Modern information technology makes it possible today to collect, store, transfer, and combine huge amounts of data at very low costs. Thus more and more companies, and scientific and governmental institutions build up large archives of all kinds of data like numbers, tables, documents, images, sounds, etc. Although users often have a vague understanding of their data and can usually formulate hypotheses and guess dependencies, turning these – often abundantly available – data into useful information turns out to be rather difficult. In response to these challenges a new area of research has emerged, called ‘‘knowledge discovery in databases’’ or ‘‘data mining’’, that tries to provide tools to extract valid, useful, understandable, unknown, and unexpected relationships from large databases [1]. This current form of data analysis is an interdisciplinary field, and employs methods from statistics, soft computing, artificial intelligence and machine learning. The stress lies on the development of techniques that produce human-understandable results, and are suited for large, real world datasets. The data in real world applications has in most cases characteristics that challenge classical analyzing approaches. Besides being heterogeneous – which in its simplest form can mean that we have to deal with numerical and symbolic features – , the data is often of low quality. Algorithms must thus be able to deal with uncertainty and imprecision. The characteristics of the data sources – their quantity, complexity, dimensionality and imperfection – , the essential of extracting understandable patterns from these, and the need to incorporate available background knowledge in that process, makes us assume that (neuro-) fuzzy techniques will play a considerable role in the future of data mining [3]. It is the ability of fuzzy sets to transform between computer representations and (naturally linguistic) human concepts that makes them so valuable to meet the advanced data mining demands, and that Zadeh meant, when he promoted his idea of computing with words [4]. The inherent imprecision of words is not necessarily a weakness, but, on the contrary, can be crucial to model complex systems. From our own experience we observed that many practical applications have this certain robustness where full precision is not necessary. In such cases, exaggerated precision can be a waste of resources, and solutions obtained using fuzzy approaches might be easier to understand and to apply, and gain their strengths by explicitly taking into account vagueness, imprecision or uncertainty. One prominent way to use fuzzy systems in data analysis, is to induce fuzzy if-then rules from data by neurofuzzy techniques. The use of linguistic variables eases the readability and interpretability of the rule base. If we apply such techniques, we must be aware of the trade-off between precision and interpretability. However, the results in data mining are not only judged for their accuracy, but also for their interpretability, as the ultimate goal is to extract human understandable patterns [2]. The papers in this special issue of the Soft Computing Journal deal with quite different aspects of neuro-fuzzy techniques, with a stress on data analysis and rule based systems. Although the intention of this issue cannot be to give a survey of this wide area, it shall give an insight in current trends and research topics in this field. The papers can roughly be divided into three groups:
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