Systematic mapping study on granular computing

Knowledge-Based Systems

Volumn 80, Issue , 2015, Pages 78-97

  Salehi, Saber ^a   Selamat, Ali ^a   Fujita, Hamido ^b  

^a UNIVERSITI TEKNOLOGI MALAYSIA   (Malaysia)

^b IWATE PREFECTURAL UNIVERSITY   (Japan)

Author keywords

c means; Clustering algorithm; DBSCAN; Granular classifier; Granular computing; k means; Systematic mapping

Indexed keywords

CLASSIFICATION (OF INFORMATION); CLUSTER ANALYSIS; CLUSTERING ALGORITHMS; GRANULAR COMPUTING; GRANULATION; INFORMATION GRANULES; MAPPING;

C-MEANS; DBSCAN; GRANULAR CLASSIFIER; K-MEANS; SYSTEMATIC MAPPING;

K-MEANS CLUSTERING;

EID: 84926201555     PISSN: 09507051     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.knosys.2015.02.018     Document Type: Article

References (78)

1. L.A. Zadeh Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic Fuzzy Sets Syst. 90 1997 111 127
2. L.A. Zadeh Fuzzy logic = computing with words IEEE Trans. Fuzzy Syst. 4 1996 103 111
3. T.Y. Lin Granular computing: fuzzy logic and rough sets Computing with Words in Information/Intelligent Systems vol. 1 1999 Springer 183 200
4. J.T. Yao, A.V. Vasilakos, and W. Pedrycz Granular computing: perspectives and challenges IEEE Trans. Cybernet. 43 2013 1977 1989
5. J. Yao A ten-year review of granular computing IEEE International Conference on Granular Computing, 2007, GRC 2007 2007 IEEE 734-734
6. L. Wang, X. Liu, W. Pedrycz, and Y. Shao Determination of temporal information granules to improve forecasting in fuzzy time series Expert Syst. Appl. 41 2014 3134 3142
7. S.K. Pal, and S.K. Meher Paper: natural computing: a problem solving paradigm with granular information processing Appl. Soft Comput. 13 2013 3944 3955
8. H. Chen, T. Li, and D. Ruan Maintenance of approximations in incomplete ordered decision systems while attribute values coarsening or refining Knowl.-Based Syst. 31 2012 140 161
9. V.G. Kaburlasos, A. Kehagias, Fuzzy inference system (FIS) extensions based on lattice theory, 2013.
10. M.G. Cimino, B. Lazzerini, F. Marcelloni, and W. Pedrycz Genetic interval neural networks for granular data regression Inf. Sci. 257 2014 313 330
11. W. Lu, W. Pedrycz, X. Liu, J. Yang, and P. Li The modeling of time series based on fuzzy information granules Expert Syst. Appl. 41 2014 3799 3808
12. F.J. Cabrerizo, E. Herrera-Viedma, and W. Pedrycz A method based on PSO and granular computing of linguistic information to solve group decision making problems defined in heterogeneous contexts Euro. J. Operat. Res. 230 2013 624 633
13. A. Albanese, S.K. Pal, and A. Petrosino Rough sets, kernel set, and spatiotemporal outlier detection IEEE Trans. Knowl. Data Eng. 26 2014 194 207
14. X. Zhang, and D. Miao Quantitative information architecture, granular computing and rough set models in the double-quantitative approximation space of precision and grade Inf. Sci. 268 2014 147 168
15. X.-Q. Tang, and P. Zhu Hierarchical clustering problems and analysis of fuzzy proximity relation on granular space IEEE Trans. Fuzzy Syst. 21 2013 814 824
16. M.A. Sanchez, O. Castillo, J.R. Castro, and P. Melin Fuzzy granular gravitational clustering algorithm for multivariate data Inf. Sci. 279 2014 498 511
17. R. Davtalab, M.H. Dezfoulian, M. Mansoorizadeh, Multi-level fuzzy min-max neural network classifier, 2014.
18. D. Sánchez, and P. Melin Optimization of modular granular neural networks using hierarchical genetic algorithms for human recognition using the ear biometric measure Eng. Appl. Artif. Intell. 27 2014 41 56
19. D. Sanchez-Valdes, A. Alvarez-Alvarez, and G. Trivino Linguistic description about circular structures of the Mars' surface Appl. Soft Comput. 13 2013 4738 4749
20. P. Lingras, A. Elagamy, A. Ammar, and Z. Elouedi Iterative meta-clustering through granular hierarchy of supermarket customers and products Inf. Sci. 257 2014 14 31
21. H. Li, S. Yang, and H. Liu Study of qualitative data cluster model based on granular computing AASRI Proc. 4 2013 329 333
22. D.K. Chiu, and T.W. Lui NHOP: a nested associative pattern for analysis of consensus sequence ensembles IEEE Trans. Knowl. Data Eng. 25 2013 2314 2324
