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International Journal of Advanced Research in Computer and Communication Engineering A monthly Peer-reviewed & Refereed journal
ISSN Online 2278-1021ISSN Print 2319-5940Since 2012
IJARCCE adheres to the suggestive parameters outlined by the University Grants Commission (UGC) for peer-reviewed journals, upholding high standards of research quality, ethical publishing, and academic excellence.
← Back to VOLUME 5, ISSUE 6, JUNE 2016

Review on Technical and Non Technical Education using Regression Technique

Jyoti Parashar, Kamal Kumar Ranga

DOI: 10.17148/IJARCCE.2016.5669

Abstract: Data mining, the extraction of hidden predictive information from large databases, is a powerful new technology with great potential to help companies focus on the most important information in their data warehouses. Data mining tools predict future trends and behaviors, allowing businesses to make proactive, knowledge-driven decisions. The automated, prospective analyses offered by data mining move beyond the analyses of past events provided by retrospective tools typical of decision support systems. Data mining tools can answer business questions that traditionally were too time consuming to resolve. They scour databases for hidden patterns, finding predictive information that experts may miss because it lies outside their expectations. Most companies already collect and refine massive quantities of data. Data mining techniques can be implemented rapidly on existing software and hardware platforms to enhance the value of existing information resources, and can be integrated with new products and systems as they are brought on-line. When implemented on high performance client/server or parallel processing computers, data mining tools can analyze massive databases to deliver answers to questions such as, "Which clients are most likely to respond to my next promotional mailing, and why?" This white paper provides an introduction to the basic technologies of data mining. Examples of profitable applications illustrate its relevance to today�s business environment as well as a basic description of how data warehouse architectures can evolve to deliver the value of data mining to end users.



Keywords: Data mining, MIS, Statistical tests, Regression technique.

How to Cite:

[1] Jyoti Parashar, Kamal Kumar Ranga, “Review on Technical and Non Technical Education using Regression Technique,” International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), DOI: 10.17148/IJARCCE.2016.5669