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From Data Mining to Knowledge Discovery in Databases

From Data Mining to Knowledge Discovery in Databases -...

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Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media atten- tion of late. What is all the excitement about? This article provides an overview of this emerging field, clarifying how data mining and knowledge discovery in databases are related both to each other and to related fields, such as machine learning, statistics, and databases. The article mentions particular real-world applications, specific data-mining techniques, challenges in- volved in real-world applications of knowledge discovery, and current and future research direc- tions in the field. A cross a wide variety of fields, data are being collected and accumulated at a dramatic pace. There is an urgent need for a new generation of computational theo- ries and tools to assist humans in extracting useful information (knowledge) from the rapidly growing volumes of digital data. These theories and tools are the subject of the emerging field of knowledge discovery in databases (KDD). At an abstract level, the KDD field is con- cerned with the development of methods and techniques for making sense of data. The basic problem addressed by the KDD process is one of mapping low-level data (which are typically too voluminous to understand and digest easi- ly) into other forms that might be more com- pact (for example, a short report), more ab- stract (for example, a descriptive approximation or model of the process that generated the data), or more useful (for exam- ple, a predictive model for estimating the val- ue of future cases). At the core of the process is the application of specific data-mining meth- ods for pattern discovery and extraction. 1 This article begins by discussing the histori- cal context of KDD and data mining and their intersection with other related fields. A brief summary of recent KDD real-world applica- tions is provided. Definitions of KDD and da- ta mining are provided, and the general mul- tistep KDD process is outlined. This multistep process has the application of data-mining al- gorithms as one particular step in the process. The data-mining step is discussed in more de- tail in the context of specific data-mining al- gorithms and their application. Real-world practical application issues are also outlined. Finally, the article enumerates challenges for future research and development and in par- ticular discusses potential opportunities for AI technology in KDD systems. Why Do We Need KDD? The traditional method of turning data into knowledge relies on manual analysis and in- terpretation. For example, in the health-care industry, it is common for specialists to peri- odically analyze current trends and changes in health-care data, say, on a quarterly basis. The specialists then provide a report detailing the analysis to the sponsoring health-care or- ganization; this report becomes the basis for future decision making and planning for health-care management. In a totally differ- ent type of application, planetary geologists
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