Application of Data Mining in Census Data Analysis using Weka
Citation
Ms.Dhwani Sondhi "Application of Data Mining in Census Data Analysis using Weka", International Journal of Engineering Trends and Technology (IJETT), V52(3),157-161 October 2017. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group
Abstract
Data mining which is the automatic process
of extraction of useful data by using statistical and
visualization techniques has become the new
preference for statisticians, scientists and
researchers alike. It helps in looking out for the most
important trends in data and taking business -
oriented decisions. The paper here presents an
application of data mining in the analysis of census
and looksat the noticeable trends. The objective of
the paper is to discover the relevant information of
gender inequality in all spheres from the primary
census of the Raigarh district in Maharashtra with
the help of appropriate data mining methods like
clustering, visualization using the Weka tool.
Reference
1. Agrawal, R., & Shim, K. (1996, August). Developing
Tightly-Coupled Data Mining Applications on a Relational
Database System. In KDD (pp. 287-290).
2. Baker, R. S. J. D. (2010). Data mining for
education. International encyclopedia of education, 7(3),
112-118.
3. Berkhin, P. (2006). A survey of clustering data mining
techniques. Grouping multidimensional data, 25, 71.
4. Diz, J., Marreiros, G., & Freitas, A. (2016). Applying Data
Mining Techniques to Improve Breast Cancer
Diagnosis. Journal of medical systems, 40(9), 203.
5. Elder, J. (2009). Handbook of statistical analysis and data
mining applications. Academic Press.
6. Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). From
data mining to knowledge discovery in databases. AI
magazine, 17(3), 37.
7. Frank, E., Hall, M., Holmes, G., Kirkby, R., Pfahringer, B.,
Witten, I. H., & Trigg, L. (2009). Weka-a machine learning
workbench for data mining. In Data mining and knowledge
discovery handbook(pp. 1269-1277). Springer US.
8. Frank, E., Hall, M., Trigg, L., Holmes, G., & Witten, I. H.
(2004). Data mining in bioinformatics using
Weka. Bioinformatics, 20(15), 2479-2481.
9. Han, J., Pei, J., &Kamber, M. (2011). Data mining: concepts
and techniques. Elsevier.
10. Koh, H. C., & Tan, G. (2011). Data mining applications in
healthcare. Journal of healthcare information
management, 19(2), 65.
11. Markov, Z., & Russell, I. (2006). An introduction to the
WEKA data mining system. ACM SIGCSE Bulletin, 38(3),
367-368.
12. Mining, W. I. D. (2006). Data Mining: Concepts and
Techniques. Morgan Kaufinann.
13. Romero, C., Ventura, S., & García, E. (2008). Data mining in
course management systems: Moodle case study and
tutorial. Computers & Education, 51(1), 368-384.
14. Romero, C., & Ventura, S. (2013). Data mining in
education. Wiley Interdisciplinary Reviews: Data Mining and
Knowledge Discovery, 3(1), 12-27.
15. Shmueli, G., Bruce, P. C., Yahav, I., Patel, N. R., &
Lichtendahl Jr, K. C. (2017). Data Mining for Business
Analytics: Concepts, Techniques, and Applications in R. John
Wiley & Sons.
16. Tan, P. N. (2006). Introduction to data mining. Pearson
Education India.
17. Varlamis, I., Apostolakis, I., Sifaki-Pistolla, D., Dey, N.,
Georgoulias, V., & Lionis, C. (2017). Application of data
mining techniques and data analysis methods to measure
cancer morbidity and mortality data in a regional cancer
registry: The case of the island of Crete, Greece. Computer
Methods and Programs in Biomedicine, 145, 73-83.
18. Witten, I. H., Frank, E., Hall, M. A., & Pal, C. J.
(2016). Data Mining: Practical machine learning tools and
techniques. Morgan Kaufmann.
Keywords
WEKA, Census, Gender inequality, data
mining.