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.
Keywords
Data mining, Databases, Biology computing, Space technology, Robustness, Finance, Warehousing, Data analysis, Information technology, Statistical analysis
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
Recommended Citation
Iglesia, D. d., & Reynolds, A. (2005). Keynote: The use of Meta-Heuristic Algorithms for data mining. International Conference on Information and Communication Technologies. Retrieved from https://ir.iba.edu.pk/icict/2005/2005/9
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.
