Title

Keynote: The use of Meta-Heuristic Algorithms for data mining

Abstract/Description

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 of robust methods for solving real-life problems in a reasonable time and the ability to apply these methods to the analysis of large repositories of data may hold the key for success in many other scientific and commercial application areas.

Location

Crystal Ball Room A, Hotel Pearl Continental, Karachi, Pakistan

Session Theme

Keynote Speeches

Session Type

Keynote Speech

Start Date

28-8-2005 10:30 AM

End Date

28-8-2005 11:00 AM

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Aug 28th, 10:30 AM Aug 28th, 11:00 AM

Keynote: The use of Meta-Heuristic Algorithms for data mining

Crystal Ball Room A, Hotel Pearl Continental, Karachi, Pakistan

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 of robust methods for solving real-life problems in a reasonable time and the ability to apply these methods to the analysis of large repositories of data may hold the key for success in many other scientific and commercial application areas.