CFP last date
20 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

Analysis of Data Mining Techniques and its Applications

by Fathimath Zuha Maksood, Geetha Achuthan
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 140 - Number 3
Year of Publication: 2016
Authors: Fathimath Zuha Maksood, Geetha Achuthan
10.5120/ijca2016909249

Fathimath Zuha Maksood, Geetha Achuthan . Analysis of Data Mining Techniques and its Applications. International Journal of Computer Applications. 140, 3 ( April 2016), 6-14. DOI=10.5120/ijca2016909249

@article{ 10.5120/ijca2016909249,
author = { Fathimath Zuha Maksood, Geetha Achuthan },
title = { Analysis of Data Mining Techniques and its Applications },
journal = { International Journal of Computer Applications },
issue_date = { April 2016 },
volume = { 140 },
number = { 3 },
month = { April },
year = { 2016 },
issn = { 0975-8887 },
pages = { 6-14 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume140/number3/24572-2016909249/ },
doi = { 10.5120/ijca2016909249 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:41:17.061643+05:30
%A Fathimath Zuha Maksood
%A Geetha Achuthan
%T Analysis of Data Mining Techniques and its Applications
%J International Journal of Computer Applications
%@ 0975-8887
%V 140
%N 3
%P 6-14
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The exponential increase in data over the recent years has urged for techniques to log, process and analyze these records. Heavy data repositories with a bulk of unprocessed content can lead to wastage of storage space as well as loss of hidden information. Since the late 90s, efforts have been taken to refine the concept of Knowledge Discovery in Databases and data mining. Organizations have started incorporating this approach to market their promotions as well as predict the buyers’ choices. This paper is aimed at providing a detailed introduction to data mining, review of real world applications pertaining to the concept, big data and data mining techniques, as well as an integrated overview of the recent studies related to smart cities in the field of traffic prediction and forecasting energy consumption, especially in Oman.

