IT Landscape - Data Mining - Literature review Example

IT Landscape: Data Mining By Author An Introduction to Data Mining Data mining is a process of extracting hidden or covered analytical information from massive collection of data or databases. Basically, it is an amazing ground-breaking technology with the wide-ranging power to facilitate every kind of business or industry to put their attention on most significant data and information in their data warehouses framework. In this scenario, the tools and techniques encompassed by this innovative technology can forecast future tendencies and activities and as a result allowing the firm to make realistic, knowledge-motivated assessments. In addition, the standard and potential overview presented by data mining is to some extent dissimilar from the study of historical data and information presented by the tools for instance decision support systems (DSS) (where a decision support system is an information system that is used to support the business decision). Additionally, the data mining methods and techniques are helpful for the organizations in taking actions against business queries that usually were prolonged to determine. In this way, they polish databases for indefinite models, discovering analytical information that professionals can overlook for the reason that it resides external to their prospects (Berson et al., 2000; Chen & Hu, 2005; Thearling, 2009). (Seifert, 2004) defines the data mining as a process which involves making use of stylish data manipulation and analysis techniques and tools to detect formerly unidentified, relationships and valid patterns in huge data sets. Additionally, these data analysis techniques and tools can include some of the statistical models, mathematical algorithms and machine learning techniques. In fact, the basic purpose of these algorithms is to improve the decision making performance automatically through experience. Some of the well-known examples of this technology are decision trees and neural networks. In short, data mining is set of a wide variety of processes, for instance collecting, organizing, and applying predictions and analysis on the collected data (Seifert, 2004; Piton et al., 2009; Turban et al., 2005, p.462). History of Data Mining The histiory of data mining can be traced back alongside three arears such as classical statistics, artificial intelligence and machine learning. In fact without using statistics, there could be no concept of data mining, for the reason that statistics are the base of the majority of technologies on which data mining is established. Without doubt, within the heart of these days’ data mining tools and techniques, statistical techniques play a major role. The second longest family line of data mining is artificial intelligence (or simply AI). In view of the fact that this method needs enormous computer processing control, which was not realistic until the beginning of 1980s, when computers started offering practical potential at affordable expenses. In this scenario, AI played an important function in development of some of the useful applications at the very high end scientific/government markets; however there was a need for using supercomputers of the time (Data-Mining-Software, 2012; Kashner, 2003). The third family line of data mining is machine learning, which allows the computer programs to gain knowledge of data they read or receive from the users, in this scenario programs are used to make a variety of decisions foundational upon the qualities of the premeditated data, making use of the statistics for basic ideas, and integrating additional complex AI heuristics and algorithms to carry out desired tasks. In fact, the data mining, in a lot of forms, can be the edition of machine learning techniques to business applications. In this scnario, the data mining can be described as the combination of past and fresh developments in statistics, AI, and machine learning (Data-Mining-Software, 2012; Kashner, 2003). Examples/Applications/Uses/Advantages of Data Mining At the present data mining is used in every kind of organization in different form. (Christen, 2005) discuss a variety of applications of data mining. In this scenario, the first example that (Christen, 2005) discuss is of telecommunication sector where a large amount of data is collected daily. This data can be in form of: Transactional data (about each phone call Data on mobile phones, house based phones, Internet, etc. Other customer data (billing, personal information, etc. Additional data (network load, faults, etc. The use of data mining can help the organization get answer of the following questions: (Christen, 2005) Which customer group is highly profitable, which one is not? To which customers should we advertise what kind of special offers? What kind of call rates would increase profit without losing good customers? How do customer profiles change over time? Fraud detection (stolen mobile phones or phone cards) Data mining can be used in healthcare organizations where a large amount of data can be collected to perform different operations of data mining. This data can include the patients’ information such as their reports and personal information. After apllying the data mining technique organizations could be able to suggest the treatment, level of diseases. Thus, data mining can provide numerous advantages (Christen, 2005). According to Keating (2008), “the data mining is a technique to achieve market intelligence from a vast amount of data customer data”. The techniques of data mining resolve the problems of learning from business data