Freitas, Alex A. (2000) Data Mining with Evolutionary Algorithms: Research Directions. AI Magazine, 21 (1). ISSN 0738-4602. (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)
There has been a growing interest in data mining in several AI-related areas, including evolutionary algorithms. Hence, it seems that it is the right time for the communities of data mining and evolutionary algorithms to meet and exchange ideas. The general goal of the workshop was be to discuss promising and necessary research directions in data mining with evolutionary algorithms. Topics included evolutionary algorithms (EA) for classification, clustering, dependence modeling, regression, time series and other data mining tasks; discovery of comprehensible, interesting knowledge with EA; scaling up EA for very large databases; parallel and/or distributed EA; comparison between EA and other data mining methods; genetic operators tailored for data mining tasks; incorporating domain knowledge in EA; integrating EA with database systems; data mining with evolutionary, intelligent agents; hybrid (neural-genetic, rule induction-genetic, etc.) EA; uncertainty handling with EA; data pre-processing with EA; post-processing of the discovered knowledge with EA; and mining semistructured or unstructured data (e.g. text mining) with EA.
|Subjects:||Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,|
|Divisions:||Faculties > Science Technology and Medical Studies > School of Computing > Applied and Interdisciplinary Informatics Group|
|Depositing User:||Mark Wheadon|
|Date Deposited:||01 Sep 2009 18:24|
|Last Modified:||20 May 2014 08:20|
|Resource URI:||https://kar.kent.ac.uk/id/eprint/22035 (The current URI for this page, for reference purposes)|