Soft computing data mining

Soft computing is a consortium of methodologies, (like fuzzy logic, neural networks, genetic algorithms, rough sets), that works synergistically and provides , in one form or another, flexible information processing capabilities for handling real life problems. Its aim is to exploit the tolerance for imprecision, uncertainty, approximate reasoning and partial truth in order to achieve tractability, robustness, low solution cost, and close resemblance with human like decision-making. The process of knowledge discovery from data bases (KDD), on the other hand, is a real life problem solving paradigm and is defined as the non-trivial process of identifying valid, novel, potentially useful and understandable patterns from large data bases, where the data is frequently ambiguous, incomplete, noisy, redundant and changes with time. Data mining is one of the fundamental steps in the KDD process and is concerned with the algorithmic means by which patterns or structures are enumerated from the data under acceptable computational efficiency. Soft computing tools, individually or in integrated manner, are turning out to be strong candidates for performing data mining tasks efficiently. At present, the results on these investigations, integrating soft computing and data mining, both theory and applications, are being available in different journals and conference proceedings mainly in the fields of computer science, information technology, engineering and mathematics. The objective of this issue is to assemble a set of high-quality original contributions that reflect the advances and the state-of-the-art in the area of Data Mining and Knowledge Discovery with Soft Computing Methodologies; thereby presenting a consolidated view to the interested researchers in the aforesaid fields, in general, and readers of the journal Information Sciences, in particular. It has ten articles. The first one is a title article. While the next four articles deal with classificatory rule analysis, the sixth one concerns with association rule mining. Utility of Self Organizing Map (SOM) in the context of text mining and clustering are discussed in seventh and eighth articles. The next contribution describes a novel multi-source data fusion strategy. The last article demonstrates an application of data mining in biological data analysis.