Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
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Mobile User Interface Pattern Clustering Using Improved Semi-Supervised Kernel Fuzzy Clustering Method

  • Jia, Wei (School of Information Science and Technology, Northwest University) ;
  • Hua, Qingyi (School of Information Science and Technology, Northwest University) ;
  • Zhang, Minjun (School of Information Science and Technology, Northwest University) ;
  • Chen, Rui (School of Information Science and Technology, Northwest University) ;
  • Ji, Xiang (School of Information Science and Technology, Northwest University) ;
  • Wang, Bo (School of Information Science and Technology, Northwest University)
  • Received : 2018.01.11
  • Accepted : 2018.07.02
  • Published : 2019.08.31
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Abstract

Mobile user interface pattern (MUIP) is a kind of structured representation of interaction design knowledge. Several studies have suggested that MUIPs are a proven solution for recurring mobile interface design problems. To facilitate MUIP selection, an effective clustering method is required to discover hidden knowledge of pattern data set. In this paper, we employ the semi-supervised kernel fuzzy c-means clustering (SSKFCM) method to cluster MUIP data. In order to improve the performance of clustering, clustering parameters are optimized by utilizing the global optimization capability of particle swarm optimization (PSO) algorithm. Since the PSO algorithm is easily trapped in local optima, a novel PSO algorithm is presented in this paper. It combines an improved intuitionistic fuzzy entropy measure and a new population search strategy to enhance the population search capability and accelerate the convergence speed. Experimental results show the effectiveness and superiority of the proposed clustering method.

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References

  1. T. Wetchakorn and N. Prompoon, "Method for mobile user interface design patterns creation for iOS platform," in Proceedings of 2015 12th International Joint Conference on Computer Science and Software Engineering (JCSSE), Songkhla, Thailand, 2015, pp. 150-155.
  2. T. D. Nguyen and J. Vanderdonckt, "User interface master detail pattern on Android," in Proceedings of the 4th ACM SIGCHI Symposium on Engineering Interactive Computing Systems, Copenhagen, Denmark, 2012, pp. 299-304.
  3. G. Thongmool and M. Phankokkruad, "Analysis of interaction user interface patterns and usability study in computer assisted instruction for Tablet PC," in Proceedings of 2014 IEEE International Conference on Control System, Computing and Engineering (ICCSCE), Batu Ferringhi, Malaysia, 2014, pp. 472-477.
  4. P. Gomes, F. C. Pereira, P. Paiva, N. Seco, P. Carreiro, J. L. Ferreira, and C. Bento, "Using CBR for automation of software design patterns," in Advances in Case-Based Reasoning. Heidelberg: Springer, 2002, pp. 534-548.
  5. L. Pavlic, V. Podgorelec, and M. Hericko, "A question-based design pattern advisement approach," Computer Science and Information Systems, vol. 11, no. 2, pp. 645-664, 2014. https://doi.org/10.2298/CSIS130824025P
  6. S. M. H. Hasheminejad and S. Jalili, "Design patterns selection: an automatic two-phase method," Journal of Systems and Software, vol. 85, no. 2, pp. 408-424, 2012. https://doi.org/10.1016/j.jss.2011.08.031
  7. M. Ester, H. P. Kriegel, J. Sander, and X. Xu, "A density-based algorithm for discovering clusters in large spatial databases with noise," in Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining, Portland, OR, 1996, pp. 226-231.
  8. G. Andrade, G. Ramos, D. Madeira, R. Sachetto, R. Ferreira, and L. Rocha, "G-DBSCAN: a GPU accelerated algorithm for density-based clustering," Procedia Computer Science, vol. 18, pp. 369-378, 2013. https://doi.org/10.1016/j.procs.2013.05.200
  9. T. Zhang, R. Ramakrishnan, and M. Livny, "BIRCH: an efficient data clustering method for very large databases," in Proceedings of the 1996 ACM SIGMOD International Conference on Management of Data, Montreal, Canada, 1996, pp. 103-114.
  10. R. T. Ng and J. Han, "CLARANS: a method for clustering objects for spatial data mining," IEEE Transactions on Knowledge & Data Engineering, vol. 14, no. 5, pp. 1003-1016, 2002. https://doi.org/10.1109/TKDE.2002.1033770
  11. W. Jia, Q. Hua, M. Zhang, R. Chen, and X. Ji, "User interface pattern language based on category theory," Journal of Computer Aided Design & Computer Graphics, vol. 29, no. 1, pp. 79-89, 2017.
