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Knowledge Discovery And Data Mining

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CHAPTER 1 INTRODUCTION
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1.1 Background
Knowledge Discovery and Data Mining are rapidly evolving areas of research that are at intersection of multiple application areas and approaches. Today no field either it belongs to computer or not, knowledge discovery is required. The loss prediction, cost estimation, identification of market moves are the common application areas where knowledge discovery is essential. Knowledge discovery is not an individual process, instead it is the combination of various session data operations that are applied in a series to extract some valuable information from dataset. Data Mining is the core part of Knowledge Discovery Process. Data Mining is the intelligent
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The tools used different data mining technique and algorithm. Example of such data mining tools are SPSS, Clementine, SAS E-Miner, Salford CART etc., (commercial software) and YALE, WEKA etc. (free software).
1.1.2 Main reason for growth of Data Mining Research
The amount of digital data has been exploding during the past decade, while the number of scientists, engineers and analysts available to analyze the data has been static. To bridge this gap requires the solutions of fundamentally new research problems, which can be grouped into the following broad challenges: (a) developing algorithms and system to mine large, massive and high dimensional datasets, (b) developing algorithms and system to mine new types of data, (c) developing algorithms, protocols and other infrastructure to mine distributed data, (d) improving the ease of use of data mining systems, and (e) developing appropriate privacy and security models for data mining. In order to respond to these challenges, we require applied, multidisciplinary and interdisciplinary research in data mining and knowledge discovery (Soman et al., 2008).
1.1.3 Introduction to Data Mining
Data Mining is considered as the effective knowledge process to perform the data value analysis so that effective data discovery over the application data can be performed. These kind of da mining operations are defined a low level
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