23. Y. Yao The art of granular computing Rough Sets and Intelligent Systems Paradigms 2007 Springer 101 112
24. Y. Yao Perspectives of granular computing 2005 IEEE International Conference on Granular Computing 2005 IEEE 85 90
25. J. Yao Information granulation and granular relationships 2005 IEEE International Conference on Granular Computing 2005 IEEE 326 329
26. K. Petersen, R. Feldt, S. Mujtaba, M. Mattsson, Systematic mapping studies in software engineering, in: 12th International Conference on Evaluation and Assessment in Software Engineering, 2008, p. 1.
27. S. Keele, Guidelines for Performing Systematic Literature Reviews in Software Engineering, Technical report, EBSE Technical Report EBSE-2007-01, 2007.
28. D. Budgen, M. Turner, P. Brereton, B. Kitchenham, Using mapping studies in software engineering, in: Proceedings of PPIG, 2008, pp. 195-204.
29. K. Bache, M. Lichman, UCI Machine Learning Repository < http://archive.ics.uci.edu/ml >, vol. 19, 2013.
30. V.G. Kaburlasos, and T. Pachidis A lattice-computing ensemble for reasoning based on formal fusion of disparate data types, and an industrial dispensing application Inf. Fusion 16 2014 68 83
31. M. Li, S. Deng, L. Wang, S. Feng, and J. Fan Hierarchical clustering algorithm for categorical data using a probabilistic rough set model Knowl.-Based Syst. 65 2014 60 71
32. Y. Lin, J. Li, P. Lin, G. Lin, and J. Chen Feature selection via neighborhood multi-granulation fusion Knowl.-Based Syst. 67 2014 162 168
33. J. Yao, and I. Global Novel developments in granular computing: applications for advanced human reasoning and soft computation Inf. Sci. Ref. 2010
34. L. Wang, X. Yang, J. Yang, and C. Wu Relationships among generalized rough sets in six coverings and pure reflexive neighborhood system Inf. Sci. 207 2012 66 78
35. Z. Zhang, and C. Guo A method for multi-granularity uncertain linguistic group decision making with incomplete weight information Knowl.-Based Syst. 26 2012 111 119
36. S. Mingli, and W. Pedrycz Granular neural networks: concepts and development schemes IEEE Trans. Neural Networks Learn. Syst. 24 2013 542 553
37. Y. Yao, and X. Deng A granular computing paradigm for concept learning Emerging Paradigms in Machine Learning 2013 Springer 307 326
38. A.R. Solis, and G. Panoutsos Granular computing neural-fuzzy modelling: a neutrosophic approach Appl. Soft Comput. 13 2013 4010 4021
39. H. Hu, L. Pang, D. Tian, and Z. Shi Perception granular computing in visual haze-free task Expert Syst. Appl. 41 2014 2729 2741
40. B. Huang, Y.-l. Zhuang, and H.-x. Li Information granulation and uncertainty measures in interval-valued intuitionistic fuzzy information systems Euro. J. Operat. Res. 231 2013 162 170
41. F. Seifi, H. Ahmadi, M. Kangavari, Twins decision tree classification: a sophisticated approach to decision tree construction, in: ICCSA, 2007, pp. 337-341.
42. A. Skowron, J. Bazan, and M. Wojnarski Interactive rough-granular computing in pattern recognition Pattern Recognition and Machine Intelligence 2009 Springer 92 97
43. Y. Yao, and L. Zhao A measurement theory view on the granularity of partitions Inf. Sci. 213 2012 1 13
44. T. Yang, Q. Li, and B. Zhou Related family: a new method for attribute reduction of covering information systems Inf. Sci. 228 2013 175 191
45. A. Skowron, J. Stepaniuk, and R. Swiniarski Modeling rough granular computing based on approximation spaces Inf. Sci. 184 2012 20 43
46. A.V. Nandedkar An interactive colour video segmentation: a granular computing approach Electrical Engineering and Intelligent Systems 2013 Springer 135 146
47. X. Yang, and J. Yang Neighborhood system and rough set in incomplete information system Incomplete Information System and Rough Set Theory 2012 Springer 101 130
48. H.S. Bhatt, S. Bharadwaj, R. Singh, and M. Vatsa Recognizing surgically altered face images using multiobjective evolutionary algorithm IEEE Trans. Inf. Foren. Sec. 8 2013 89 100
49. S.K. Pal, S.K. Meher, and S. Dutta Class-dependent rough-fuzzy granular space, dispersion index and classification Pattern Recog. 45 2012 2690 2707
