Data Clustering Method based on Mixed Similarity Measures

Doaa S. Ali, Ayman Ghoneim, Mohamed Saleh


Data clustering aims to organize data and concisely summarize it according to cluster prototypes. There are different types of data (e.g., ordinal, nominal, binary, continuous), and each has an appropriate similarity measure. However when dealing with mixed data set (i.e., a dataset that contains at least two types of data.), clustering methods use a unified similarity measure. In this study, we propose a novel clustering method for mixed datasets. The proposed mixed similarity measure (MSM) method uses a specific similarity measure for each type of data attribute. When computing distances and updating clusters’ centers, the MSM method merges between the advantages of k-modes and K-means algorithms. The ‎proposed MSM method is tested using benchmark real life datasets obtained from the UCI Machine Learning Repository. The MSM method performance is compared against other similarity methods whether in a non-evolutionary clustering setting or an evolutionary clustering setting (using differential evolution). Based on the experimental results, the ‎MSM method proved its efficiency in dealing with mixed datasets, and achieved significant improvement in the clustering performance in 80% of the tested datasets in the non-evolutionary clustering setting and in 90% of the tested datasets in the evolutionary clustering setting. The time and space complexity of our proposed method is analyzed, and the comparison with the other methods demonstrates the effectiveness of our method.


