Full Paper View

Efficiency of Data Mining Algorithms Used In Agnostic Data Analytics Insight Tools

A. Jenita Jebamalar1

Section:Research Paper, Product Type: Journal
Vol.6 , Issue.6 , pp.14-18, Dec-2018

Online published on Dec 31, 2018


Copyright © A. Jenita Jebamalar . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

View this paper at   Google Scholar | DPI Digital Library


XML View     PDF Download

Citation :
IEEE Style Citation: A. Jenita Jebamalar, “Efficiency of Data Mining Algorithms Used In Agnostic Data Analytics Insight Tools”, International Journal of Scientific Research in Network Security and Communication, Vol.6, Issue.6, pp.14-18, 2018.

MLA Style Citation: A. Jenita Jebamalar "Efficiency of Data Mining Algorithms Used In Agnostic Data Analytics Insight Tools." International Journal of Scientific Research in Network Security and Communication 6.6 (2018): 14-18.

APA Style Citation: A. Jenita Jebamalar, (2018). Efficiency of Data Mining Algorithms Used In Agnostic Data Analytics Insight Tools. International Journal of Scientific Research in Network Security and Communication, 6(6), 14-18.

30 Views    36 Downloads    23 Downloads
  
  

Abstract :
Insights are the results of the analytics with various parameters like customer demographics, gender, age, behavior, interests, etc. The objective is to predict which product the customers are least likely and most likely to buy. The result of the analytics is the insights which are provided in the form of tables, charts and graphs. In the technology world, the term agnostic means that the tools are not restricted to a specific systems and it works with various systems rather than being designed for a single system. Agnostic data means that it does not comes from a specific source. In machine learning, feature selection is used to reduce the properties of the class variables by removing the redundancy from the dataset. The goal of this research work is compare and find the efficiency of various data mining algorithms used in analytics insight tools. Dataset is collected from an analytics of a website for the listed algorithms. Data mining utilizes algorithms, statistical analysis and even artificial intelligence to extract data from huge data sets into an apprehensible form. The future work will be the implementation of the selected algorithm in the data analytics insight tool.

Key-Words / Index Term :
Agnostic, Insights, Feature Selection Algorithms, Data Analysis, Data Discovery

References :
[1]. Aldekhail M, “Application and Significance of Web Usage Mining in the 21st Century: A Literature Review”, International Journal of Computer Theory and Engineering, Vol.8, Issue.1, pp. 41-47,2016..
[2]. Tarik A. Rashid, “Improving on Classification Models of Multiple Classes through Effectual Processes”, International Journal of Advanced Computer Science and Applications, Vol.6, Issue.7, pp.55-62, 2015.
[3]. Forman, George, "An extensive empirical study of feature selection metrics for text classification", The Journalof machine learning research, Vol.3, pp.1289-1305, 2003.
[4]. Egozi, Ofer, EvgeniyGabrilovich, and Shaul Markovitch, "Concept-Based Feature Generation and Selection for Information Retrieval", AAAI, Vol.8, pp. 1132-1137, 2008.
[5]. Russel, Stuart, and Peter Norvig, "Artificial Intelligence: A Modern Approach ", EUA: Prentice Hall, 2003.
[6]. Lee, Wenke, Salvatore J. Stolfo, and Kui W. Mok, "Adaptive intrusion detection: A data mining approach", Artificial Intelligence Review, Vol.14, Issue.6, pp. 533-567, 2000.
[7]. Cooley R., Mobasher B. and Srivastava J, “Data Preparation for Mining World Wide Web Browsing Patterns. Knowledge and Information Systems”, Vol.1, Issue 1, pp. 5-32, 1999.
[8]. Vaibhav P. Vasani, Rajendra D. Gawali, “Classification and performance evaluation using data mining algorithm”, International. Journal of Innovative Research in Science, Engineering and Technology, Vol.3, Issue 3, pp.10453-10458, 2014.

Authorization Required

 

You do not have rights to view the full text article.
Please contact administration for subscription to Journal or individual article.
Mail us at  editor@isroset.org or view contact page for more details.

Impact Factor

Journals Contents

Author & Reviewer

Download

Digital Certificate

Go to Navigation