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A Hybrid K-Mean Clustering Algorithm for Prediction Analysis
Background/Objective: The objective of this research is to make improvement in defining the clusters automatically and to assign required clusters to un-clustered points. Methods/Statistical Analysis: The main disadvantage of k-mean is of accuracy, as in k-mean clustering user needs to define number of clusters during the start of process. This restriction of predefined number of clusters leads to some points of the dataset remained un-clustered. So by enhancing the cluster technique, the predictions can be improved. We use Iris dataset for the current study and to generate the results using normalization in the methodology which will lead to improvement in accuracy and will reduce clustering time by the member assigned to the cluster. Findings: The normalization is used to get better results in the form of finding distance to have exact centroid and to remove noise data which is not needed. We are applying backtracking method to find the exact number of clusters that should be defined to analyze the data in better way. The results shows that there is an improvement in clustering when compared to the existing methodologies.
K-mean Clustering, Prediction Analysis, Data Mining, Classification, Clustering, Hybrid Clustering.
- Rauf A, Mahfooz M, Khusro S, Javed H. Enhanced K-Mean Clustering Algorithm To Reduce Number of Iterations and Time Complexity. Middle-East Journal of Scientific Research. 2012; 12(7):959-63.
- Osamor VC, Adebiyi A, Oyelade O, Doumbia D. Reducing the Time Requirement of K-Means Algorithm. PLoS ONE. 2012; 7(12):56-62.
- Rajalakshmi K, Dhenakaran SS, Roobin N. Comparative Analysis of K-Means Algorithm in Disease Prediction. International Journal of Science, Engineering and Technology Research (IJSETR). 2015 July; 4(7):1-3.
- Oyelade O, Oladipupo O, Obagbuwa IC. Application of K-Means Clustering Algorithm for Prediction of Students’ Academic Performance. International Journal of Computer Science and Information Security. 2010; 7(1):1-4.
- Rani CMS, Narayana T, Sajana K. A Survey on Clustering Techniques for Big Data Mining. Indian Journal of Science and Technology. 2016; 9(3):1-12.
- Weather Forecasting using Incremental K-means Clustering. Date accessed: 2014: Available from: https://arxiv.org/ftp/arxiv/papers/1406/1406.4756.pdf.
- Yadav AK, Tomar D, Agarwal S. Clustering of Lung Cancer Data Using Foggy K-Means. International Conference on Recent Trends in Information Technology (ICRTIT). 2013; p. 13-18.
- Sa CL, Ibrahim BA, Hossain D. Student performance analysis system (SPAS). Information and Communication Technology for The Muslim World (ICT4M). 2014 Nov; p.1-6.
- Selvakumar K, Sai Ramesh L and Kannan A. Enhanced K-Means Clustering Algorithm for Evolving User Groups. Indian Journal of Science and Technology. 2015; 8(24):1-8.
- Bellaachia A, Guven E. Predicting Breast Cancer Survivability Using Data Mining Techniques. Software Technology and Engineering (ICSTE). 2010 Oct; 2(1):227-31.
- Qasem A. Al-Radaideh A, Assaf AA, Alnagi A. Predicting Stock Prices Using Data Mining Techniques. The International Arab Conference on Information Technology. 2013; p.1-8.
- Sundar B, Devi VT, Saravan V. Development of A Data Clustering Algorithm for Predicting Heart. International Journal of Computer Application. 2012 June; 48(7):888-975.
- Ray S, Rose H, Turi T. Determination of Number of Clusters in K-Means Clustering and Application in Colour Image Segmentation. Proceedings of the 4th International Conference on Advances in Pattern Recognition and Digital Techniques, India. 1999; p. 137-43.
- Yedla M, Srinivasa TM. Enhancing K-means Clustering Algorithm with Improved Initial Center. International Journal of Computer Science and Information Technologies. 2010; 1(2):1-5.
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