International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395 -0056
Volume: 04 Issue: 02 | Feb -2017
p-ISSN: 2395-0072
www.irjet.net
Data Mining and Knowledge Management Vinay Singh1, Kaushal Kumar2 1,2Research
Scholar Department of Mechanical Engineering, GJUS&T, Hisar, Haryana, India
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Abstract - Rapid increases in technological and
informational systems have led businesses to collect customer data in huge databases. Data mining is the process involving analyzing, searching data to make it useful for human use. Large amount of data is modeled, selected and explored in order to determine comprehensible information. This article represents data mining tools used to understand the data mining process. Also, data mining enablers as well as barriers are also described to make the subject more understandable. Key Words: Data mining, Data mining models, Knowledge Management, Enablers, Barriers
1. INTRODUCTION Data mining (DM) is the process of trawling through data to find previously unknown relationships among the data that are interesting to the user of the data (Hand, 1998). Data Mining has been an established field (Fayyad et al., 1996; Chen and Liu, 2005; Wang, 2005). Data mining is the process of searching and analyzing data in order to find implicit, but potentially useful, information (M.J.A. Berry et al, 1997). It involves selecting, exploring and modeling large amounts of data to uncover previously unknown patterns, and ultimately comprehensible information, from large databases (Shaw et al, 2001). Data mining uses a broad family of computational methods that include statistical analysis, decision trees, neural networks, rule induction and refinement, and graphic visualization (Brachman, 1996). Also, Data Mining techniques should be carefully understood and applied by the frontline users (Hall, 2004; Violino, 2004; King, 2005). Data mining allows a search, for valuable information, in large volumes of data. The explosive growth in databases has created a need to develop technologies that use information and knowledge intelligently (Weiss & Indurkhya, 1998). According to Rubenking (2001), "data mining is the process of automatically extracting useful information and relationships from immense quantities of data. In its purest form, data mining doesn't involve looking for specific information. Data mining is an interdisciplinary field that combines artificial intelligence, database management, data visualization, machine learning, mathematic algorithms, and statistics. Data mining, also known as knowledge discovery in databases (KDD) (Chen, Han, & Yu, 1996; Fayyad, Piatetsky-Shapiro, & Smyth, 1996a), is a rapidly emerging field. This technology provides different methodologies for decision-making, problem solving, analysis, planning, diagnosis, detection, integration, Š 2017, IRJET
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prevention, learning, and innovation. Data Mining was defined by Turban, Aronson, Liang, and Sharda (2007) as a process that uses statistical, mathematical, artificial intelligence and machine-learning techniques to extract and identify useful information and subsequently gain knowledge from large databases.
2. Data Mining and Knowledge Management Knowledge discovery and learning is an iterative process that extends the collection of data mining techniques into a knowledge management framework (Michael J. Shaw, 2001). Higher education will find larger and wider applications for data mining than its counterpart in the business sector, because higher education institutions carry three that data mining intensive duties: scientific research that relates to the creation of knowledge, teaching that concerns with the transmission of knowledge, and institutional research that pertains to the use of knowledge for decision making. All the above tasks are well within the boundaries of Knowledge Management, which drives the need for better and faster decision making tools and methods (Luan Jing, 2005). Owing to its strength, Data Mining is known as a powerful Business Intelligence tool for knowledge discovery (Chen and Liu, 2005). The process of Data Mining is a Knowledge Management process because it involves human knowledge (Brachman et al., 1996). Several authors have also written about the factors behind the dawn of data mining. For instance, Therling (1995) identified three reasons: The ease of data collection and storage, the computing power of modern processors, and the need for fast and real time data mining. Yet, one important reason absent from these is the growing interest in Knowledge Management.
3. Data Mining Tools a) Web-based software tools: To meet the competitive global challenges, the firm’s knowledge workers require improved tools for understanding the changing markets and customer requirements. Historically, forecasting tools were the primary business insight generation tools used to analyze the competitive landscape (D.N. Clark, 1992). The business objective for using these insight-generation tools was to help knowledge workers predict the future of a given market segment or the success of a particular product line. These forecasting tools aided in reducing decision uncertainty by providing a degree of confidence to those decisions related to the success of market segments or product lines (G.J. Browne et al, 1997). ISO 9001:2008 Certified Journal
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