A Clustering Algorithm of High-Dimensional Data Based on Sequential Psim Matrix and Differential Truncation

  • Gongming WangEmail author
  • Wenfa Li
  • Weizhi Xu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11335)


For high-dimensional data, the failure in distance calculation and the inefficient index tree that are respectively derived from equidistance and redundant attribute, have affected the performance of clustering algorithm seriously. To solve these problems, this paper introduces a clustering algorithm of high-dimensional data based on sequential Psim matrix and differential truncation. Firstly, the similarity of high-dimensional data is calculated with Psim function, which avoids the equidistance. Secondly, the data is organized with sequential Psim matrix, which improves the indexing performance. Thirdly, the initial clusters are produced with differential truncation. Finally, the K-Medoids algorithm is used to refine cluster. This algorithm was compared with K-Medoids and spectral clustering algorithms in two types of datasets. The experiment result indicates that our proposed algorithm reaches high value of Macro-F1 and Micro-F1 at the small number of iterations.


High-dimensional data Clustering Psim Differential truncation Heuristic search K-Medoids Spectral clustering 



This work is partly supported by the National Nature Science Foundation of China (No. 61502475, 61602285) and the Importation and Development of High-Caliber Talents Project of the Beijing Municipal Institutions (No. CIT & TCD201504039).


  1. 1.
    Han, J.W., Kamber, H.L., Pei, J.: Data Mining: Concepts and Techniques, 3rd edn. Morgan Kaufmann, San Francisco (2011)zbMATHGoogle Scholar
  2. 2.
    Ericson, K.L., Pallickara, S.D.: On the performance of high dimensional data clustering and classification algorithms. Future Gener. Comput. Syst. 29(4), 1024–1034 (2013)CrossRefGoogle Scholar
  3. 3.
    Keogh, E., Mueen, A.: Curse of dimensionality. In: Encyclopedia of Machine Learning, pp. 257–258. Springer, Berlin (2010)Google Scholar
  4. 4.
    Yang, Q., Wu, X.D.: 10 Challenging problems in data mining research. Int. J. Inf. Technol. Decis. Making 5(4), 597–604 (2006)CrossRefGoogle Scholar
  5. 5.
    Berkhin, P.: A survey of clustering data mining techniques. In: Kogan, J., Nicholas, C., Teboulle, M. (eds.) Grouping Multidimensional Data, pp. 25–71. Springer, Heidelberg (2006). Scholar
  6. 6.
    Parsons, L., Haque, E.S., Liu, H.: Subspace clustering for high dimensional data: a review. ACM SIGKDD Explor. Newsl. 6(1), 90–105 (2004)CrossRefGoogle Scholar
  7. 7.
    Dhillon, I.S.: Co-clustering documents and words using bipartite spectral graph partitioning. In: 7th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 269–274. ACM Press, New York (2001)Google Scholar
  8. 8.
    Fu, Q., Li, Z.F.: The research of clustering algorithm based on CLIQUE. J. East China Jiaotong Univ. 23(5), 79–82 (2006)Google Scholar
  9. 9.
    Feng, Z.H., Zhou, B., Shen, J.Y.: A parallel hierarchical clustering algorithm for PCs cluster system. Neurocomputing 70, 809–818 (2007)CrossRefGoogle Scholar
  10. 10.
    Du, Z., Lin, F.: A novel parallelization approach for hierarchical clustering. Parallel Comput. 31, 523–527 (2005)CrossRefGoogle Scholar
  11. 11.
    Wu, H.Y., Wang, W.T., Wen, J.H., He, G.H.: Research on clustering algorithm of high-dimensional dataset with input knowledge. Comput. Sci. 33(1), 240–242 (2006)Google Scholar
  12. 12.
    Yi, L.H.: Research on clustering algorithm for high dimensional data. Master’s thesis, Yan Shan University, Qinhuangdao Hebei, China (2011)Google Scholar
  13. 13.
    Tan, P.N., Steinbach, M., Kumar, V.: Introduction to Data Mining. Addison-Wesley Publishing Company, Boston (2005)Google Scholar
  14. 14.
    Yang, F.Z., Zhu, Y.Y.: An efficient method for similarity search on quantitative transaction data. J. Comput. Res. Dev. 41(2), 361–368 (2004)Google Scholar
  15. 15.
    Huang, S.D., Chen, Q.M.: On clustering algorithm of high dimensional data based on similarity measurement. Comput. Appl. Softw. 26(9), 102–105 (2009)Google Scholar
  16. 16.
    Shao, C.S., Lou, W., Yan, L.M.: Optimization of algorithm of similarity measurement in high-dimensional data. Comput. Technol. Dev. 21(2), 1–4 (2011)Google Scholar
  17. 17.
    Wang, X.Y., Zhang, H.Y., Shen, L.Z., Chi, W.L.: Research on high dimensional clustering algorithm based on similarity measurement. Comput. Technol. Dev. 23(5), 30–33 (2013)Google Scholar
  18. 18.
    Jia, X.Y.: A high dimensional data clustering algorithm based on twice similarity. J. Comput. Appl. 25(B12), 176–177 (2005)Google Scholar
  19. 19.
    Brakatsoulas, S., Pfoser, D., Theodoridis, Y.: Revisiting R-tree construction principles. In: Manolopoulos, Y., Návrat, P. (eds.) ADBIS 2002. LNCS, vol. 2435, pp. 149–162. Springer, Heidelberg (2002). Scholar
  20. 20.
    Nielsen, F., Piro, P., Barlaud, M.: Bregman vantage point trees for efficient nearest Neighbor Queries. In: 10th IEEE International Conference on Multimedia and Expo, pp. 878–881. IEEE Computer Society, Birmingham (2009)Google Scholar
  21. 21.
    Kunze, M., Weske, M.: Metric trees for efficient similarity search in large process model repositories. Lect. Notes Bus. Inf. Process. 66, 535–546 (2011)CrossRefGoogle Scholar
  22. 22.
    Navarro, G.Z.: Searching in metric spaces by spatial approximation. VLDB J. 11(1), 28–46 (2002)CrossRefGoogle Scholar
  23. 23.
    Chen, J.B.: The Research and Application of Key Technologies in Knowledge Discovery of High-Dimensional Clustering. Publishing House of Electronics Industry, Beijing (2011)Google Scholar
  24. 24.
    Andrew, Y.N., Jordan, M.I., Weiss, Y.: On spectral clustering: analysis and algorithm. In: Advances in Neural Information Processing Systems, pp. 121–526. MIT Press, Cambridge (2002)Google Scholar
  25. 25.
    Raymond, T.N., Han, J.W.: Efficient and effective clustering methods for spatial data mining. In: 20th International Conference on Very Large Data Bases, pp. 144–155. IEEE Computer Society, Birmingham (1994)Google Scholar
  26. 26.
    Chen, L.F., Ye, Y.F., Jiang, Q.S.: A new centroid-based classifier for text categorization. In: 22nd IEEE International Conference on Advanced Information Networking and Applications, pp. 1217–1222. IEEE Computer Society, Birmingham (2008)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Institute of BiophysicsChinese Academy of SciencesBeijingChina
  2. 2.College of Information TechnologyBeijing Union UniversityBeijingChina
  3. 3.School of Information Science and EngineeringShandong Normal UniversityJinanChina

Personalised recommendations