Motivation and scope

Data Mining algorithms present some drawbacks, due to the nature of the problems they try to solve. They are very time consuming, they tend to obtain an excessive number of outputs, unmanageable for any expert (think about association rules, for instance), and they must be able to deal with imprecise, uncertain, and noisy data.

So it is necessary to develop new techniques in order to obtain good results (even though not the very best ones) in a reasonable amount of time, techniques able to properly summarize the results obtained in the data mining process, and techniques capable to manage imperfect data.

Advanced representation schemes and "Soft Computing" techniques have proved valuable when they are applied to Data Mining problems. Therefore, we encourage authors to present original papers dealing with the incorporation of such techniques into data mining algorithms and processes.

We also expect contributions from researchers and practitioners describing efficient and scalable algorithms that result in more understandable models, as well as novel exploratory techniques to sift through vast amounts of data.

Topics

Topics of interest include, but are not limited to:

• The use of fuzzy and rough sets to improve the interpretability of data mining results.
• The applicability of genetic algorithms and evolutionary computation in data mining tasks.
• Ontologies and their role in discovering complex patterns.
• The discovery of rarities, anomalies, exceptions, and other kinds of knowledge.
• Alternative techniques for the representation and exploration of data mining results.
• Novel models and techniques for summarizing data mining results.
• Methods for dealing with imprecision and uncertainty in the data mining results.

Sponsored by

In cooperation with

held in conjunction with the Sixth IEEE
International Conference on Data Mining