Granular Mining and Big Data Analytics: Rough Models and Challenges

  • Sankar K. PalEmail author
Review Article


Data analytics in granular computing framework is considered for several mining applications, such as in video analysis, bioinformatics and online social networks which have all the characteristics of Big data. The role of granulation, lower approximation and rf information measure is exhibited. While the lower approximation over a video sequence signifies the object model for unsupervised tracking, it characterizes the probability (relative frequency) of definite regions in ranking miRNAs for normal and cancer classification. For neural learning, the information on definite region is used as the initial knowledge for encoding while generating the networks through evolution. Granules considered are of different sizes and dimensions with fuzzy and crisp boundaries. The tracking method is effective in handling different ambiguous situations, e.g., overlapping objects, newly appeared object(s), multiple objects in different directions and speeds, in unsupervised mode. The ranking algorithm could find only 1% miRNAs to result in significantly higher F-score than the entire set. Fuzzy–rough communities detected over the granular model of social networks are suitable in dealing with overlapping virtual communities in Big data. The knowledge encoding based on fuzzy–rough set provides superior performance than that of rough set. Future directions of research and challenges including the significance of z-numbers in precisiation of granules are stated. The article includes some of the results published elsewhere.


Granular computing Fuzzy–rough sets Data mining Bioinformatics Video tracking Social network analysis Neural networks z-Numbers Granulated deep learning 



The author acknowledges the DAE Raja Ramanna Fellowship and Sir J.C. Bose Fellowship of the Govt. of India. A part of the work was done while he held an INSA Distinguished Professorship Chair.


