Volume : 10, Issue : 5, MAY 2024

AN OVERVIEW OF DATA MINING TECHNIQUES AND ITS APPLICATIONS

C.RADHA, S. PALANIMURUGAN, C.MANI, MR.B.MOHANRAJ

Abstract

Data mining is a process which finds useful patterns from large amount of data. The paper discusses few of the data mining techniques, algorithms and some of the organizations which have adapted data mining technology to improve their businesses and found excellent results. In order to determine how data mining techniques (DMT) and their applications have developed, during the past decade, this paper reviews data mining techniques. We'll explore a number of important categories of data mining techniques, such as pattern matching, data visualisation, meta-rule guided mining, generalisation, characterisation, classification, clustering, association, and evolution. We will look at knowledge mining techniques for various database types, such as relational, transactional, object-oriented, spatial, and active databases, as well as global information systems. There will also be a discussion of various research issues and possible uses for data mining. Data mining tools are specialised tools required to analyse and derive meaningful conclusions and inferences from this massive volume of data. An overview of data mining systems and some of their uses is provided in this paper.

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References

1. Jiawei Han and Micheline Kamber (2006), Data Mining Concepts and Techniques, published by Morgan Kauffman, 2nd ed.

2. A.S.Aneeshkumar, & C. J. Venkateswaran, 2015, A novel approach for Liver disorder Classification using Data Mining Techniques, Engineering and Scientific International Journal (ESIJ), 2(1): 15-18

3. Dr. Gary Parker, vol 7, 2004, Data Mining: Modules in emerging fields, CD-ROM.

4. Zhao, Kaidi and Liu, Bing, Tirpark, Thomas M. and Weimin, Xiao,”A Visual Data Mining Framework forConvenient Identification of Useful Knowledge”, ICDM'05 Proceedings of the Fifth IEEE International Conference on DataMining,vol.-1, no.-1,pp.- 530-537,Dec 2005.

5. S.  Alturki,  &  N.  Alturki,  2021,  Using  Educational  Data  Mining  to  Predict  Students’  Academic Performance  for  Applying  Early  Interventions,  Journal  of  Information  Technology  Education: Innovations in Practice, 20: 121-137.

6. H. Johan, B. Bart and V. Jan, “Using Rule Extraction to Improve the Comprehensibility of Predictive Models”.In Open Access publication from Katholieke UniversiteitLeuven, pp.1-56, 2006

7. Giovanni Vigna, Sumit Gwalani, Kavitha Srinivasan, Elizabeth M. BeldingRoyer and Richard A. Kemmerer ,” An Intrusion Detection Tool for AODVbased Ad hoc Wireless Networks”, IEEE Computer Society Washington, DC, USA ,2004.