CHAPTER ONE
INTRODUCTION
1.0 Introduction
Data mining is the process of sorting through large data sets to identify patterns and establish relationships to solve problems through data analysis. Data mining tools allow enterprises to predict future trends. It is an interdisciplinary subfield of computer science and statistics with an overall goal to extract information (with intelligent methods) from a data set and transform the information into a comprehensible structure for further use. Clifton, Christopher (2010); Hastie, Trevor et al., (2009). Data mining is the analysis step of the “knowledge discovery in databases” process, or KDD.Fayyad, Usama et al., (1996). Aside from the raw analysis step, it also involves database and data management aspects, data pre-processing, model and inference considerations, interestingness metrics, complexity considerations, post-processing of discovered structures, visualization, and online updating. The difference between data analysis and data mining is that data analysis is to summarize the history such as analyzing the effectiveness of a marketing campaign, in contrast, data mining focuses on using specific machine learning and statistical models to predict the future and discover the patterns among data.Olson, D. L. (2007).
The term “data mining” is in fact a misnomer, because the goal is the extraction of patterns and knowledge from large amounts of data, not the extraction (mining) of data itself.Han, Jiawei; Kamber, Micheline (2001). It also is a buzzword and is frequently applied to any form of large-scale data or information processing (collection, extraction, warehousing, analysis, and statistics) as well as any application of computer decision support system, including artificial intelligence (e.g., machine learning) and business intelligence. The book Data mining: Practical machine learning tools and techniques with Java (which covers mostly machine learning material) was originally to be named just Practical machine learning, and the term data mining was only added for marketing reasons. Bouckaert, Remco R. et al., (2010). Often the more general terms (large scale) data analysis and analytics – or, when referring to actual methods, artificial intelligence and machine learning – are more appropriate.
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