  1. 1.
    Pal SK, Meher SK (2013) Natural computing: a problem solving paradigm with granular information processing. Appl Soft Comput 13(9):3944–3955CrossRefGoogle Scholar
  2. 2.
    Zadeh LA (1997) Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy Sets Syst 90:111–127MathSciNetzbMATHCrossRefGoogle Scholar
  3. 3.
    Pawlak Z (1982) Rough sets. Int J Comput Inf Sci 11:341–356zbMATHCrossRefGoogle Scholar
  4. 4.
    Pawlak Z (1991) Rough sets: theoretical aspects of reasoning about data. Kluwer Academic, DordrechtzbMATHCrossRefGoogle Scholar
  5. 5.
    Zadeh LA (1996) Fuzzy logic = computing with words. IEEE Trans Fuzzy Syst 4(2):103–111CrossRefGoogle Scholar
  6. 6.
    Pedrycz W (2001) Granular computing: an emerging paradigm. Physica-Verlag, HeidelbergzbMATHCrossRefGoogle Scholar
  7. 7.
    Polkowski L, Skowron A (1998) Towards adaptive calculus of granules. In: Proceedings of the 7th IEEE international conference on fuzzy system, Anchorage, AK, USA, May 1998, pp 111–116Google Scholar
  8. 8.
    Yao JT, Vasilakos AV, Pedrycz W (2013) Granular computing: perspectives and challenges. IEEE Trans Cybern 43(6):1977–1989CrossRefGoogle Scholar
  9. 9.
    Komorouski J, Pawlak Z, Polkowski L, Skowron A (1999) Rough sets: a tutorial. In: Pal SK, Skowron A (eds) Rough fuzzy hybridization: a new trend in decision making. Springer, Singapore, pp 3–98Google Scholar
  10. 10.
    Pawlak Z, Skowron A (2007) Rudiments of rough sets. Inf Sci 177:3–27MathSciNetzbMATHCrossRefGoogle Scholar
  11. 11.
    Sen D, Pal SK (2009) Generalized rough sets, entropy and image ambiguity measures. IEEE Trans Syst Man Cybern Part B 39(1):117–128CrossRefGoogle Scholar
  12. 12.
    Pal SK (2012) Granular mining and rough–fuzzy pattern recognition: a way to natural computation, (Feature Article). IEEE Intell Inf Bull 13(1):3–13MathSciNetGoogle Scholar
  13. 13.
    Pal SK, Mitra P (2004) Case generation using rough sets with fuzzy discretization. IEEE Trans Knowl Data Eng 16(3):292–300CrossRefGoogle Scholar
  14. 14.
    Qian Y, Lian J, Yao Y, Dang C (2010) MGRS: a multi-granulation rough set. Inf Sci 180:949–970MathSciNetzbMATHCrossRefGoogle Scholar
  15. 15.
    Pal SK, Meher SK, Dutta S (2012) Class-dependent rough–fuzzy granular space, dispersion index and classification. Pattern Recognit 45(7):2690–2707CrossRefGoogle Scholar
  16. 16.
    Zhu W, Wang FY (2007) On three types of covering-based rough sets. IEEE Trans Knowl Data Eng 19(8):1649–1667CrossRefGoogle Scholar
  17. 17.
    Maggio E, Cavallaro A (2010) Video tracking: theory and practice. Wiley, New YorkzbMATHGoogle Scholar
  18. 18.
    Pal SK, Petrosino A, Maddalena L (eds) (2012) Handbook on soft computing for video surveillance. CRC Press, Boca RatonzbMATHGoogle Scholar
  19. 19.
    Pal SK, Bhunia Chakraborty D (2017) Granular flow graph, adaptive rule generation and tracking. IEEE Trans Cybern 47(12):4096–4107CrossRefGoogle Scholar
  20. 20.
    Pawlak Z (2005) Flow graphs and data mining. Springer, HeidelbergzbMATHCrossRefGoogle Scholar
  21. 21.
    Bhunia Chakraborty D, Pal SK (2016) Neighborhood granules and rough rule-base in tracking. Natural Computing (special issue on pattern recognition and mining), Springer, vol. 15, no. 3, pp 359–370Google Scholar
  22. 22.
    Chakraborty D, Uma Shankar B, Pal SK (2013) Granulation, rough entropy and spatiotemporal moving object detection. Appl Soft Comput 13(9):4001–4009CrossRefGoogle Scholar
  23. 23.
    Bhunia Chakraborty D, Pal SK (2018) Neighborhood rough filter and intuitionistic entropy in unsupervised tracking. IEEE Trans Fuzzy Syst 26:2188–2200. CrossRefGoogle Scholar
  24. 24.
    Yilmaz A, Javed O, Shah M (2006) Object tracking: a survey. ACM Comput Surv 38:1264–1291CrossRefGoogle Scholar
  25. 25.
    Comaniciu D, Ramesh V, Meer P (2003) Kernel-based object tracking. IEEE Trans Pattern Anal Mach Intell 25:564–577CrossRefGoogle Scholar
  26. 26.
    Cucchiara R, Grana C, Piccardi M, Prati A (2003) Detecting moving objects, ghosts and shadows in video streams. IEEE Trans Pattern Anal Mach Intell 25:1337–1342CrossRefGoogle Scholar
  27. 27.
    Fang H, Jiang J, Feng Y (2006) A fuzzy logic approach for detection of video shot boundaries. Pattern Recognit 39:2092–2100zbMATHCrossRefGoogle Scholar
  28. 28.
