This paper presents a first step towards a unifying framework for Knowledge Discovery in Databases. We describe links between data mining, knowledge discovery, and other related fields. We then define the KDD process and basic data mining algorithms, discuss application issues and conclude with an analysis of challenges facing practitioners in the field. keywords: Knowledge Discovery in Databases (KDD), Data mining, overview article, large databases, automated analysis, issues and challenges in data mining. To appear: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD-96), Portland, Oregon, August 2-4, 1996, AAAI Press. http://wwwaig. jpl.nasa.gov/kdd96 Knowledge Discovery and Data Mining: Towards a Unifying Framework Usama Fayyad Microsoft Research One Microsoft Way Redmond, WA 98052, USA fayyad@microsoft.com Gregory Piatetsky-Shapiro GTE Laboratories, MS 44 Waltham, MA 02154, USA gps@gte.com Padhraic Smyth Information and Computer S...

Knowledge discovery in databases is a rapidly growing field, whose development is driven by strong research interests as well as urgent practical, social, and economical needs. While the last few years knowledge discovery tools have been used mainly in research environments, sophisticated software products are now rapidly emerging. In this paper, we provide an overview of common knowledge discovery tasks and approaches to solve these tasks. We propose a feature classification scheme that can be used to study knowledge and data mining software. This scheme is based on the software's general characteristics, database connectivity, and data mining characteristics. We then apply our feature classification scheme to investigate 43 software products, which are either research prototypes or commercially available. Finally, we specify features that we consider important for knowledge discovery software to possess in order to accommodate its users effectively, as well as issues that are either ...

Intelligent data analysis (IDA) is an interdisci-plinary study concerned with the effective analysis of data. This paper will briefly look at some of the key issues in intelligent data analysis, discuss the opportu-nities for soft computing in this context, and present.several IDA case studies in which soft computing has played key roles. These studies are all concerned with complex real-world problem solving, including consis-tency checking between mass spectral data with pro-posed chemical structures, screening for glaucoma and other eye diseases, forecasting of visual field deteriora-tion, and diagnosis in an oil refinery involving multi-variate time series. Bayesian networks, evolutionary computation, neural networks, and machine learning in general are some of those soft computing techniques effectively used in these studies. 1.

. In this paper we discuss the efficient implementation of the STRIP (STrong Rule Induction in Parallel) algorithm in parallel using a transputer network. Strong rules are rules that are almost always correct. We show that STRIP is well suited for parallel implementation with scope for parallelism existing at four different levels of the algorithm. We present a performance study analysing the best topologies for the transputer network using different number of transputers. The choice of certain variables (the number and size of samples) in the STRIP algorithm affects the performance (speedup and efficiency) of the implementation. 1. Introduction Since 1970 when Codd introduced the relational model for databases [7], the database industry has matured a great deal and applications that were never envisaged earlier have become possible. Furthermore, heterogeneous data collections, perhaps distributed and multi-media, can now be integrated and used globally [6]. Despite these and other adv...