Data mining

Many approaches have already been proposed for classification and modeling in the literature. These approaches are usually based on mathematical mod­ els. Computer systems can easily handle mathematical models even when they are complicated and nonlinear (e.g., neural networks). On the other hand, it is not always easy for human users to intuitively understand mathe­ matical models even when they are simple and linear. This is because human information processing is based mainly on linguistic knowledge while com­ puter systems are designed to handle symbolic and numerical information. A large part of our daily communication is based on words. We learn from various media such as books, newspapers, magazines, TV, and the Inter­ net through words. We also communicate with others through words. While words play a central role in human information processing, linguistic models are not often used in the fields of classification and modeling. If there is no goal other than the maximization of accuracy in classification and model­ ing, mathematical models may always be preferred to linguistic models. On the other hand, linguistic models may be chosen if emphasis is placed on interpretability.

The Intelligent Data Engineering and Automated Learning (IDEAL) conf- ence series began in 1998 in Hong Kong, when the world started to experience information and data explosion and to demand for better, intelligent meth- ologies and techniques. It has since developed, enjoyed success in recent years, and become a unique annual international forum dedicated to emerging topics and technologies in intelligent data analysis and mining, knowledge discovery, automated learning and agent technology, as well as interdisciplinary appli- tions, especially bioinformatics. These techniques are common and applicable to many ?elds. The multidisciplinary nature of research nowadays is pushing the boundaries and one of the principal aims of the IDEAL conference is to p- mote interactions and collaborations between disciplines, which are bene?cial and bringing fruitful solutions. This volume of Lecture Notes in Computer Science contains accepted papers presented at IDEAL 2004, held in Exeter, UK, August 25-27, 2004. The conf- ence received 272 submissions from all over the world, which were subsequently refereed by the ProgramCommittee. Among them 124 high-quality papers were accepted and included in the proceedings. IDEAL 2004 enjoyed outstanding keynote talks by distinguished guest speakers,Jim Austin, Mark Girolami, Ross King, Lei Xu and Robert Esnouf. This year IDEAL also teamed up with three international journals, namely the International Journal of Neural Systems,the Journal of Mathematical M- elling and Algorithms,and Neural Computing & Applications. Three special issues on Bioinformatics, Learning Algorithms,and Neural Networks & Data Mining, respectively, have been scheduled for selected papers from IDEAL 2004.

The aim of this book is to gather the most recent works that address issues related to the concept of mining complex data. Actually, managing complex data within the KDD process implies to work on every step, starting from the pre-processing (e.g.