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From Data Mining to Knowledge Discovery in Databases

Hence the spectrum from simple natural language

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Unformatted text preview: learning, other AI fields can potentially contribute significantly to various aspects of the KDD process. We mention a few examples of these areas here: Natural language presents significant opportunities for mining in free-form text, especially for automated annotation and indexing prior to classification of text corpora. Limited parsing capabilities can help substantially in the task of deciding what an article refers to. Hence, the spectrum from simple natural language processing all the way to language understanding can help substantially. Also, natural language processing can contribute significantly as an effective interface for stating hints to mining algorithms and visualizing and explaining knowledge derived by a KDD system. P lanning considers a complicated data analysis process. It involves conducting complicated data-access and data-transformation operations; applying preprocessing routines; and, in some cases, paying attention to resource and data-access constraints. Typically, data processing steps are expressed in terms of desired postconditions and preconditions for the application of certain routines, which lends itself easily to representation as a planning problem. In addition, planning ability can play an important role in automated agents (see next item) to collect data samples or conduct a search to obtain needed data sets. Intelligent agents can be fired off to collect necessary information from a variety of Articles sources. In addition, information agents can be activated remotely over the network or can trigger on the occurrence of a certain event and start an analysis operation. Finally, agents can help navigate and model the World-Wide Web (Etzioni 1996), another area growing in importance. Uncertainty in AI includes issues for managing uncertainty, proper inference mechanisms in the presence of uncertainty, and the reasoning about causality, all fundamental to KDD theory and practice. In fact, the KDD-96 conference had a joint session with the UAI-96 conference this year (Horvitz and Jensen 1996). Knowledge representation includes o ntologies, new concepts for representing, storing, and accessing knowledge. Also included are schemes for representing knowledge and allowing the use of prior human knowledge about the underlying process by the KDD system. These potential contributions of AI are but a sampling; many others, including humancomputer interaction, knowledge-acquisition techniques, and the study of mechanisms for reasoning, have the opportunity to contribute to KDD. In conclusion, we presented some definitions of basic notions in the KDD field. Our primary aim was to clarify the relation between knowledge discovery and data mining. We provided an overview of the KDD process and basic data-mining methods. Given the broad spectrum of data-mining methods and algorithms, our overview is inevitably limited in scope: There are many data-mining techniques, particularly specialized methods for particular types of data and domain. Although various algorithms and applications might appear quite different on the surface, it is not unc...
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