Using ant colonies to solve data-mining problems

Data mining is a constantly growing area. More and more domains of the daily life take advantage of the available tools (medicine, trade, meteorology, ...). However, such tools are confronted to a particular problem: the great number of characteristics that qualify data samples. They are more or les...

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Bibliographic Details
Main Authors: Admane, L., Benatchba, K., Koudil, M., Drias, M., Gharout, S., Hamani, N.
Format: Conference Proceeding
Language:English
Subjects:
Ant colony optimization
Applied sciences
Artificial intelligence
Computer science
control theory
systems
Control theory. Systems
Data mining
Data processing. List processing. Character string processing
Delta modulation
Diseases
Exact sciences and technology
Genetic algorithms
Marketing and sales
Medical diagnostic imaging
Memory organisation. Data processing
Meteorology
Software
Supervised learning
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Summary:Data mining is a constantly growing area. More and more domains of the daily life take advantage of the available tools (medicine, trade, meteorology, ...). However, such tools are confronted to a particular problem: the great number of characteristics that qualify data samples. They are more or less victims of the abundance of information. Sat domain benefits from the appearance of powerful solvers that can process huge amounts of data in short times. This paper proposes to solve supervised learning problems expressed as Sat ones. This is done to take advantage of an existing environment that allows experimenting different heuristics, such as: tabu search, genetic algorithm, ant colonies, etc., in order to extract solutions that satisfy a maximum number of clauses (Max-Sat problem). Finally, the best solutions are back-translated into rules that are applied to the data sets in order to verify that they really satisfy a maximum number of instances in the original learning problem.
ISSN:1062-922X
2577-1655
DOI:10.1109/ICSMC.2004.1400824