Abstract
Data Mining is now a common method for mining data from databases and finding out patterns from the data. Today many organizations are using data mining techniques. In this paper concepts and techniques such as Neural Network, Decision Tree, Clustering, Association Rule, Clustering and many more techniques of Data Mining is reviewed. This paper focuses how different techniques of Data Mining are used in different applications for finding out patterns from the data taken from the data base.
Highlights
Data mining involves the use of sophisticated data analysis tools to discover previously unknown, valid patterns and relationships in large data sets
E-business technology: The persons whose profile suggest that they are likely to provide a highlifetime value to the firm will be provided opportunities that will differ from those that are offered to consumers with less attractive profiles
It was examined issues related to social policy that arises as the result of convergent developments in e-business technology and corporate marketing strategies [4]
Summary
Data mining involves the use of sophisticated data analysis tools to discover previously unknown, valid patterns and relationships in large data sets. These tools can include statistical models, mathematical algorithms, and machine learning methods such as neural networks or decision trees. The term KDD (Knowledge Discovery in Databases) refers to the overall process of discovering useful knowledge from data, where data mining is a particular step in this process [2, 3]. The steps in the KDD process, such as data preparation, data selection, data cleaning, and proper interpretation of the results of the data mining process, ensure that useful knowledge is derived from the data. Data mining is an extension of traditional data analysis and statistical approaches as it incorporates analytical techniques drawn from various disciplines like AI, machine learning, OLAP, data visualization, etc
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