Abstract: Large numbers of data are generated everyday in many organizations. To extract hidden predictive information from large volumes of data, data mining (DM) techniques are needed. Organizations are starting to realize the importance of data mining in their strategic planning and successful application of DM techniques can be an enormous payoff for the organizations. This paper discusses the requirements and challenges of DM, and describes major DM techniques such as statistics, artificial intelligence, decision tree approach, genetic algorithm, and visualization. DM is the search for valuable information in large volumes of data. It is the process of nontrivial extraction of implicit, previously unknown and potentially useful information such as knowledge rules, constraints, and regularities from data stored in repositories using pattern recognition technologies as well as statistical and mathematical techniques. Many companies have recognized DM as an important technique that will have an impact on the performance of the companies.
Keywords: Statistics, Machine Learning, Genetic Algorithms.
Title: A Study of Predictive Data Mining Techniques
Author: M.K.Saranya, R.Rathnavathy, Dr.G.N.K.Suresh Babu
International Journal of Computer Science and Information Technology Research
ISSN 2348-120X (online), ISSN 2348-1196 (print)
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