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A Novel Technique to Enhance K-Mean Clustering using Back Propagation Algorithm

Affiliations

  • Computer Science Engineering Department, Chandigarh University, Gharuan - 140413, Punjab, India

Abstract


Objectives: In this work, improvement in the k-mean clustering is proposed in terms of accuracy and execution time. Methods/Statistical Analysis: The clustering is the technique which is used to analyze the data in the efficient manner. In the recent times various clustering algorithms has been proposed which are based on different type of clusters. Among the proposed algorithms k-mean performs batter in terms of accuracy and execution time. In the k-mean clustering algorithm, the dataset is loaded and from which number of attributes and members are analyzed. The arithmetic mean is calculated from the dataset which will be the central point. The central point is considered as the referral point and Euclidian distance to calculate to all other points in the dataset. The Euclidian distance is calculated by taking single central point due which accuracy of clustering is reduced. In the work, back propagation technique is applied which will calculate Euclidian distance multiple times and achieve maximum accuracy of clustering. Findings: The k-mean clustering is the efficient clustering technique which clusters the similar and dissimilar type of data. The k-mean clustering calculates data similarity by calculating the Euclidian distance from the central point. The central point is calculated by taking arithmetic mean of the dataset. When the dataset is complex and large in size, k-mean clustering is not able to drive exact relation between the data points due which some points are left uncluttered. The back propagation algorithm is used to drive accurate relationship between the data points. In the back propagation algorithm input values and actual clustering is derived and this process continues unless maximum accuracy is achieved of clustering. The proposed algorithm is implemented in MATLAB and it is being analyzed that accuracy is increased upto 15 percent and execution time is reduce 2 seconds as compared to existing k-mean algorithm.

Keywords

Arithmetic Mean,Back Propagation,Clustering, K-mean, Neural Networks.

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References


  • Bharati M, Ramageri R. Data mining techniques and applications. Indian Journal of Computer Science and Engineering. 2004; 1(4):301–5.
  • Charu C, Aggarwal A, Chandan K, Reddy R. Data clustering: Algorithms and applications. Springer-Verlag Berlin Heidelberg; 2015.
  • Choudhary A. Survey on K-means and its variants International Journal of Innovative Research in Computer and Communication Engineering. 2016.
  • Coates A, Andrew YNG. Learning feature representations with K-means. Springer LNCS. 2012; 7700:561–80.
  • Babu GP, Murty MN. A near-optimal initial seed value selection in K-means algorithm using a genetic algorithm. Elsevier Science Publishers. 1993; 14(10):763–9.
  • Philip D, Khazenie HN. Classification of multispectral remote sensing data using a back-propagation neural network. IEEE Transactions on Geoscience and Remote Sensing. 1992; 30(1):1–15.
  • Chou C, Hsieh YZ, Su MC, Chu YL. Extracting and labeling the objects from an image by using the fuzzy clustering algorithm and a new cluster validity. International Journal of Computer and Communication Engineering. 2013; 2(3):1–3.
  • Joshi A, Kaur R. A review: Comparative study of various clustering techniques in data mining. International Journal of Advanced Research in Computer Science and Software Engineering. 2013; 3(3):1–3.
  • Singh A, Kaur N. To improve the convergence rate of K-means clustering over K-means with weighted page rank algorithm. International Journal of Advanced Research in Computer Science and Software Engineering. 2013; 3(8):1–5.
  • Jain A, Rajavat A, Bhartiya R. Design, analysis and implementation of modified K-mean algorithm for large data-set to increase scalability and efficiency. Fourth International Conference on Computational Intelligence and Communication Networks. 2012.
  • Santhanam T, Padmavathi MS. Application of K-Means and Genetic Algorithms for Dimension Reduction by Integrating SVM for Diabetes. Diagnosis Procedia Computer Science. 2012; 47:76–83.
  • Purwar A, Singh SK. Hybrid prediction model with missing value imputation for medical data. Expert Systems with Applications. 2015; 42(13):5621–31.
  • Poteras CM, Mihaescu MC, Mocanu M. An optimized version of the K-Means clustering algorithm. Federated Conference on Computer Science and Information Systems (FedCSIS), IEEE Romania; 2014. p. 695–9.
  • Kumari VA, Chitra R. Classification of diabetes disease using support vector machine. International Journal of Engineering Research and Applications. 2014; 3(2):1797–1801.
  • Nawi NMRS, Ransing R, Salleh MNM, Ghazali R, Hamid NA. An improved back propagation neural network algorithm on classification problems. Database Theory and Application, Bio-Science and Bio-Technology. 2010; 118:177–88.
  • Verma V, Bhardwaj S, Singh H. A hybrid K-mean clustering algorithm for prediction analysis. Indian Journal of Science and Technology. 2016 Jul; 9(28):1–5.
  • Kaur S, Kaur A. Detection of malware of code clone using string pattern back propagation neural network algorithm. Indian Journal of Science and Technology. 2016 Aug; 9(33):1–12.
  • Jayadurga R, Gunasundari R. A novel approach in vehicle object classification system with hybrid of central and hu moment features using back propagation algorithm. Indian Journal of Science and Technology. 2016 Jul; 9(26):1–7.
  • Ashok V, Singh SR, Nirmalkumar A. Determination of blood glucose concentration by back propagation neural network. Indian Journal of Science and Technology. 2010 Aug; 3(8):1–3.
  • Fathima KAR, Raghavendiran TA. A novel intelligent unified controller for the management of the Unified Power Flow Controller (UPFC) using a single back propagation feed forward artificial neural network. Indian Journal of Science and Technology. 2014 Jan; 7(8):1155–69.

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