CFP

Special Issue of IEEE Transactions on Evolutionary Computation on Data Mining and Knowledge Discovery with Evolutionary Algorithms

Data mining (DM) consists of extracting interesting knowledge from real-world, large & complex data sets; and is the core step of a broader process, called the knowledge discovery from databases (KDD) process. In addition to the DM step, which actually extracts knowledge from data, the KDD process includes several preprocessing (or data preparation) and post-processing (or knowledge refinement) steps. The goal of data preprocessing methods is to transform the data to facilitate the application of a (or several) given DM algorithm(s), whereas the goal of knowledge refinement methods is to validate and refine discovered knowledge. Ideally, discovered knowledge should be not only accurate, but also comprehensible and interesting for the user. The total process is highly computation intensive.

The idea of automatically discovering knowledge from databases is a very attractive and challenging task, both for academia and for industry. Hence, there has been a growing interest in data mining in several AI-related areas, including evolutionary algorithms (EAs). The main motivation for applying EAs to KDD tasks is that they are robust and adaptive search methods, which perform a global search in the space of candidate solutions (for instance, rules or another form of knowledge representation). Intuitively, the global search performed by EAs can more effectively discover interesting patterns that would have been missed by the greedy search performed by many KDD methods.

The EA community has been publishing KDD-related articles in a relatively scattered manner in journals dedicated to knowledge discovery and data mining or evolutionary computing. The objective of this issue is to assemble a set of high-quality original contributions that reflect and advance the state-of-the-art in the area of Data Mining and Knowledge Discovery with Evolutionary Algorithms. The special issue will emphasize the utility of different evolutionary computing tools to various facets of KDD, ranging from theoretical analysis to real-life applications.

Manuscripts should be prepared as per the format of the journal available at its web site (http://www.ewh.ieee.org/tc/nnc/pubs/tec/). Submission should be made to the guest editors (electronic submissions in postscript or PDF are preferred) at ash@isical.ac.in or alex@ppgia.pucpr.br.

All submissions will be peer reviewed as per the norm of the IEEE Tr. on Evolutionary Computation.

Topics of interest include (but are not restricted to):

• Evolutionary algorithms (EAs) for data preprocessing (e.g., data cleaning, attribute selection, attribute construction), data mining (e.g., classification/prediction, clustering, dependence modeling, regression, extraction of comprehensible & interesting knowledge), post-processing of extracted knowledge

Papers on other topics (not listed above) related to applications of EAs to KDD process are also welcome.

Important dates: * Manuscript submission: August 31, 2002 * Notification of review reports for revision (if any): December 31, 2002 * Final version submission: February 28, 2003 * Publication of the issue: as per IEEE-TEC schedule