50. A. Rosenfeld, L.S. Davis, Iterative histogram modification, in, IEEE-INST Electrical Electronics Engineers Inc., 345 E 47th St., New York, NY 10017-2394, 1978, pp. 300-302.
51. M.K. Kundu, and S.K. Pal Thresholding for edge detection using human psychovisual phenomena Patt. Recog. Lett. 4 1986 433 441
52. M. Ester, H.-P. Kriegel, J. Sander, X. Xu, A density-based algorithm for discovering clusters in large spatial databases with noise, in: Kdd, 1996, pp. 226-231.
53. T. Velmurugan Performance based analysis between k-Means and Fuzzy C-Means clustering algorithms for connection oriented telecommunication data Appl. Soft Comput. 19 2014 134 146
54. Y.W. Lim, and S.U. Lee On the color image segmentation algorithm based on the thresholding and the fuzzy c-means techniques Pattern Recog. 23 1990 935 952
55. R. Wieringa, N. Maiden, N. Mead, and C. Rolland Requirements engineering paper classification and evaluation criteria: a proposal and a discussion Require. Eng. 11 2006 102 107
56. P. Grzegorzewski Fuzzy number approximation via shadowed sets Inf. Sci. 225 2013 35 46
57. P. Hońko Relational pattern updating Inf. Sci. 189 2012 208 218
58. L. Wang, X. Liu, and W. Qiu Nearness approximation space based on axiomatic fuzzy sets Int. J. Approx. Reason. 53 2012 200 211
59. A.M. San Roque, C. Maté, J. Arroyo, and Á. Sarabia iMLP: Applying multi-layer perceptrons to interval-valued data Neural Process. Lett. 25 2007 157 169
60. W. Pedrycz, B.-J. Park, and S.-K. Oh The design of granular classifiers: a study in the synergy of interval calculus and fuzzy sets in pattern recognition Pattern Recog. 41 2008 3720 3735
61. S. Salehi, A. Selamat, R. Masinchi, and H. Fujita The synergistic combination of particle swarm optimization and fuzzy sets to design granular classifier Knowledge-Based Systems 2015
62. Y. Yao Interval sets and interval-set algebras 8th IEEE International Conference on Cognitive Informatics, 2009, ICCI'09, 2009 IEEE 307 314
63. B.Q. Hu Three-way decisions space and three-way decisions Inf. Sci. 2014
64. W. Pedrycz, R. Al-Hmouz, A. Morfeq, and A.S. Balamash Building granular fuzzy decision support systems Knowl.-Based Syst. 58 2014 3 10
65. P. Hońko Rough-granular computing based relational data mining Advances on Computational Intelligence 2012 Springer 290 299
66. M. Ye, X. Wu, X. Hu, and D. Hu Anonymizing classification data using rough set theory Knowl.-Based Syst. 43 2013 82 94
67. C. Liu, D. Miao, and J. Qian On multi-granulation covering rough sets Int. J. Approx. Reason. 55 2014 1404 1418
68. Y. Qian, H. Zhang, Y. Sang, and J. Liang Multigranulation decision-theoretic rough sets Int. J. Approx. Reason. 55 2014 225 237
69. W. Pedrycz, and G. Vukovich Granular computing with shadowed sets Int. J. Intell. Syst. 17 2002 173 197
70. S.-B. Roh, W. Pedrycz, and T.-C. Ahn A design of granular fuzzy classifier Expert Syst. Appl. 2014 66 78
71. W. Pedrycz, B. Russo, and G. Succi Knowledge transfer in system modeling and its realization through an optimal allocation of information granularity Appl. Soft Comput. 12 2012 1985 1995
72. G. Lin, Y. Qian, and J. Li NMGRS: neighborhood-based multigranulation rough sets Int. J. Approx. Reason. 53 2012 1080 1093
73. J. Liang, F. Wang, C. Dang, and Y. Qian An efficient rough feature selection algorithm with a multi-granulation view Int. J. Approx. Reason. 53 2012 912 926
74. W. Wei, J. Liang, and Y. Qian A comparative study of rough sets for hybrid data Inf. Sci. 190 2012 1 16
75. W. Zhu, and F.-Y. Wang The fourth type of covering-based rough sets Inf. Sci. 201 2012 80 92
76. J.W. Escobar, R. Linfati, and P. Toth A two-phase hybrid heuristic algorithm for the capacitated location-routing problem Comp. Operat. Res. 40 2013 70 79
77. U. Maulik, and S. Bandyopadhyay Genetic algorithm-based clustering technique Pattern Recog. 33 2000 1455 1465
78. S. Salehi, A. Selamat, and M. Bostanian Enhanced genetic algorithm for spam detection in email 2011 IEEE 2nd International Conference on Software Engineering and Service Science (ICSESS) 2011 IEEE 594 597