Data mining is a new and rapidly growing field. It draws

Abstract :Data mining is a new and rapidly growing field. It draws ideas and resources frommultiple disciplines, including machine learning, statistics, database research, high performancecomputing and commerce. This explains the dynamic, multifaceted and rapidly evolving nature of thedata mining discipline. While there is a broad consensus that the abstract goal of data mining is todiscover new and useful information in data bases this is where the consensus ends and the means ofachieving this goal are as diver seas the communities contributing. The foundations of all data miningmethods, however, are in mathematics. Any moderately sized treatment of data mining techniquesnecessarily has to be selective and maybe biased towards a particular approach. Data miningtechniques are used to find patterns, structure or regularities and singularities in large and growingdata sets.Artificial neural network ANN are gross simplification of real networks of neurons . Theparadigm of neural network which began during the 1940’s promises to be a very important tool forstudying the structure-function relationship of human brain. Due to the complexity and incompleteunderstanding of biological neurons. Various architecture of artificial neural network have beenreported in the literature. The aim neural network is to mimic the human ability to adopt to changingin circumstances and the current environment.In this paper I will Discuss about Neural networks are useful for data mining and decision-supportapplications.Keyword :ANN, AI, Data mining , Data Models, ,Pattern .I. INTRODUCTIONData mining is proving to be a great tool for exploring new avenues to automatically examine,visualize, and uncover patterns in data that facilitate the decision-making process.data miningidentifies trends within data that go beyond simple analysis. Modern data mining techniques(association rules, decision trees, Gaussian mixture models, regression algorithms, neural networks,support vector machines, Bayesian networks, etc.) are used in many domains to solve association,classification, segmentation, diagnosis and prediction problems.A artificial neural network isdeveloped with a systematic step-by-step procedure which optimizes a criterion commonly known asthe learning rule. The input/output training data is fundamental for these networks as it conveys theinformation which is necessary to discover the optimal operating point. In addition, a non linearnature make neural network processing elements a very flexible system. One representative definitionis pivoted around the comparison of intelligence of computing machines with human beings . Anotherdefinition is concerned with the performance of machines which is historically have been judged tolie within the domain of intelligence.

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