ng refers to the step in which the data mining algorithms are applied. This has created a fair amount of confusion in the literature. But more often the term is used to refer the entire process of finding and using interesting patterns in data (BenoГ®t, 2002). application of data mining techniques was first applied to databases. A better term for this process is KDD (Knowledge Discovery in Databases). BenoГ®t (2002) offers this definition of KDD (which he refers to as data mining): mining (DM) is a multistaged process of extracting previously unanticipated knowledge from large databases, and applying the results to decision making. Data mining tools detect patterns from the data and infer associations and rules from them. The extracted information may then be applied to prediction or classification models by identifying relations within the data records or between databases. Those patterns and rules can then guide decision making and forecast the effects of those decisions.
Today, data mining usually refers to the process broadly described by BenoГ®t (2002) but without the restriction to databases. It is a multidisciplinary field drawing work from areas including database technology, artificial intelligence, machine learning, neural networks, statistics, pattern recognition, knowledge-based systems, knowledge acquisition, information retrieval, high-performance computing and data visualization. (Han & Kamber, 2001, p. Xix). Mining techniques can be applied to a wide variety of data repositories including databases, data warehouses, spatial data, multimedia data, Internet or Web-based data and complex objects. A more appropriate term for describing the entire process would be knowledge discovery, but unfortunately the term data mining is what has caught on (AndrГЎssoyГЎ & Parali?, 1999).
2. Developmental History of Data Mining and Knowledge Discovery
The building blocks of today s data mining techniques date back to the 1950s when the work of mathematicians, logicians, and computer scientists combined to create artificial intelligence (AI) and machine learning (Buchanan, 2006.). the 1960s, AI and statistics practitioners developed new algorithms such as regression analysis, maximum likelihood estimates, neural networks, bias reduction, and linear models of classification (Dunham, 2003, p. 13). The term data mining was coined during this decade, but the term was pejoratively used to describe the practice of wading through data and finding patterns that had no statistical significance (Fayyad, et al., 1996, p. 40). 5in the 1960s, the field of information retrieval (IR) made its contribution in the form of clustering techniques and similarity measures. At the time these techniques were applied to text documents, but they would later be utilized when mining data in databases and other large, distributed data sets (Dunham, 2003, p. 13). Database systems focus on query and transaction processing of structured data, whereas information retrieval is concerned with the organization and retrieval of information from a large number of text-based documents (Han & Kamber, 2001, p. 428). By the end of the 1960s, information retrieval and database systems were developing in parallel.1971, Gerard Salton published his groundbreaking work on the SMART Information Retrieval System. This represented a new approach to information retrieval which utilized the algebra-based vector space model (VSM). VSM models would prove to be a key ingredient in the data mining toolkit (Dunham, 2003, p. 13). The 1970s, 1980s, and 1990s, the confluence of disciplines (AI, IR, statistics, and database systems) plus the availability of fast microcomputers opened up a world of possibilities for retrieving and analyzing data. During this time new programming languages ​​were developed and new computing techniques were developed including genetic algorithms, EM algorithms, K-Means clustering, and decision tree algorithms (Dunham, 2003, p. 13). The start of the 1990s, the term Knowledge Discovery in Databases (KDD) had been coined and the first KDD workshop held (Fayyad, Piatetsky-Shapiro, & Smyth, 1996, p. 40). The huge volume of data available created the need for new techniques for handling massive quantities of information, much of which was located in huge databases.1990s saw the development of database warehouses, a term used to describe a large database (composed of a single schema ), created from the consolidation of operational and transactional database data. Along with the development of data warehouses came online analyt...