    Pan P, Schonfeld D (2011) Video tracking based on sequential particle filtering on graphs. IEEE Trans Image Process 20(6):1641–1651ADSMathSciNetzbMATHCrossRefGoogle Scholar
  29. 29.
    Shen C, Kim J, Wang H (2010) Generalized kernel-based visual tracking. IEEE Trans Circuits Syst Video Technol 20:119–130CrossRefGoogle Scholar
  30. 30.
    Maddalena L, Petrosino A, Ferone A (2008) Object motion detection and tracking by an artificial intelligence approach. Int J Patt Recognit Artif Intell 22:915–928CrossRefGoogle Scholar
  31. 31.
    Dai S, Ren W, Gu F, Huang H, Chang S (2008) Implementation of robot visual tracking system based on rough set theory. In: Proceedings of the fifth international conference on fuzzy systems and knowledge discovery (FSKD 2008), IEEE Computer Society, vol 2, pp 155–160Google Scholar
  32. 32.
    Zhang K, Zhang L, Yang M-H (2014) Fast compressive tracking. IEEE Trans Pattern Anal Mach Intell 36:2002–2015CrossRefGoogle Scholar
  33. 33.
    Huang C-M, Fu L-C (2011) Multitarget visual tracking based effective surveillance with cooperation of multiple active cameras. IEEE Trans Syst Man Cybern B Cybern 41L:234–247CrossRefGoogle Scholar
  34. 34.
    Nawaz T, Poiesi F, Cavallaro A (2014) Measures of effective video tracking. IEEE Trans Image Process 23:376–388ADSMathSciNetzbMATHCrossRefGoogle Scholar
  35. 35.
    Pal JK, Ray SS, Chow SB, Pal SK (2018) Fuzzy-rough entropy measure and histogram based patient selection for miRNA ranking in cancer. IEEE/ACM Trans Comput Biol Bioinf 15(2):659–672CrossRefGoogle Scholar
  36. 36.
    Pal JK, Ray SS, Pal SK (2016) Identifying relevant group of miRNAs in cancer using fuzzy mutual information”. Med Biol Eng Comput 54(4):701–710CrossRefGoogle Scholar
  37. 37.
    Pal JK, Ray SS, Pal SK (2017) Fuzzy mutual information based grouping and new fitness function for PSO in selection of miRNAs in cancer. Comput Biol Med 89:540–548CrossRefGoogle Scholar
  38. 38.
    Maji P, Pal SK (2010) Fuzzy-rough sets for information measures and selection of relevant genes from microarray data. IEEE Trans Syst Man Cybern Part B Cybern 40(3):741–752CrossRefGoogle Scholar
  39. 39.
    Yu L, Han Y, Berens ME (2012) Stable gene selection from microarray data via sample weighting. IEEE/ACM Trans Comput Biol Bioinf 9:262–272CrossRefGoogle Scholar
  40. 40.
    Sehhati M, Mehridehnavi S, Rabbani H, Pourhossien M (2015) Stable gene signature selection for prediction of breast cancer recurrence using joint mutual information. IEEE/ACM Trans Comput Biol Bioinf 12:1440–1447CrossRefGoogle Scholar
  41. 41.
    Guyon J, Weston S, Barnhill V (2002) Vapnik, Gene selection for cancer classification using support vector machines. Mach Learn 46:389–422zbMATHCrossRefGoogle Scholar
  42. 42.
    Peng H, Long F, Ding C (2005) Feature selection based on mutual information: criteria of max-dependency, max-relevance and min-redundancy. IEEE Trans Pattern Anal Mach Intell 27:1226–1238CrossRefGoogle Scholar
  43. 43.
    Mundra PA, Rajapakse JC (2010) SVM-RFE with MRMR filter for gene selection. IEEE Trans Nanobiosci 9:31–37CrossRefGoogle Scholar
  44. 44.
    Mitra P, Murthy CA, Pal SK (2012) Unsupervised feature selection using feature similarity. IEEE Trans. Pattern Anal Mach Intell 24:301–312CrossRefGoogle Scholar
  45. 45.
    Arndt GM, Dossey L, Cullen LM, Lai A, Druker R, Eisbacher M, Zhang C, Tran N, Fan H, Retzlaff K, Bittner A, Raponi M (2009) Characterization of global microRNA expression reveals oncogenic potential of mir-145 in metastatic colorectal cancer. BMC Cancer 9:1–17CrossRefGoogle Scholar
  46. 46.
    Leidinger P et al (2010) High-throughput miRNA profiling of human melanoma blood samples. BMC Cancer 10:1–11CrossRefGoogle Scholar
  47. 47.
    Kundu S (2016) Granular model for social networks, target selection and fuzzy-rough community detection, Ph.D. Dissertation, Jadavpur University, Kolkata, IndiaGoogle Scholar
  48. 48.
    Kundu S, Pal SK (2015) FGSN: fuzzy granular social networks—model and applications. Inf Sci 314:100–117CrossRefGoogle Scholar
  49. 49.
    Kundu S, Pal SK (2015) Fuzzy-rough community in social networks. Pattern Recognit Lett 67(2):145–152CrossRefGoogle Scholar
  50. 50.
    Girvan M, Newman MEJ (2002) Community structure in social and biological networks. Proc Natl Acad Sci USA 99(12):7821–7826ADSMathSciNetzbMATHCrossRefGoogle Scholar
  51. 51.