  12. D. Q. Zhang and S. C. Chen, "Clustering incomplete data using kernel-based fuzzy c-means algorithm," Neural Processing Letters, vol. 18, no. 3, pp. 155-162, 2003. https://doi.org/10.1023/B:NEPL.0000011135.19145.1b
  13. D. Zhang, K. Tan, and S. Chen, "Semi-supervised kernel-based fuzzy c-means," in Neural Information Processing. Heidelberg: Springer, 2004, pp. 1229-1234.
  14. D. E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning. Reading, MA: Addison-Wesley, 1989.
  15. D. Bertsimas and J. Tsitsiklis, "Simulated annealing," Statistical Science, vol. 8, no. 1, pp. 10-15, 1993. https://doi.org/10.1214/ss/1177011077
  16. J. Kennedy and R. Eberhart, "Particle swarm optimization," in Proceedings of the IEEE International Conference on Neural Networks (ICNN), Perth, Australia, 1995, pp. 1942-1948.
  17. S. Yu, Y. M. Wei, J. Fan, X. Zhang, and K. Wang, "Exploring the regional characteristics of inter-provincial CO2 emissions in China: an improved fuzzy clustering analysis based on particle swarm optimization," Applied Energy, vol. 92, pp. 552-562, 2012. https://doi.org/10.1016/j.apenergy.2011.11.068
  18. A. Mekhmoukh and K. Mokrani, "Improved fuzzy c-means based particle swarm optimization (PSO) initialization and outlier rejection with level set methods for MR brain image segmentation," Computer Methods and Programs in Biomedicine, vol. 122, no. 2, pp. 266-281, 2015. https://doi.org/10.1016/j.cmpb.2015.08.001
  19. Y. Liu, F. Liu, T. Hou, and X. Zhang, "Kernel-based fuzzy C-means clustering method based on parameter optimization," Journal of Jilin University (Engineering and Technology Edition), vol. 46, no. 1, pp. 246-251, 2016.
  20. A. Sedki and D. Ouazar, "Hybrid particle swarm optimization and differential evolution for optimal design of water distribution systems," Advanced Engineering Informatics, vol. 26, no. 3, pp. 582-591, 2012. https://doi.org/10.1016/j.aei.2012.03.007
  21. R. Storn and K. Price, "Differential evolution: a simple and efficient heuristic for global optimization over continuous spaces," Journal of Global Optimization, vol. 11, no. 4, pp. 341-359, 1997. https://doi.org/10.1023/A:1008202821328
  22. F. Liu and Z. Zhou, "A new data classification method based on chaotic particle swarm optimization and least square-support vector machine," Chemometrics and Intelligent Laboratory Systems, vol. 147, pp. 147-156, 2015. https://doi.org/10.1016/j.chemolab.2015.08.015
  23. H. J. Lu, H. M. Zhang, and L. H. Ma, "A new optimization algorithm based on chaos," Journal of Zhejiang University-Science A, vol. 7, no. 4, pp. 539-542, 2006. https://doi.org/10.1631/jzus.2006.A0539
  24. Y. Dai, L. Liu, Y. Li, and J. Song, "An improved particle swarm optimisation based on cellular automata," International Journal of Computing Science and Mathematics, vol. 5, no. 1, pp. 94-106, 2014. https://doi.org/10.1504/IJCSM.2014.059385
  25. J. L. Schiff, Cellular Automata: A Discrete View of the World. Hoboken, NJ: John Wiley & Sons, 2007.
  26. Y. Z. Wang, Y. J. Lei, L. Zhou, and R. L. Li, "Intuitionistic fuzzy discrete particle swarm algorithm," Control and Decision, vol. 27, no. 11, pp. 1735-1739, 2012.
  27. Y. Wang and Y. J. Lei, "A technique for constructing intuitionistic fuzzy entropy," Control and Decision, ol. 22, no. 12, pp. 1390-1394, 2007. https://doi.org/10.3321/j.issn:1001-0920.2007.12.014
  28. H. R. Tizhoosh, "Opposition-based learning: a new scheme for machine intelligence," in Proceedings of International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCAIAWTIC' 06), Vienna, Austria, 2005, pp. 695-701.
