| Summary: | In the last two decades, we have witnessed an explosive growth in our capabilities to both collect and store data, and generate even more data by further computer processing. In fact, it is estimated that the amount of information in the world doubles every 20 months. Our inability to interpret and digest these data, as readily as they are accumulated, has created a need for a new generation of tools and techniques for automated and intelligent database analysis. Consequently, the discipline of knowledge discovery in databases (KDD), which deals with the study of such tools and techniques, has evolved into an important and active area of research because of theoretical challenges and practical applications associated with the problem of discovering (or extracting) valuable, interesting and previously unknown knowledge from very large real-world databases. Many aspects of KDD have been investigated in several related fields such as database systems, machine learning, intelligent information systems, statistics, and expert systems. In the first part of our study (Part I), we discuss the fundamental issues of KDD as well as its process oriented view with a special emphasis on modelling association rules. In the second part (Part II), a follow-up study of this article, association queries will be modelled by formal concept analysis.
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