Total views : 259

Stock Market Prediction using Hierarchical Agglomerative and K-Means Clustering Algorithm


  • School of Computing, SASTRA University, Thirumalaisamudram, Thanjavur - 613401, Tamilnadu, India


Objectives: The stock market performance has more impact on national economy. The purpose of this work is to generate a portfolio to reduce the uncertainty of stock in short term basis. Methods: Hierarchical clustering is more efficient while non-determinism is of concern when compared with flat clustering. Hierarchical agglomerative Clustering is used, which results in more informative structure than flat clustering on unstructured data. Single-link clustering is taken into account as it does not pays more attention to outliers and amalgamation criterion is local than complete-link clustering and results in intuitive cluster structure. Dendrogram is used to represent the progressive formation of clusters in HAC. Findings: Flat clustering K-means algorithm is used to combine the clusters generated by Hierarchical agglomerative clustering (HAC). As the number of samples has been reduced, iterative use of k-means will choose better centroid. Applications: The final list of the recommended stocks is then showcased to the investor on short term basis. The baseline data is downloaded from National Stock Exchange (NSE).


Dendrogram, Hierarchical Agglomerative Clustering (HAC), Single-Link Clustering.

Full Text:

 |  (PDF views: 239)


  • Pauksto A, Raudys A. Intraday forex bid/ask spread patterns – analyzing and forecasting. IEEE Conference on Computational Intelligence for Financial Engineering & Economics (CIFEr). 2013.
  • Nair RB, Mohandas VP, Sakthivel NR. A Genetic algorithm optimized decision tree- SVM based stock market trend prediction system. (IJCSE) International Journal on Computer Science and Engineering. 2010; 2(9):2981-88.
  • Cheu EY, Kwoh CK, Zhou Z. On the two-level hybrid clustering algorithm. International Conference on Artificial Intelligence in Science and Technology. 2004; p. 138-42.
  • Cheung WS, Ng HS, Lam KP. Intraday stock price analysis and prediction. Proceedings of the IEEE International Conference on Management of Innovation and Technology.2000.
  • David E, Thawornwong S. The use of data mining and neural networks for forecasting Stock market returns. Expert Systems with Applications. 2005; 29(4):927–40.
  • Zhang D, Jiang Q, Li X. Application of Neural Networks in Financial Data Mining. World Academy of Science, Engineering and Technology. 2007; 1(1):225-28.
  • Yao J, Kong S. The application of stream data time series pattern reliance mining in stock market analysis. IEEE International Conference on Service Operations and Logistics, and Informatics. 2008.
  • Lam KP, Mok PY. Stock price prediction using intraday and AHIPMI data. Proceedings of the 9th International Conference on Neural Information Processing. 2002; 5:2167–71.
  • Voditel PP, Deshpande U. A stock market portfolio recommender system based on association rule mining. Applied Soft Computing. 2013; 13(2):1055-63.
  • Suresh Babu M, Geethanjali N, Satyanarayana B. Clustering approach to stock market prediction. Advanced Networking and Applications. 2012; 3(4):1281-91.
  • Liao SH, Ho HH, Lin HW. Mining stock category association and cluster on Taiwan stock market. Expert Systems with Applications. 2008; 35(1-2):19-29.
  • Hajizadeh E, Ardakani HD, Shahrabi J. Application of data mining techniques in stock markets: A survey.Journal of Economics and International Finance. 2010 Jul; 2(7):109-18.
  • Kambey S, Thakur RS, Jalori S. Applications of data mining technique in stock market (An analysis). International Journal of Computer & Communication Technology. 2012; 3(3):109-18.
  • Debashish D, Safa SA, Noraziah A. An Efficient Time Series Analysis for Pharmaceutical Sector Stock Prediction by Applying Hybridization of Data Mining and Neural Network Technique. Indian Journal of Science and Technology. 2016 Jun; 9(21):1-7.


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.