International Journal of Innovative Research and Development

Abstract

With the development and penetration of data mining within different fields and industries, many data mining algorithms have emerged. The selection of a good data mining algorithm to obtain the best result on a particular data set has become very important. What works well for a particular data set may not work well on another. The requirements for data mining systems for large organisation and enterprises range from logical and physical distribution of large data and heterogeneous computational resources to the general need for high performance at a level that is sufficient for interactive work. Data mining has many advantages across different industries. It allows large historical data to be used as the background for prediction. The interpretation and evaluation of the patterns obtained by data mining produces new knowledge that decision-makers can act upon. Data mining provides a means to obtain information that can support decision making and predict new business opportunities. For example, telecommunications, stock exchanges, and credit card and insurance companies use data mining to detect fraudulent use of their services; the medical industry uses data mining to predict the effectiveness of surgical procedures, medical tests, and medications; and retailers use data mining to assess the effectiveness of coupons and special events.

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