Data Mining
Data Mining
Abdullah Alshawdhabi
Coleman University
Simply stated data mining refers to extracting or mining knowledge from large amounts of it. The term is actually a misnomer. Remember that the mining of gold from rocks or sand is referred to as gold mining rather than rock or sand mining. Thus, data mining should have been more appropriately named “knowledge mining from data,” which is unfortunately somewhat long. Knowledge mining, a shorter term, may not reflect the emphasis on mining from large amounts of data. Nevertheless, mining is a vivid term characterizing the process that finds a small set of precious nuggets from a great deal of raw material Thus, such a misnomer that carries both “data” and mining became a popular choice. Many other terms carry a similar or slightly different meaning to data mining, such as knowledge mining from data, knowledge extraction, data pattern analysis, data archaeology, and data dredging The definition above refers to observational data, as opposed to experimental data. Data mining typically deals with data that have already been collected for some purpose other than the data mining analysis (for example, they may have been collected in order to maintain an up-to-date record of all the transactions in a bank). This means that the objectives of the data mining exercise play no role in the data collection strategy. This is one way in which data mining differs from much of statistics, in which data are often collected by using efficient strategies to answer specific questions. For this reason, data mining is often referred to as secondary data analysis. The definition also mentions that the data sets examined in data mining are often large. If only small data sets were involved, we would merely discussing classical exploratory data analysis as practiced by statisticians. When we are faced with large bodies
Abdullah Alshawdhabi
Coleman University
Simply stated data mining refers to extracting or mining knowledge from large amounts of it. The term is actually a misnomer. Remember that the mining of gold from rocks or sand is referred to as gold mining rather than rock or sand mining. Thus, data mining should have been more appropriately named “knowledge mining from data,” which is unfortunately somewhat long. Knowledge mining, a shorter term, may not reflect the emphasis on mining from large amounts of data. Nevertheless, mining is a vivid term characterizing the process that finds a small set of precious nuggets from a great deal of raw material Thus, such a misnomer that carries both “data” and mining became a popular choice. Many other terms carry a similar or slightly different meaning to data mining, such as knowledge mining from data, knowledge extraction, data pattern analysis, data archaeology, and data dredging The definition above refers to observational data, as opposed to experimental data. Data mining typically deals with data that have already been collected for some purpose other than the data mining analysis (for example, they may have been collected in order to maintain an up-to-date record of all the transactions in a bank). This means that the objectives of the data mining exercise play no role in the data collection strategy. This is one way in which data mining differs from much of statistics, in which data are often collected by using efficient strategies to answer specific questions. For this reason, data mining is often referred to as secondary data analysis. The definition also mentions that the data sets examined in data mining are often large. If only small data sets were involved, we would merely discussing classical exploratory data analysis as practiced by statisticians. When we are faced with large bodies
References: Madison WI, June 2002, Muthukrishnan and V. Shkapenyuk. Publisher Mining database structure; or how to build data quality browser pages 240–251 John Wiley & Sons,2003, Publisher Exploratory Data Mining and Data Cleaning M. Berry and G. Linoff,1997.Publisher Data Mining Techniques, John Wiley João Moura Pires 2013, Publisher -Relational and Dimensional