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R: An Emerging Statistical Data Mining Tool

Published on April 2016 by Pooja Kshirsagar, A.r. Kulkarni
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National Seminar on Recent Trends in Data Mining
Foundation of Computer Science USA
RTDM2016 - Number 3
April 2016
Authors: Pooja Kshirsagar, A.r. Kulkarni
ff263752-8fdc-4b95-9fed-8a34f757b187

Pooja Kshirsagar, A.r. Kulkarni . R: An Emerging Statistical Data Mining Tool. National Seminar on Recent Trends in Data Mining. RTDM2016, 3 (April 2016), 4-8.

@article{
author = { Pooja Kshirsagar, A.r. Kulkarni },
title = { R: An Emerging Statistical Data Mining Tool },
journal = { National Seminar on Recent Trends in Data Mining },
issue_date = { April 2016 },
volume = { RTDM2016 },
number = { 3 },
month = { April },
year = { 2016 },
issn = 0975-8887,
pages = { 4-8 },
numpages = 5,
url = { /proceedings/rtdm2016/number3/24690-2584/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 National Seminar on Recent Trends in Data Mining
%A Pooja Kshirsagar
%A A.r. Kulkarni
%T R: An Emerging Statistical Data Mining Tool
%J National Seminar on Recent Trends in Data Mining
%@ 0975-8887
%V RTDM2016
%N 3
%P 4-8
%D 2016
%I International Journal of Computer Applications
Abstract

On account of incremental growth in big data analytics, various fields of research and industries require effective data mining tools to derive relevant infsormation from various databases. Thus data mining, big data, machine learning algorithms are all linked with each other and work for a common cause i. e. information. Big Data are very complex in nature and thus mining them is not an easy job. Thus the need of effective data mining tools comes into picture. This paper explores the aspects of R and R studio along with the overview of big data and data mining. R provides different dimensions to statistical analysis of data sets. However in this paper we discuss the overview of the R studio and demonstrate the implementation of k-means algorithm. (Burda, 2015)

References
  1. BURDA, M. Linguistic fuzzy logic in R. Fuzzy Systems (FUZZ-IEEE), 2015 IEEE International Conference on, 2-5 Aug. 2015. 1-7.
  2. KITCHAROEN, N. , KAMOLSANTISUK, S. , ANGSOMBOON, R. & ACHALAKUL, T. RapidMiner framework for manufacturing data analysis on the cloud. Computer Science and Software Engineering (JCSSE), 2013 10th International Joint Conference on, 29-31 May 2013 2013. 149-154.
  3. KOSORUS, H. , HONIGL, J. & KUNG, J. Using R, WEKA and RapidMiner in Time Series Analysis of Sensor Data for Structural Health Monitoring. Database and Expert Systems Applications (DEXA), 2011 22nd International Workshop on, Aug. 29 2011-Sept. 2 2011 2011. 306-310.
  4. PANDEY, R. , SRIVASTAVA, N. & FATIMA, S. Extending R Boxplot Analysis to Big Data in Education. Communication Systems and Network Technologies (CSNT), 2015 Fifth International Conference on, 4-6 April 2015 2015. 1030-1033.
  5. RAMIREZ, C. , NAGAPPAN, M. & MIRAKHORLI, M. Studying the impact of evolution in R libraries on software engineering research. Software Analytics (SWAN), 2015 IEEE 1st International Workshop on, 2-2 March 2015 2015. 29-30.
  6. http://dss. princeton. edu/training/RStudio101. pdf
  7. https://www. youtube. com/watch?v=sAtnX3UJyN0
Index Terms

Computer Science
Information Sciences

Keywords

R Tool R Script Big Data K-means Weka Rapid Miner