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Predictive Data mining and discovering hidden values of Data warehouse

Author(s): Mehta Neel B

Journal: ARPN Journal of Systems and Software
ISSN 2222-9833

Volume: 1;
Issue: 1;
Start page: 1;
Date: 2011;
Original page

Keywords: Predictive Data mining | Data Warehousing | Decision tree learning | OLAP

Data Mining is an analytic process to explore data (usually large amounts of data - typically business or market related) in search of consistent patterns and/or systematic relationships between variables, and then to validate the findings by applying the detected patterns to new sets of data. The main target of data mining application is prediction. Predictive data mining is important and it has the most direct business applications in world. The paper briefly explains the process of data mining which consists of three stages: (1) the Initial exploration, (2) Pattern identification with validation, and (3) Deployment (application of the model to new data in order to generate predictions). Data Mining is being done for Patterns and Relationships recognitions in Data analysis, with an emphasis on large Observational data bases. From a statistical perspective Data Mining is viewed as computer automated exploratory data analytical system for large sets of data and it has huge Research challenges in India and abroad as well. Machine learning methods form the core of Data Mining and Decision tree learning. Data mining work is integrated within an existing user environment, including the works that already make use of data warehousing and Online Analytical Processing (OLAP). The paper describes how data mining tools predict future trends and behaviour which allows in making proactive knowledge-driven decisions.
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