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Algorithmic Approach to Data Mining and Classification Techniques

Affiliations

  • Department of Computer Science and Engineering, Chandigarh Engineering College, Mohali - 140307, Punjab, India

Abstract


Objective/Background: This paper highlights the extension of access data to data mining from passing year to recent. Main aim of this paper is comparative study of tools/techniques/algorithms which are used for analysis of huge amount of data. Methods/Statistical Analysis: Different methods of data mining has been studied and discussed which include decision tree, neural network, regression, clustering techniques are implemented on different tools for fraud detection. Different algorithms Adaboost, page rank, K-means used for data mining are also discussed. For generate relevant information from data streams, frequent pattern generation tree algorithm is also implemented and discussed. Findings: Out of so many available algorithms decision tree has been found out to be the most suitable for mining data provided the data is restricted to some thousand of entries. The most prominent feature as its advantage lies in its clear illustration in the form of graphical tree with inherent tree structure capability. However the concern about ambiguity should be carefully dealt with maintains consistency. Applications: For the extraction of the relevant data, data mining is helpful in various ways. The various areas where data mining is being used have also been discussed in the paper. Future Scope: The scope of the paper extends from an exhaustive survey and analysis of all available empirical and conceptual techniques and tools in the area of data mining.

Keywords

Association Rule Mining, Classification, Clustering, Data, Data Mining, Decision tree, Neural Network.

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References


  • Phridvi Raj MSB, Guru Rao CV. Data mining–past, present and future – A typical survey on data streams. Procedia Technology, 2014; 12:255–63.
  • Usama F, Piatetsky-Shapiro G, Smyth P. From data mining to knowledge discovery in databases. American Association for Artificial Intelligence.1996; 17(3):37–54.
  • Chris R, Wang JC, Yen DC. Data mining techniques for customer relationship management. Technology in Society. 2002; 24(4):483–502.
  • Padhy N, Mishra P, Panigrahi P. The survey of data mining applications and feature scope. International Journal of Computer Science, Engineering and Information Technology. 2012; 2(3):43–58.
  • King M, Elder J, Gomolka B, Schmidt E, Summers M, Toop K. Evaluation of fourteen desktop data mining tools. IEEE International Conference on Systems, Man and Cybernetics. San Diego, CA. 1998; 3:2927–32.
  • Abbott DW, Matkovsky IP, Elder JF. An evaluation of high-end data mining tools for fraud detection. IEEE International Conference on Systems, Man and Cybernetics. 1998; 3:2836–41.
  • Hauser AT, Scherer TW. Data mining tools for real-time traffic signal decision support and maintenance. IEEE International Conference on Systems, Man and Cybernetics. 2001; 3:1471–7.
  • Rushing J, Ramachandran R, Nair U, Graves S, Welch R, Lin H. ADaM: A Data Mining toolkit for scientists and engineers. Computers and Geosciences. 2005; 31(5):607–18.
  • Bayam E, Liebowitz J, Agresti W. Older drivers and accidents: A meta analysis and data mining application on traffic accident data. Expert Systems with Applications, 2005; 29(3):598–629.
  • Wu X, Kumar V, Quinlan JR, Ghosh J, Yang Q, Motoda H, McLachlan GJ, et al. Top 10 algorithms in data mining. Knowledge and Information Systems. 2008; 14(1):1–37.
  • Korting TS, Fonseca LM, Camara G. GeoDMA - Geographic Data Mining Analyst. Computers and Geosciences. 2013; 57:133–45.
  • Huang TCK, Wu IL, Chou CC. Investigating use continuance of data mining tools. International Journal of Information Management. 2013; 33(5):791–801.
  • Mohamad SK, Tasir Z. Educational data mining: A review. Procedia-Social and Behavioral Sciences. 2013; 97:320–4.
  • Natek S, Zwilling M. Student data mining solution–knowledge management system related to higher education institutions. Expert Systems with Applications. 2014; 41(14):6400–7.
  • Jovic A, Brkic K, Bogunovic N. An overview of free software tools for general data mining. 37th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO); 2014. p. 1112–7.
  • Gera M, Goel S. Data mining-techniques, methods and algorithms: A review on tools and their validity. International Journal of Computer Applications. 2015; 113(18):22–9.
  • Sajana T, Sheela Rani CM, Narayana KV. A survey on clustering techniques for big data mining. Indian Journal of Science and Technology. 2016 Jan; 9(3).
  • Hariharan R, Mahesh C, Prasenna P, Vinoth Kumar R. Enhancing privacy preservation in data mining using cluster based greedy method in hierarchical approach. Indian Journal of Science and Technology. 2016 Jan; 9(3).
  • Murugananthan V, Shiva Kumar BL. An adaptive educational data mining technique for mining educational data models in E-learning systems. Indian Journal of Science and Technology. 2016 Jan; 9(3).
  • Sivakumar S, Venkataraman S, Selvaraj R. Predictive modeling of student dropout indicators in educational data mining using improved decision tree. Indian Journal of Science and Technology. 2016 Jan; 9(4).
  • Undavia JN, Dolia P, Patel A. Customized prediction model to predict post-graduation course for graduating students using decision tree classifier. Indian Journal of Science and Technology. 2016 Mar; 9(12).
  • Alzahrani AS, Qureshi MS. Privacy preserving optimized rules mining from decision tables and decision trees. Indian Journal of Science and Technology. 2012 Jun; 5(6).
  • Verma A, Kaur I, Singh I. Comparative analysis of data mining tools and techniques for information retrieval. Indian Journal of Science and Technology. 2016; 9(11).
  • Purusothaman G, Krishnakumari P. A survey of data mining techniques on risk prediction: Heart disease. Indian Journal of Science and Technology. 2015; 8(12).
  • Lohita K, Sree AA, Poojitha D, Devi TR, Umamakeswari A. Performance analysis of various data mining techniques in the prediction of heart disease. Indian Journal of Science and Technology. 2015; 8(35).
  • Murugananthan V, Kumar BLS. An adaptive educational data mining technique for mining educational data models in E-learning systems. Indian Journal of Science and Technology. 2016; 9(3).
  • Rajalakshmi V, Mala GSA. Anonymization by data relocation using sub-clustering for privacy preserving data mining. Indian Journal of Science and Technology. 2014; 7(7).
  • Chakradeo SN, Abraham RM, Rani BA, Manjula R. Data mining: Building social network. Indian Journal of Science and Technology. 2015; 8(2).
  • Kholghi M, Hassanzadeh H, Keyvanpour MR. Classification and evaluation of data mining techniques for data stream requirements. International Symposium on Computer Communication Control and Automation (3CA); Tainan. 2010. p. 474–8.
  • Purusothaman G, Krishnakumari P. A survey of data mining techniques on risk prediction: Heart disease. Indian Journal of Science and Technology. 2015; 8(12).

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