because lack of data is not a huge problem nowadays (Keating, 2008). Keating (2008) outline that association rules based data discovery is one of the main techniques used by the data mining in the web based advertisement and marketing. In association rules based discovery, the data mining tools find out relations (for example relationships like what type of books definite groups of people study, what goods specific groups of people buy, what movies specific groups of people watch, etc) among different data items. In addition, a corporation makes use of this extracted information in targeting their customers and potential marketplaces. In this scenario, there is an example of data mining tool like Netflix that recommends the movies based on movies people have viewed as well as rated in the long-ago. Furthermore, the web based selling king “Amazon” also offers similar capability in case of recommending books through viewing the customer’s buying and viewing behaviors (Keating, 2008). The Data Mining Process Data mining is an incremental and iterative approach that is normally based on the following stages: (IBM, 2009; Oracle, 2012). Fig1 outlines the process of data mining along with the steps that are applied in the sequence: Figure 1data Mining Process, Image Source: http://publib.boulder.ibm.com/infocenter/db2luw/v8/index.jsp?topic=/com.ibm.im.easy.doc/c_dm_process.html Problem definition A data mining project is started with understanding the business problem for which data mining approach has to be applied. In this scenario, data mining professionals, business executives, and technology experts work directly together to describe the project goals and the necessities from a business viewpoint. After that the project goals and requirements are converted into a data mining problem definition (IBM, 2009; Oracle, 2012). Data exploration At this stage, specific business domain experts start understanding the meaning of the metadata. In this scenario, they bring together, illustrate, and discover the data. Moreover, at the data exploration stage, traditional data analysis tools, for instance, statistics, are utilized to discover the data (IBM, 2009; Oracle, 2012). Data preparation At this stage, business domain professionals actually start building the data model for the modeling process. At this stage, they gather, clean, and set-up the data for the reason that some of the mining operations understand data simply in a specific format. However, the meaning if the data should not be changed (IBM, 2009; Oracle, 2012). Modeling At this stage, business data mining professionals decide on and put into practice a variety of mining operations for the reason that we can apply different mining operations for the different data mining problems. However, some of the data mining operations require specific data types. In addition, the data mining professionals need to determine and evaluate each model they are going to use or reject (IBM, 2009; Oracle, 2012). Evaluation At this stage, data mining professionals assess the selected model. However, if the model does not work according to their requirements, they return to the previous stage and reconstruct the model by changing its parameters until optimal values are achieved. At the end of the evaluation phase, the data mining experts decide how to use the data mining results (IBM, 2009; Oracle, 2012). Deployment At this stage, data mining professionals make use of the mining results by transferring the results into database tables or into other applications, such as, MS Excel Worksheet, which can be used for evaluation (IBM, 2009; Oracle, 2012). References Berson, A., Smith, S. & Thearling, K., 2000. Building Data Mining Applications for CRM. McGraw-Hill. Chen, Y. & Hu, L., 2005. Study on data mining application in CRM system based on insurance trade. In ACM International Conference Proceeding Series; Vol. 113, Proceedings of the 7th international conference on Electronic commerce. Xi'an, China , 2005. ACM New York, USA. Christen, P., 2005. A very short introduction to Data Mining. [Online] Available at: http://datamining.anu.edu.au/talks/2005/datamining-comp2340-2005.pdf [Accessed 29 March 2012]. Data-Mining-Software, 2012. A Brief History of Data Mining. [Online] Available at: http://www.data-mining-software.com/data_mining_history.htm [Accessed 29 March 2012]. IBM, 2009. The data mining process. [Online] Available at: http://publib.boulder.ibm.com/infocenter/db2luw/v8/index.jsp?topic=/com.ibm.im.easy.doc/c_dm_process.html [Accessed 26 March 2012]. Kashner, Z., 2003. A Data Mining Primer for the Data Warehouse Professional Business Intelligence. Business Intelligence Journal Spring, pp.44-54. Oracle, 2012. Data Mining Process. [Online] Available at: http://docs.oracle.com/html/B14339_01/5dmtasks.htm [Accessed 28 March 2012]. Piton, T., Blanchard, J., Briand, H. & Guillet, F., 2009. Domain driven data mining to improve promotional campaign ROI and select marketing channels. In Conference on Information and Knowledge Management, Proceeding of the 18th ACM conference on Information and knowledge management. Hong Kong, China, 2009. ACM New York, USA. Seifert, J.W., 2004. CRS Report for Congress: Data Mining: An Overview. Congressional Research Service ˜ The Library of Congress, pp.1-19. Thearling, K., 2009. An Introduction to Data Mining. [Online] Available at: http://www.thearling.com/text/dmwhite/dmwhite.htm [Accessed 24 March 2012]. Turban, E., Leidner, D., McLean, E. & Wetherbe, J., 2005. Information Technology for Management: Transforming Organizations in the Digital Economy. 4th ed. New York: Wiley. Read More