A Review of Data Mining Techniques and Its Applications

Authors

  • Madhav Singh Solanki SOEIT, Sanskriti University, Mathura, Uttar Pradesh, India Author
  • Ms Anuska Sharma SOEIT, Sanskriti University, Mathura, Uttar Pradesh, India Author

Keywords:

EDM, Educational Data Mining, KDD, Knowledge Discovery in Database, LMS, Learning Management System, SNA, Social Network Analysis

Abstract

Knowledge Discovery in Databases  (KDD) is another name for data mining. It's also known  as the process of extracting interpretable, intriguing and  valuable statistics from unstructured data. There are a  variety of resources which generally produce huge  amounts of raw data. This is the primary cause for the fast  growth of data mining applications. This article discusses  data mining methods and their applications, including  scholastic data mining (SDM), life sciences, commerce,  finance, and medicine among others. We put current  methods together to see how data mining might be used  to various areas. Our classification focuses on research  that was published between 2007 and 2017. We provide a  simple and brief perspective of various models used in  data mining with this classification. 

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References

Dhiman AK. Knowledge Discovery in Databases and Libraries. DESIDOC J Libr Inf Technol. 2011; [2] Guarascio M, Manco G, Ritacco E. Knowledge discovery in databases. In: Encyclopedia of Bioinformatics and Computational Biology: ABC of Bioinformatics. 2018.

Comparative Study of K-NN, Naive Bayes and Decision Tree Classification Techniques. Int J Sci Res. 2016;

Peña-Ayala A. Educational data mining: A survey and a data mining-based analysis of recent works. Expert Systems with Applications. 2014.

Bakhshinategh B, Zaiane OR, ElAtia S, Ipperciel D. Educational data mining applications and tasks: A survey of the last 10 years. Educ Inf Technol. 2018;

Dinesh Kumar A, Pandi Selvam R, Sathesh Kumar K. Review on prediction algorithms in educational data mining. Int J Pure Appl Math. 2018;

Rangra K, Bansal KL. Comparative Study of Data Mining Tools. Int J Adv Res Comput Sci Softw Eng. 2014;

Colonna L. A Taxonomy and Classification of Data Mining. Sci Technol Law Rev. 2013;

Drumond M, Daglis A, Mirzadeh N, Ustiugov D, Picorel J, Falsafi B, et al. Algorithm/Architecture Co Design for Near-Memory Processing. ACM SIGOPS Oper Syst Rev. 2018;

Yordanova A, Yordanov M. WEB BASED SYSTEM FOR CHOOSING A STATISTICAL METHOD FOR DATA PROCESSING. Appl Res Tech Technol Educ. 2018;

Kitchen AM, Drachenberg R, Symanzik J. Assessing the reliability of web-based statistical software. Comput Stat. 2003;

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Published

2021-11-30

Issue

Vol. 9 No. 6 (2021): International Journal of Innovative Research in Computer Science and Technology (Nov) 2021

Section

Articles

How to Cite

A Review of Data Mining Techniques and Its Applications . (2021). International Journal of Innovative Research in Computer Science & Technology, 9(6), 100–104. Retrieved from https://acspublisher.com/journals/index.php/ijircst/article/view/11128