References
  1. W. J. Frawley, G. Piatetsky-Shapiro and C. Matheus, "Knowledge Discovery in Databases: An Overview," AI Magazine, vol. 13, no. 3, pp. 57-70, 1992.
  2. L. A. Kurgan and P. Musilek, "A survey of Knowledge Discovery and Data Mining Process," The Knowledge Engineering Review, vol. 21, no. 1, pp. 1-24, 2006.
  3. F. Weiping and W. Yuming, "The Development of Data Mining," International Journal of Business and Social Science, vol. 4, no. 16, pp. 157-162, 2013.
  4. T. Silwattananusarn and K. Tuamsuk, "Data Mining and Its Applications for Knowledge Management : A Literature Review from 2007 to 2012," International Journal of Data Mining & Knowledge Management Process, vol. 2, no. 5, pp. 13-24, 2012.
  5. U. Fayyad, G. Piatetsky-Shapiro and P. Smyth, "From Data Mining to Knowledge Discovery in Databases," AI Magazine, vol. 17, no. 3, pp. 37-54, 1996.
  6. ] Smita and P. Sharma, "Use of Data Mining in Various Field: A Survey Paper," IOSR Journal of Computer Engineering, vol. 16, no. 3, pp. 18-21, 2014.
  7. S. Adelman, "The Data Warehouse Database Explosion," Enterprise Information Management Institute, March 2008.
  8. Universidad San Pablo, "Case study: The Rise of Wal-Mart," 21 June 2012. [Online]. Available: http://biolab.uspceu.com/datamining/WalMart.pdf. [Accessed 28 November 2015].
  9. Ian Davey and Technolegis, "Consumers, Big Data, and Online Tracking in the Retail Industry: A CASE STUDY OF WALMART," 10 August 2014. [Online]. Available: https://saveballston.files.wordpress.com/2014/08/walmart_privacy_.pdf. [Accessed 29 November 2015].
  10. K. Hill, "How Target Figured Out A Teen Girl Was Pregnant Before Her Father Did," Forbes, 16 February 2012. [Online]. Available: http://www.forbes.com/sites/kashmirhill/2012/02/16/how-target-figured-out-a-teen-girl-was-pregnant-before-her-father-did/. [Accessed 29 November 2015].
  11. N. Baby and P. L.T, "Customer Classification And Prediction Based On Data Mining Technique," International Journal of Emerging Technology and Advanced Engineering, vol. 2, no. 12, pp. 314-318, 2012.
  12. G. Simonsen, "Retail Insights," Online Digital Publishing, [Online]. Available: http://onlinedigitalpublishing.com/article/Retail_Insights/549723/52404/article.html. [Accessed 30 November 2015].
  13. D. Pareek, Business Intelligence for Telecommunications, New York: Auerbach Publications, 2007.
  14. M. V. Joseph, "Data Mining and Business Intelligence Applications in Telecommunication Industry," International Journal of Engineering and Advanced Technology, vol. 2, no. 3, pp. 525-528, 2013.
  15. D. Crockett and B. Eliason, "What is Data Mining in Healthcare?," HealthCatalyst, [Online]. Available: https://www.healthcatalyst.com/data-mining-in-healthcare. [Accessed 30 November 2015].
  16. J. Jackson, "DATA MINING: A CONCEPTUAL OVERVIEW," Communications of the Association for Information Systems, vol. 8, pp. 267-296, 2002.
  17. R. Bellazzi and B. Zupan, "Predictive data mining in clinical medicine: Current issues and guidelines," International journal of medical informatics: Elsevier, vol. 77, pp. 81-97, 2008.
  18. C. Huttenhower and O. Hofmann, "A Quick Guide to Large Scale Genomic Data Mining," 3 April 2012. [Online]. Available: http://www.stat.harvard.edu/18ACCF14-7036-4F35-A3BD-A3D55AF66DE8/FinalDownload/DownloadId-4E694BAB09E8713F88915D8E891CF0D9/18ACCF14-7036-4F35-A3BD-A3D55AF66DE8/NESS10/HuttenhowerMarkowetz/A%20Quick%20Guide%20to%20Large%20Scale%20Genomic%20Data%20Mining.pd. [Accessed 30 November 2015].
  19. B. Louie, P. Mork, F. Martin-Sanchez, A. Halevy and P. Tarczy-Hornoch, "Data integration and genomic medicine," Journal of Biomedical Informatics, vol. 40, no. 1, pp. 5-16, 2006.
  20. P. Szolovits, "Mining Clinical Data to build Predictive Model," 2 May 2013. [Online]. Available: https://www.siam.org/meetings/sdm13/szolovits.pdf. [Accessed 30 November 2015].
  21. J. Luan, "Data Mining and Its Applications in Higher Education," Wiley Periodicals, pp. 17-36, 3 June 2002.
  22. M. Goyal and R. Vohra, "Applications of Data Mining in Higher Education," International Journal of Computer Science Issues, vol. 9, no. 2, pp. 113-120, 2012.