  1. Ahmad, Dey L., 2007, A k-mean clustering algorithm for mixed numeric and categorical data, Data & Knowledge Engineering, 63, pp. 503-527.
  2. Ammar E. Z., Lingras P., 2012, K-modes clustering using possibilistic membership, IPMU 2012, Part III, CCIS 299, pp. 596-605.
  3. Aranganayagi S., Thangavel K., 2009, Improved Kmodes for categorical clustering using weighted dissimilarity measure, International Journal of Computer, Electrical, Automation, Control and Information Engineering, 3 (3), pp. 729-735.
  4. Arbelaitz O., Gurrutxaga I., Muguerza J., Rez J. M., Perona I., 2013, An extensive comparative study of cluster validity indices, Pattern Recognition (46), pp. 243-256.
  5. Asadi S., Rao S., Kishore C., Raju Sh., 2012, Clustering the mixed numerical and categorical datasets using similarity weight and filter method, International Journal of Computer Science, Information Technology and Management, 1 (1-2).
  6. Baghshah M. S., Shouraki S. B., 2009, Semi-supervised metric learning using pairwise constraints, Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence (IJCAI), pp. 1217-1222.
  7. Bai L., Lianga J., Dang Ch., Cao F., 2013, A novel fuzzy clustering algorithm with between-cluster information for categorical data, Fuzzy Sets and Systems, 215, pp. 55-73.
  8. Bai L., Liang J., Sui Ch., Dang Ch., 2013, Fast global kmeans clustering based on local geometrical information, Information Sciences, 245, pp. 168-180.
  9. Bhagat P. M., Halgaonkar P. S., Wadhai V. M., 2013, Review of clustering algorithm for categorical data, International Journal of Engineering and Advanced Technology, 3 (2).
  10. Blake, C., Merz, C., 1998. UCI repository machine learning datasets.
  11. Boriah Sh., Chandola V., Kumar V., 2008, Similarity measures for categorical data: A comparative evaluation. The Eighth SIAM International Conference on Data Mining. pp. 243-254.
  12. Cha S., 2007, Comprehensive survey on distance/similarity measures between probability density functions, International journal of mathematical models and methods in applied sciences, 1(4), pp. 300-307.
  13. Gibson D., Kleinberg J., Raghavan P., 1998, Clustering categorical data: An approach based on dynamical systems, In 24th International Conference on Very Large Databases, pp. 311-322.
  14. Kim K.K., Hyunchul A., 2008, A recommender system using GA K-means clustering in an online shopping market, Elsevier Journal, Expert Systems with Applications 34, pp. 1200-1209.
  15. Kumar N., Kummamuru K., 2008, Semi-supervised clustering with metric learning using relative comparisons, IEEE Transactions on Knowledge and Data Engineering, 20 (4), pp. 496-503.
  16. Mukhopadhyay A., Maulik U., 2007, Multiobjective approach to categorical data clustering, IEEE Congress on Evolutionary Computation, pp. 1296 - 1303.
  17. Mutazinda H., Sowjanya M., Mrudula O., 2015, Cluster ensemble approach for clustering mixed data, International Journal of Computer Techniques, 2 (5), pp. 43-51.
  18. Parameswari P., Abdul Samath J., Saranya S., 2015, Scalable clustering using rank based preprocessing technique for mixed data sets using enhanced rock algorithm, African Journal of Basic & Applied Sciences, 7 (3), pp. 129-136.
  19. Pinisetty V.N. P., Valaboju R., Rao N. R., 2012, Hybrid algorithm for clustering mixed data sets, IOSR Journal of Computer Engineering, 6, pp 9-13.
  20. Saha, D. PlewczÉnski, Maulik U., Bandyopadhyay S., 2010, Consensus multiobjective differential crisp clustering for categorical data analysis, RSCTC, LNAI 6086, pp. 30-39.
  21. Serapião B. S., Corrêa G. S. , Gonçalves F. B. , Carvalho V. O., 2016, Combining K-means and K-harmonic with fish school search algorithm for data clustering task on graphics processing units, Applied Soft Computing, 41, pp. 290-304.
  22. Shih M., Jheng J., Lai L., 2010, A two-step method for clustering mixed categroical and numeric data, Tamkang Journal of Science and Engineering, 13 (1), pp. 11-19.
  23. Soundaryadevi M., Jayashree L.S., 2014, Clustering of data with mixed attributes based on unified similarity metric, Proceedings of International Conference On Global Innovations In Computing Technology, pp. 1865-1870.
  24. Suresh K., Kundu D., Ghosh S. , Das S., Han, Y. S., 2009, Multi-Objective Differential Evolution for Automatic Clustering with Application to Micro-Array Data Analysis, Sensors, 9(5), pp. 3981-4004.
  25. Tasdemir K., Merényi E., 2011, A validity index for prototype-based clustering of data sets with complex cluster structures, IEEE transactions on systems, man, and cybernetics-part b, 41(4), pp. 1039-1053.
  26. Tiwari M., Jha M. B., 2012, Enhancing the performance of data mining algorithm in letter image recognition data, International Journal of Computer Applications in Engineering Sciences, II (III), pp. 217-220.
  27. Wu X., Kumar V., Quinlan J. R., Ghosh J., Yang Q. , Motoda H., McLachlan G. J., Ng A., Liu B. , Yu Ph. S., Zhou Zh., Steinbach M., Hand D. J., Steinberg D., 2008, Top 10 algorithms in data mining, Knowledge Information System, 14, pp. 1-37.
  28. Xing E., 2003, Distance metric learning with application to clustering with side-information, in NIPS, pp. 505- 512.

Paper Citation

in Harvard Style

S. Ali D., Ghoneim A. and Saleh M. (2017). Data Clustering Method based on Mixed Similarity Measures . In Proceedings of the 6th International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES, ISBN 978-989-758-218-9, pages 192-199. DOI: 10.5220/0006245601920199

in Bibtex Style

author={Doaa S. Ali and Ayman Ghoneim and Mohamed Saleh},
title={Data Clustering Method based on Mixed Similarity Measures},
booktitle={Proceedings of the 6th International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES,},

in EndNote Style

JO - Proceedings of the 6th International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES,
TI - Data Clustering Method based on Mixed Similarity Measures
SN - 978-989-758-218-9
AU - S. Ali D.
AU - Ghoneim A.
AU - Saleh M.
PY - 2017
SP - 192
EP - 199
DO - 10.5220/0006245601920199