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Full-Text Articles in Physical Sciences and Mathematics
Keynote: The Use Of Meta-Heuristic Algorithms For Data Mining, Dr. Beatrize De La Iglesia, A. Reynolds
Keynote: The Use Of Meta-Heuristic Algorithms For Data Mining, Dr. Beatrize De La Iglesia, A. Reynolds
International Conference on Information and Communication Technologies
In this paper we explore the application of powerful optimisers known as metaheuristic algorithms to problems within the data mining domain. We introduce some well-known data mining problems, and show how they can be formulated as optimisation problems. We then review the use of metaheuristics in this context. In particular, we focus on the task of partial classification and show how multi-objective metaheuristics have produced results that are comparable to the best known techniques but more scalable to large databases. We conclude by reinforcing the importance of research on the areas of metaheuristics for optimisation and data mining. The combination …
A Dynamic Weight Assignment Approach For Ir Systems, M. Shoaib, Prof Dr. Abad Ali Shah, A. Vashishta
A Dynamic Weight Assignment Approach For Ir Systems, M. Shoaib, Prof Dr. Abad Ali Shah, A. Vashishta
International Conference on Information and Communication Technologies
Weights are assigned to the extracted keywords for partial matching and computing ranking in an IR system. Weight assignment technique is suggested by the IR model that is used for an IR system. Currently suggested weight assignment techniques are static which means that once weight is assigned a keyword it remains unchanged during life-span of an IR system. In this paper, we suggest a dynamic weight assignment technique. This technique can be used by any IR model that supports partial matching.
Social Network Discovery By Mining Spatio-Temporal Events, Hady Lauw, Ee Peng Lim, Hwee Hwa Pang, Teck-Tim Tan
Social Network Discovery By Mining Spatio-Temporal Events, Hady Lauw, Ee Peng Lim, Hwee Hwa Pang, Teck-Tim Tan
Research Collection School Of Computing and Information Systems
Knowing patterns of relationship in a social network is very useful for law enforcement agencies to investigate collaborations among criminals, for businesses to exploit relationships to sell products, or for individuals who wish to network with others. After all, it is not just what you know, but also whom you know, that matters. However, finding out who is related to whom on a large scale is a complex problem. Asking every single individual would be impractical, given the huge number of individuals and the changing dynamics of relationships. Recent advancement in technology has allowed more data about activities of individuals …