  29. J. C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms. New York, NY: Plenum, 1981.
  30. K. T. Atanassov, "Intuitionistic fuzzy sets," Fuzzy Sets and Systems, vol. 20, no. 1, pp. 87-96, 1986. https://doi.org/10.1016/S0165-0114(86)80034-3
  31. W. Zeng and H. Li, "Relationship between similarity measure and entropy of interval valued fuzzy sets," Fuzzy Sets and Systems, vol. 157, no. 11, pp. 1477-1484, 2006. https://doi.org/10.1016/j.fss.2005.11.020
  32. J. Li, G. Deng, H. Li, and W. Zeng, "The relationship between similarity measure and entropy of intuitionistic fuzzy sets," Information Sciences, vol. 188, pp. 314-321, 2012. https://doi.org/10.1016/j.ins.2011.11.021
  33. R. Verma and B. D. Sharma, "Exponential entropy on intuitionistic fuzzy sets," Kybernetika, vol. 49, no. 1, pp. 114-127, 2013.
  34. C. P. Wei, Z. H. Gao, and T. T. Guo, "An intuitionistic fuzzy entropy measure based on trigonometric function," Control and Decision, vol. 27, no. 4, pp. 571-574, 2012.
  35. M. M. Gao, T. Sun, and J. J. Zhu, "An revised axiomatic definition and structural formula of intuitionistic fuzzy entropy," Control and Decision, vol. 29, no. 3, pp. 470-474, 2014.
  36. M. F. Liu and H. P. Ren, "A study of multi-attribute decision making based on a new intuitionistic fuzzy entropy measure," System Engineering Theory and Practice, vol. 35, no. 11, pp. 2909-2916, 2015.
  37. E. Szmidt and J. Kacprzyk, "Entropy for intuitionistic fuzzy sets," Fuzzy Sets and Systems, vol. 118, no. 3, pp. 467-477, 2001. https://doi.org/10.1016/S0165-0114(98)00402-3
  38. K. Guo and Q. Song, "On the entropy for Atanassov's intuitionistic fuzzy sets: an interpretation from the perspective of amount of knowledge," Applied Soft Computing, vol. 24, pp. 328-340, 2014. https://doi.org/10.1016/j.asoc.2014.07.006
  39. W. F. Gao and S. Y. Liu, "A modified artificial bee colony algorithm," Computers & Operations Research, vol. 39, no. 3, pp. 687-697, 2012. https://doi.org/10.1016/j.cor.2011.06.007
  40. Y. Zhou, L. Bao, and C. P. Chen, "A new 1D chaotic system for image encryption," Signal Processing, vol. 97, pp. 172-182, 2014. https://doi.org/10.1016/j.sigpro.2013.10.034
  41. D. L. Davies and D. W. Bouldin, "A cluster separation measure," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 1, no. 2, pp. 224-227, 1979.
  42. M. R. Rezaee, B. P. Lelieveldt, and J. H. Reiber, "A new cluster validity index for the fuzzy c-mean," Pattern Recognition Letters, vol. 19, no. 3-4, pp. 237-246, 1998. https://doi.org/10.1016/S0167-8655(97)00168-2
  43. T. Neil, Mobile Design Pattern Gallery: UI Patterns for Smartphone Apps. Sebastopol, CA: O'Reilly, 2012.
  44. S. Hoober and E. Berkman, Designing mobile interfaces. Sebastopol, CA: O'Reill, 2012.
  45. W. Y. Chen, Y. Song, H. Bai, C. J. Lin, and E. Y. Chang, "Parallel spectral clustering in distributed systems," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no.3, pp. 568-586, 2010. https://doi.org/10.1109/TPAMI.2010.88
  46. L. Hubert and P. Arabie, "Comparing partitions," Journal of Classification, vol. 2, no. 1, pp. 193-218, 1985. https://doi.org/10.1007/BF01908075
  47. A. Strehl and J. Ghosh, "Cluster ensembles: a knowledge reuse framework for combining multiple partitions," Journal of Machine Learning Research, vol. 3, pp. 583-617, 2002.
  48. C. H. Papadimitriou and K. Steiglitz, Combinatorial Optimization: Algorithms and Complexity. Englewood Cliffs, NJ: Prentice-Hall, 1982.