  23. B. M. Ramageri, "DATA MINING TECHNIQUES AND APPLICATIONS," Indian Journal of Computer Science and Engineering, vol. 1, no. 4, pp. 301-305, 2011.
  24. R. Petre, "Data Mining Solutions for the Business Environment," Database Systems Journal, vol. 4, pp. 21-28, 2013.
  25. L. Rokach and O. Maimon, "Clustering Methods," in The Data Mining and Knowledge Discovery Handbook, New York, Springer US, 2006, pp. 321--352.
  26. [N. Sharma, A. Bajpai and R. Litoriya, "Comparison the various clustering algorithms of weka," International Journal of Emerging Technology and Advanced Engineering, vol. 2, no. 5, pp. 73-80, 2012.
  27. S. Kumar and N. , "K-Mean Evaluation in Weka Tool and Modifying It using Standard Score Method," International Journal on Recent and Innovation Trends in Computing and Communication, vol. 2, no. 9, p. 2704 – 2706, 2014.
  28. J. Han and M. Kamber, Data Mining - Concepts and Techniques, 2nd Edition ed., San Fransisco: Elsevier, 2008.
  29. P. Shrivastava and H. Gupta, "A Review of Density-Based clustering in Spatial Data," International Journal of Advanced Computer Research , vol. 2, no. 5, pp. 200-202, 2012.
  30. X. Wu, X. Zhu, G.-Q. Wu and W. Ding, Blind Men and the elephant, 2014.
  31. X. Wu, X. Zhu, G.-Q. Wu and W. Ding, "Data mining with big data," IEEE Transactions on Knowledge and Data Engineering, vol. 26, no. 1, pp. 97-107, 2014.
  32. "Taming Big Data: Small Data vs. Big Data," IBM. [Online]. [Accessed 14 November 2015].
  33. M. Herland, T. M. Khoshgoftaar and R. Wald, "A review of data mining using big data in health informatics," Springer Journal of Big Data, vol. 1, no. 2, pp. 1-35, 2014.
  34. J. Shafer, R. Agrawal and M. Mehta, "SPRINT: A Scalable Parallel Classifier for Data Mining," in Proceedings of the 22nd VLDB Conference , Mumbai, 1996.
  35. D. Luo, C. Ding and H. Huang, "Parallelization with Multiplicative Algorithms for Big Data Mining," in IEEE 12th International Conference on Data Mining , Brussels, 2012.
  36. Y. Li, L. Guo and Y. Guo, "An Efficient and Performance-Aware Big Data Storage System," in Cloud Computing and Services Science, New York, Springer International Publishing, 2013, pp. 102-116.
  37. "The Four V's of Big Data," IBM, 2015. [Online]. Available: http://www.ibmbigdatahub.com/infographic/four-vs-big-data. [Accessed 30 November 2015].
  38. S. Kumar, F. Morstatter and H. Liu, Twitter Data Analytics, New York: Springer, 2013.
  39. S. Agrawal, "I hate the whole concept of describing Big Data as a lot of data: Mu Sigma’s Dhiraj Rajaram," Tech Circle, 2 September 2014. [Online]. Available: http://techcircle.vccircle.com/2014/09/02/i-hate-the-whole-concept-of-describing-big-data-as-a-lot-of-data-ipo-is-a-possibility-mu-sigmas-dhiraj-rajaram/. [Accessed 20 October 2015].
  40. D. Borthakur, "HDFS Architecture Guide," 4 August 2013. [Online]. Available: https://hadoop.apache.org/docs/r1.2.1/hdfs_design.pdf. [Accessed 30 November 2015].
  41. G. Yogaraj and A. A. Arun, "Mining High Dimensional Data Sets Using Big Data," International Journal of Advanced Research in Computer Science and Software Engineering, vol. 5, no. 2, pp. 970-974, 2015.
  42. B. Oancea and R. Dragoescu, "Integrating R and Hadoop for Big Data Analaysis," Revista Romana de Statistica, vol. 2, pp. 83-94, 2014.
  43. A. Chakravarthy, Components of Hadoop Architecture, Cisco, 2012.
  44. J. Manyika, M. Chui, B. Brown, J. Bughin, R. Dobbs, C. Roxburg and A. H. Byers, "Big data: The next frontier for innovation, competition, and productivity," May 2011. [Online]. Available: http://www.mckinsey.com/insights/business_technology/big_data_the_next_frontier_for_innovation. [Accessed 29 November 2015].
  45. J. Svetlik, "Rise of the smart city: The awesome and scary reality of future urban living," Wearable, 22 July 2015. [Online]. Available: http://www.wareable.com/internet-of-things/the-awesome-and-scary-future-of-our-cities-2025. [Accessed 30 November 2015].
  46. M. Batty, K. Axhausen, G. Fosca, A. Pozdnoukhov, A. Bazzani, M. Wachowicz, G. Ouzounis and Y. Portugali, "Smart Cities of the Future," Centre for Advanced Spatial Analysis, London, 2012.
  47. M. Romkey, " Smart cities…not just the sum of its parts," Deloitte, Dubai, 2015.
