiple link properties. The underlying approach is based on the methodology of stress function by which the aggregation behavior can be easily interpreted and modeled. In addition, a semi-supervised method is introduced to assist a user in designing a stress function, i.e. the weighting scheme of link properties, appropriate for a particular link network. The application of the OWA aggregation approach to alias detection is demonstrated and evaluated, against state-of- art link-based techniques, over datasets specifically related to terrorism, publication and email domains. en
dc.format.extent 6 en
dc.language.iso eng
dc.relation.ispartof en
dc.title Semi-Supervised OWA Aggregation for Link-Based Similarity Evaluation and Alias Detection en
dc.type Text en
dc.type.publicationtype Conference paper en
dc.contributor.institution Department of Computer Science en
dc.contributor.institution Advanced Reasoning Group en
dc.description.status Non peer reviewed en


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