INTERNATIONAL RESEARCH JOURNAL OF SCIENCE ENGINEERING AND TECHNOLOGY

( Online- ISSN 2454 -3195 ) New DOI : 10.32804/RJSET

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APPLICATION OF DATA MINING IN THE BUSINESS ENVIRONMENT

    1 Author(s):  TANMAYA KUMAR PATTNAIK

Vol -  1, Issue- 1 ,         Page(s) : 25 - 34  (2011 ) DOI : https://doi.org/10.32804/RJSET

Abstract

Over the past years, data mining became a matter of considerable importance due to the large amounts of data available in the applications belonging to various domains. Data mining, a dynamic and fast-expanding field, that applies advanced data analysis techniques from statistics, machine learning, database systems or artificial intelligence, in order to discover relevant patterns, trends and relations contained within the data, information impossible to observe using other techniques. The paper focuses on presenting the applications of data mining in the business environment. It contains a general overview of data mining, providing a definition of the concept, enumerating six primary data mining techniques and mentioning the main fields for which data mining can be applied. The paper also presents the main business areas which can benefit from the use of data mining tools, along with their use cases: retail, banking and insurance. Also the main commercially available data mining tools and their key features are presented within the paper. Besides the analysis of data mining and the business areas that can successfully apply it, the paper presents the main features of a data mining solution that can be applied for the business environment and the architecture, with its main components, for the solution, that would help improve customer experiences and decision-making.

  1. Vikas Jayasree and Rethnamoney Vijayalakshmi Siva Balan, A Review on Data Mining in Banking Sector, American Journal of Applied Sciences, Vol. 10, Issue 10, 2013, ISSN 1554- 3641, pp. 1160-1165.
  2. A. B. Devale and Dr. R. V. Kulkarni, Applications of data mining techniques in life insurance, International Journal of Data Mining & Knowledge Management Process, Vol.2, Issue 4, July 2012, ISSN 2230-9608, pp. 31-40.

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