  48. "Focus Group on Smart Sustainable Cities," ITU, 2015. [Online]. Available: http://www.itu.int/en/ITU-T/focusgroups/ssc/Pages/default.aspx. [Accessed 30 November 2015].
  49. B. Murgante and G. Borruso, "Cities and Smartness: A Critical Analysis of Opportunities and Risks," in Computational Science and Its Applications – ICCSA 2013, New York, Springer Berlin Heidelberg, 2013, pp. 630-642.
  50. M. Tercek, "More People, More Problems: Future-Proofing our Cities," [Online]. Available: http://bigthink.com/experts-corner/more-people-more-problems-future-proofing-our-cities. [Accessed 20 December 2015].
  51. B. Zhang, K. Xing, X. Cheng, L. Huang and R. Bie, "Traffic Clustering and Online Traffic Prediction in Vehicle Networks: A Social Influence Perspective," in 2012 Proceedings IEEE INFOCOM, Orlanndo, 2012.
  52. B. Pan, D. Ugur and S. Cyrus, "Utilizing Real-World Transportation Data for Accurate Traffic Prediction," in 2012 IEEE 12th International Conference on Data Mining (ICDM), Brussels, 2012.
  53. C. Costa and M. Y. Santos, "Improving Cities Sustainability through the Use of Data Mining in a Context of Big City Data," in Proceedings of the World Congress on Engineering, London, 2015.
  54. I. Khan, A. Capozzoli, S. P. Corgnati and T. Cerquitelli, "Fault Detection Analysis of Building Energy Consumption Using Data Mining Techniques," in Energy Procedia: The Mediterranean Green Energy Forum 2013, Fez, 2013.
  55. N. Arghira, S. Ploix, I. Fagarasan and S. S. Iliescu, "Forecasting Energy Consumption in Dwellings," in Advances in Intelligent Control Systems and Computer Science, Berlin, Springer Berlin Heidelberg, 2013, pp. 251-264.
  56. V. Ginn, "TPPF: California’s failed green energy project lesson for Texas," Midland Reporter-Telegram, Texas, 2015.
  57. Sara, "FAIL: 20 Infamous ‘Green Innovations’ That Aren’t," WebEcoist, [Online]. Available: http://webecoist.momtastic.com/2008/10/20/failed-green-technologies-designs-and-innovations/. [Accessed 13th December 2015].
  58. T. Singh, "6 Ways in Which London 2012 has Failed to be ‘The Green Olympics’," inhabitat, 8 May 2012. [Online]. Available: http://inhabitat.com/6-ways-in-which-london-2012-has-failed-to-be-the-green-olympics/. [Accessed 13 December 2015].
  59. "Oman - Electricity production," Indexmundi, [Online]. Available: http://www.indexmundi.com/facts/oman/electricity-production. [Accessed 8 March 2016].
  60. "Renewable energy," Public Authority for Electricity and Water, [Online]. Available: https://www.paew.gov.om/Our-role-in-Oman/Renewable-energy. [Accessed 8 March 2016].
  61. C. Prabhu, "Energy efficiency can halve gas consumption in Oman," Oman Observer, 30 May 2015. [Online]. Available: http://omanobserver.om/energy-efficiency-can-halve-gas-consumption-expert/. [Accessed 8 March 2016].
  62. E. H. AlHarrasi, B. Jrew and M. Abojaradeh, "Development of Traffic Accident Models in Oman," in Seventh Traffic Safety Conference, Amman, 2015.
  63. International Organisation for Knowledge Economy and Enterprise Development, "Smart Data & Well-Being," 29 October 2014. [Online]. Available: http://iked.org/pdf/Proj%20GENERAL%20Pres%2017%20Nov.pdf. [Accessed 10 December 2015].
  64. Authority of Electricity Regulation, Oman, "Study on Renewable Energy Resources, Oman," May 2008. [Online]. Available: http://www.aer-oman.org/pdf/studyreport.pdf. [Accessed 10 December 2015].
  65. Y. H. Zurigat, N. M. Sawaqed, H. Al-Hinai and B. A. Jubran, "Analysis of Typical Meteorological Year for," International Journal of Low Carbon Technologies, vol. 2, no. 4, pp. 323-338, 2007.
  66. T. Sweetnam, "Residential Energy Use In Oman:A Scoping Study," 13 January 2014. [Online]. Available: http://discovery.ucl.ac.uk/1425280/1/Oman%20Final%20Report%20v0%208_revised.pdf. [Accessed 12 December 2015].
  67. I. Vilajosana, J. Llosa, B. Martinez, M. Domingo-Prieto, A. Angles and X. Vilajosana, "Bootstrapping Smart Cities through a Self-Sustainable Model Based on Big Data Flows," IEEE Communications Magazine, pp. 128-134, June 2013.
Index Terms

Computer Science
Information Sciences

Keywords

Data Mining Big Data Smart City Clustering Classification Regression

Powered by PhDFocusTM