An Internal Clustering Validation Index for Boolean Data

1. Elangasinghe, M. A., N. Singhal, K. N. Dirks et al. Complex Time Series Analysis of PM 10, and PM 2.5, for a Coastal Site Using Artificial Neural Network Modelling and k-Means Clustering. – Atmospheric Environment, Vol. 94, 2014, pp. 106-116.10.1016/j.atmosenv.2014.04.051Search in Google Scholar

2. Ferrandez, S. M., T. Harbison, T. Weber et al. Optimization of a Truck-Drone in Tandem Delivery Network Using k-Means and Genetic Algorithm. – Journal of Industrial Engineering & Management, Vol. 9, 2016, No 2, pp. 374-388.10.3926/jiem.1929Search in Google Scholar

3. Guan, N., D. Tao, Z. Luo et al. NeNMF: An Optimal Gradient Method for Nonnegative Matrix Factorization. – IEEE Transactions on Signal Processing, Vol. 60, 2012, No 6, pp. 2882-2898.10.1109/TSP.2012.2190406Search in Google Scholar

4. Niennattrakul, V., C. A. Ratanamahatana. On Clustering Multimedia Time Series Data Using k-Means and Dynamic Time Warping. – International Conference on Multimedia and Ubiquitous Engineering, IEEE, 2007, pp. 733-738.10.1109/MUE.2007.165Search in Google Scholar

5. Niennattrakul, V., C. A. Ratanamahatana. On Clustering Multimedia Time Series Data Using k-Means and Dynamic Time Warping. – International Conference on Multimedia and Ubiquitous Engineering, IEEE, 2007, pp. 733-738.10.1109/MUE.2007.165Search in Google Scholar

6. Rani, S., G. Sikka. Recent Techniques of Clustering of Time Series Data: A Survey. – International Journal of Computer Applications, Vol. 52, 2012, No 15, pp. 1-9.10.5120/8282-1278Search in Google Scholar

7. Liu, Y. Research on Internal Clustering Validation Measures. University of Science and Technology Beijing, 2012, pp. 16-20.Search in Google Scholar

8. Kremer, H., P. Kranen, T. Jansen et al. An Effective Evaluation Measure for Clustering on Evolving Data Streams. – In: Proc. of 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM, 2011, pp. 868-876.10.1145/2020408.2020555Search in Google Scholar

9. Feng, X., S. Wu, Y. Liu. Imputing Missing Values for Mixed Numeric and Categorical Attributes Based on Incomplete Data Hierarchical Clustering. – In: Proc. of International Conference on Knowledge Science, Engineering and Management, Springer Verlag, 2011, pp. 414-424.10.1007/978-3-642-25975-3_37Search in Google Scholar

10. Ralambondrainy, H. A Conceptual Version of the k-Means Algorithm. – Pattern Recognition Letters, Vol. 16, 1995, No 11, pp. 1147-1157.10.1016/0167-8655(95)00075-RSearch in Google Scholar

11. Liu, Y., Z. Li, H. Xiong et al. Understanding and Enhancement of Internal Clustering Validation Measures. – IEEE Transactions on Systems Man & Cybernetics Part B. Cybernetics A Publication of the IEEE Systems Man & Cybernetics Society, Vol. 43, 2012, No 3, pp. 982-994.10.1109/TSMCB.2012.222054323193245Search in Google Scholar

12. Kraus, J. M., C. Müssel, G. Palm et al. Multi-Objective Selection for Collecting Cluster Alternatives. – Computational Statistics, Vol. 26, 2011, No 2, pp. 341-353.10.1007/s00180-011-0244-6Search in Google Scholar

13. Zhang, G. X., L. Q. Pan. School of Electrical Engineering, University S. J., Chengdu. A Survey of Membrane Computing as a New Branch of Natural Computing. – Chinese Journal of Computers, Vol. 33, 2010, No 2, pp. 208-214.10.3724/SP.J.1016.2010.00208Search in Google Scholar

14. Busi, N. Using Well-Structured Transition Systems to Decide Divergence for Catalytic P Systems. – Theoretical Computer Science, Vol. 372, 2007, No 2-3, pp. 125-135.10.1016/j.tcs.2006.11.021Search in Google Scholar

15. Nishida, T. Y. An Approximate Algorithm for NP-Complete Optimization Problems Exploiting P Systems. – In: Proc. of 8th World Multi-Conference on Systems, Cybernetics and Information, 2004, pp. 109-112.Search in Google Scholar

16. Huang, L. Research on Membrane Computing Optimization Methods. – Zhejiang University, 2007.Search in Google Scholar

17. Huang, Z. A Fast Clustering Algorithm to Cluster Very Large Categorical Data Sets in Data Mining. – Research Issues on Data Mining & Knowledge Discovery, 1998, pp. 1-8.Search in Google Scholar

18. Wu, S., X. Gao. CABOSFV Algorithm for High Dimensional Sparse Data Clustering. – Journal of University Science & Technology Beijing, Vol. 11, 2004, No 3, pp. 283-288.Search in Google Scholar

19. Knops, Z. F., J. B. Maintz, M. A. Viergever et al. Normalized Mutual Information Based Registration Using k-Means Clustering and Shading Correction. – Medical Image Analysis, Vol. 10, 2006, No 3, pp. 432-439.10.1016/j.media.2005.03.00916111913Search in Google Scholar

20. Chen, L. F., Q. S. Jiang, S. R. Wang. A Hierarchical Method for Determining the Number of Clusters. – Journal of Software, Vol. 19, 2008, No 1, pp. 62-72.10.3724/SP.J.1001.2008.00062Search in Google Scholar