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P-2 - Sedma Nacionalna Konferencija so Me|unarodno U~estvo...

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DATA MINING AND ITS ENVIRONMENTAL APPLICATIONS Sa ˇ so D ˇ zeroski Department of Knowledge Technologies, Jo ˇ zef Stefan Institute, Jamova 39, 1000 Ljubljana, Slovenia [email protected] Abstract - Data mining, the central activity in the process of knowledge discovery in databases (KDD), is concerned with fi nding patterns in data. This paper introduces and illustrates the most com- mon types of patterns considered by data mining approaches and gives rough outlines of the data mining algorithms that are most frequently used to look for such patterns. In this paper, we also to give an overview of KDD applications in environmental sciences, complemented with a sample of case stud- ies. The latter are described in slightly more de- tail and used to illustrate KDD-related issues that arise in environmental applications. The applica- tion domains addressed mostly concern ecological modelling. 1. INTRODUCTION Knowledge discovery in databases (KDD) was initially de fi ned as the “non-trivial extraction of implicit, pre- viously unknown, and potentially useful information from data” [14]. A revised version of this de fi nition states that “KDD is the non-trivial process of identi- fying valid, novel, potentially useful, and ultimately understandable patterns in data” [11]. According to this de fi nition, data mining (DM) is a step in the KDD process concerned with applying computational tech- niques (i.e., data mining algorithms implemented as computer programs) to actually fi nd patterns in the data. In a sense, data mining is the central step in the KDD process. The other steps in the KDD process are concerned with preparing data for data mining, as well as evaluating the discovered patterns (the results of data mining). The above de fi nitions contain very imprecise notions, such as knowledge and pattern. To make these (slightly) more precise, some explanations are neces- sary concerning data, patterns and knowledge, as well as validity, novelty, usefulness, and understandability. For example, the discovered patterns should be valid on new data with some degree of certainty (typically prescribed by the user). The patterns should poten- tially lead to some actions that are useful (according to user de fi ned utility criteria). Patterns can be treated as knowledge: according to Frawley et al. [14], “a pattern that is interesting (according to a user-imposed interest measure) and certain enough (again according to the user’s criteria) is called knowledge.” This paper will focus on data mining and will not deal with the other aspects of the KDD process (such as data preparation). Since data mining is concerned with fi nding patterns in data, the notions of most direct rele- vance here are the notions of data and patterns. An- other key notion is that of a data mining algorithm, which is applied to data to fi nd patterns valid in the data. Different data mining algorithms address differ- ent data mining tasks, i.e., have different intended use for the discovered patterns.
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