    Newman MEJ (2004) Fast algorithm for detecting community structure in networks. Phys Rev E 69(6):066133ADSCrossRefGoogle Scholar
  52. 52.
    Palla G, Derényi I, Farkas I, Vicsek T (2005) Uncovering the overlapping community structure of complex networks in nature and society. Nature 435(7043):814–818ADSCrossRefGoogle Scholar
  53. 53.
    Boorman SA, White HC (1976) Social structure from multiple networks. II. Role structures social structure from multiple networks. Am J Sociol 81:1384–1446CrossRefGoogle Scholar
  54. 54.
    Davis GB, Carley KM (2008) Clearing the FOG: fuzzy, overlapping groups for social networks. Soc Netw 30:201–212CrossRefGoogle Scholar
  55. 55.
    Newman M, Girvan M (2004) Finding and evaluating community structure in networks. Phys Rev E 69:1–15Google Scholar
  56. 56.
    Chattopadhyay S, Murthy CA, Pal SK (2014) Fitting truncated geometric distributions in large scale real world networks. Theoret Comput Sci 551:22–28MathSciNetzbMATHCrossRefGoogle Scholar
  57. 57.
    Malliarosa FD, Vazirgiannis M (2013) Clustering and community detection in directed networks: a survey. Phys Rep 533:95–142ADSMathSciNetzbMATHCrossRefGoogle Scholar
  58. 58.
    Weiss RS, Jacobson E (1955) A method for the analysis of the structure of complex organizations. Am Sociol Assoc 20:661–668CrossRefGoogle Scholar
  59. 59.
    Ganivada A, Dutta S, Pal SK (2011) Fuzzy rough granular neural networks, fuzzy granules and classification. Theor Comput Sci Part C 412(42):5834–5853MathSciNetzbMATHCrossRefGoogle Scholar
  60. 60.
    Banerjee M, Mitra S, Pal SK (1998) Rough fuzzy MLP: knowledge encoding and classification. IEEE Trans Neural Netw 9(6):1203–1216CrossRefGoogle Scholar
  61. 61.
    Ganivada A, Ray SS, Pal SK (2012) Fuzzy rough granular self-organizing map and fuzzy rough entropy. Theoret Comput Sci 466:37–63MathSciNetzbMATHCrossRefGoogle Scholar
  62. 62.
    Ray SS, Ganivada A, Pal SK (2016) A granular self-organizing map for clustering and gene selection in microarray data. IEEE Trans Neural Netw Learn Syst 27(9):1890–1906MathSciNetCrossRefGoogle Scholar
  63. 63.
    Ganivada A, Ray SS, Pal SK (2013) Fuzzy rough sets, and a granular neural network for unsupervised feature selection. Neural Netw 48:91–108zbMATHCrossRefGoogle Scholar
  64. 64.
    Pal SK, Ray SS, Ganivada A (2017) Granular neural networks, pattern recognition and bioinformatics. Springer, BerlinCrossRefGoogle Scholar
  65. 65.
    Zhang YQ, Jin B, Tang Y (2008) Granular neural networks with evolutionary interval learning. IEEE Trans Fuzzy Syst 16:309–319CrossRefGoogle Scholar
  66. 66.
    Pal SK, Dasgupta B, Mitra P (2004) Rough self organizing map. Appl Intel 21:289–299zbMATHCrossRefGoogle Scholar
  67. 67.
    Yeung DS, Chen D, Tsang ECC, Lee JWT, Xizhao W (2005) On the generalization of fuzzy rough sets. IEEE Trans Fuzzy Syst 13:343–361CrossRefGoogle Scholar
  68. 68.
    Banerjee M, Pal SK (1996) Roughness of a fuzzy set. Inf Sci 93:235–246MathSciNetzbMATHCrossRefGoogle Scholar
  69. 69.
    Pal SK, Meher SK, Skowron A (2015) Data science, big data and granular mining. Pattern Recognit Lett 67(2):109–112CrossRefGoogle Scholar
  70. 70.
    Zadeh LA (2001) A new direction in AI: toward a computational theory of perceptions. AI Magazine 22:73–84zbMATHGoogle Scholar
  71. 71.
    Pal SK, Banerjee R (2013) Context granularization and subjective-information quantification. Theoret Comput Sci 448:2–14zbMATHCrossRefGoogle Scholar
  72. 72.
    Zadeh LA (2011) A note on Z-numbers. Inf Sci 18(14):2923–2932zbMATHCrossRefGoogle Scholar
  73. 73.
    Banerjee R, Pal SK (2015) Z*-numbers: augmented Z-numbers for machine-subjectivity representation. Inf Sci 323:143–178MathSciNetCrossRefGoogle Scholar
  74. 74.
    Banerjee R, Pal SK (2017) A computational model for the endogenous arousal of thoughts through Z*-numbers. Inf Sci 405:227–258CrossRefGoogle Scholar
  75. 75.
    Bhoumik D (2018) Granulated deep learning: application in video tracking and object recognition, M. Tech. (CSE) Dissertation, Department of Computer Science and Engineering, University of Calcutta, IndiaGoogle Scholar

Copyright information

© The National Academy of Sciences, India 2019

Authors and Affiliations

  1. 1.Center for Soft Computing ResearchIndian Statistical InstituteKolkataIndia

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