Abstract
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 r–f 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.
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References
Pal SK, Meher SK (2013) Natural computing: a problem solving paradigm with granular information processing. Appl Soft Comput 13(9):3944–3955
Zadeh LA (1997) Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy Sets Syst 90:111–127
Pawlak Z (1982) Rough sets. Int J Comput Inf Sci 11:341–356
Pawlak Z (1991) Rough sets: theoretical aspects of reasoning about data. Kluwer Academic, Dordrecht
Zadeh LA (1996) Fuzzy logic = computing with words. IEEE Trans Fuzzy Syst 4(2):103–111
Pedrycz W (2001) Granular computing: an emerging paradigm. Physica-Verlag, Heidelberg
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 111–116
Yao JT, Vasilakos AV, Pedrycz W (2013) Granular computing: perspectives and challenges. IEEE Trans Cybern 43(6):1977–1989
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 3–98
Pawlak Z, Skowron A (2007) Rudiments of rough sets. Inf Sci 177:3–27
Sen D, Pal SK (2009) Generalized rough sets, entropy and image ambiguity measures. IEEE Trans Syst Man Cybern Part B 39(1):117–128
Pal SK (2012) Granular mining and rough–fuzzy pattern recognition: a way to natural computation, (Feature Article). IEEE Intell Inf Bull 13(1):3–13
Pal SK, Mitra P (2004) Case generation using rough sets with fuzzy discretization. IEEE Trans Knowl Data Eng 16(3):292–300
Qian Y, Lian J, Yao Y, Dang C (2010) MGRS: a multi-granulation rough set. Inf Sci 180:949–970
Pal SK, Meher SK, Dutta S (2012) Class-dependent rough–fuzzy granular space, dispersion index and classification. Pattern Recognit 45(7):2690–2707
Zhu W, Wang FY (2007) On three types of covering-based rough sets. IEEE Trans Knowl Data Eng 19(8):1649–1667
Maggio E, Cavallaro A (2010) Video tracking: theory and practice. Wiley, New York
Pal SK, Petrosino A, Maddalena L (eds) (2012) Handbook on soft computing for video surveillance. CRC Press, Boca Raton
Pal SK, Bhunia Chakraborty D (2017) Granular flow graph, adaptive rule generation and tracking. IEEE Trans Cybern 47(12):4096–4107
Pawlak Z (2005) Flow graphs and data mining. Springer, Heidelberg
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 359–370
Chakraborty D, Uma Shankar B, Pal SK (2013) Granulation, rough entropy and spatiotemporal moving object detection. Appl Soft Comput 13(9):4001–4009
Bhunia Chakraborty D, Pal SK (2018) Neighborhood rough filter and intuitionistic entropy in unsupervised tracking. IEEE Trans Fuzzy Syst 26:2188–2200. https://doi.org/10.1109/tfuzz.2017.2768322
Yilmaz A, Javed O, Shah M (2006) Object tracking: a survey. ACM Comput Surv 38:1264–1291
Comaniciu D, Ramesh V, Meer P (2003) Kernel-based object tracking. IEEE Trans Pattern Anal Mach Intell 25:564–577
Cucchiara R, Grana C, Piccardi M, Prati A (2003) Detecting moving objects, ghosts and shadows in video streams. IEEE Trans Pattern Anal Mach Intell 25:1337–1342
Fang H, Jiang J, Feng Y (2006) A fuzzy logic approach for detection of video shot boundaries. Pattern Recognit 39:2092–2100
Pan P, Schonfeld D (2011) Video tracking based on sequential particle filtering on graphs. IEEE Trans Image Process 20(6):1641–1651
Shen C, Kim J, Wang H (2010) Generalized kernel-based visual tracking. IEEE Trans Circuits Syst Video Technol 20:119–130
Maddalena L, Petrosino A, Ferone A (2008) Object motion detection and tracking by an artificial intelligence approach. Int J Patt Recognit Artif Intell 22:915–928
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 155–160
Zhang K, Zhang L, Yang M-H (2014) Fast compressive tracking. IEEE Trans Pattern Anal Mach Intell 36:2002–2015
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:234–247
Nawaz T, Poiesi F, Cavallaro A (2014) Measures of effective video tracking. IEEE Trans Image Process 23:376–388
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):659–672
Pal JK, Ray SS, Pal SK (2016) Identifying relevant group of miRNAs in cancer using fuzzy mutual information”. Med Biol Eng Comput 54(4):701–710
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 89:540–548
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):741–752
Yu L, Han Y, Berens ME (2012) Stable gene selection from microarray data via sample weighting. IEEE/ACM Trans Comput Biol Bioinf 9:262–272
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 12:1440–1447
Guyon J, Weston S, Barnhill V (2002) Vapnik, Gene selection for cancer classification using support vector machines. Mach Learn 46:389–422
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 27:1226–1238
Mundra PA, Rajapakse JC (2010) SVM-RFE with MRMR filter for gene selection. IEEE Trans Nanobiosci 9:31–37
Mitra P, Murthy CA, Pal SK (2012) Unsupervised feature selection using feature similarity. IEEE Trans. Pattern Anal Mach Intell 24:301–312
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 9:1–17
Leidinger P et al (2010) High-throughput miRNA profiling of human melanoma blood samples. BMC Cancer 10:1–11
Kundu S (2016) Granular model for social networks, target selection and fuzzy-rough community detection, Ph.D. Dissertation, Jadavpur University, Kolkata, India
Kundu S, Pal SK (2015) FGSN: fuzzy granular social networks—model and applications. Inf Sci 314:100–117
Kundu S, Pal SK (2015) Fuzzy-rough community in social networks. Pattern Recognit Lett 67(2):145–152
Girvan M, Newman MEJ (2002) Community structure in social and biological networks. Proc Natl Acad Sci USA 99(12):7821–7826
Newman MEJ (2004) Fast algorithm for detecting community structure in networks. Phys Rev E 69(6):066133
Palla G, Derényi I, Farkas I, Vicsek T (2005) Uncovering the overlapping community structure of complex networks in nature and society. Nature 435(7043):814–818
Boorman SA, White HC (1976) Social structure from multiple networks. II. Role structures social structure from multiple networks. Am J Sociol 81:1384–1446
Davis GB, Carley KM (2008) Clearing the FOG: fuzzy, overlapping groups for social networks. Soc Netw 30:201–212
Newman M, Girvan M (2004) Finding and evaluating community structure in networks. Phys Rev E 69:1–15
Chattopadhyay S, Murthy CA, Pal SK (2014) Fitting truncated geometric distributions in large scale real world networks. Theoret Comput Sci 551:22–28
Malliarosa FD, Vazirgiannis M (2013) Clustering and community detection in directed networks: a survey. Phys Rep 533:95–142
Weiss RS, Jacobson E (1955) A method for the analysis of the structure of complex organizations. Am Sociol Assoc 20:661–668
Ganivada A, Dutta S, Pal SK (2011) Fuzzy rough granular neural networks, fuzzy granules and classification. Theor Comput Sci Part C 412(42):5834–5853
Banerjee M, Mitra S, Pal SK (1998) Rough fuzzy MLP: knowledge encoding and classification. IEEE Trans Neural Netw 9(6):1203–1216
Ganivada A, Ray SS, Pal SK (2012) Fuzzy rough granular self-organizing map and fuzzy rough entropy. Theoret Comput Sci 466:37–63
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):1890–1906
Ganivada A, Ray SS, Pal SK (2013) Fuzzy rough sets, and a granular neural network for unsupervised feature selection. Neural Netw 48:91–108
Pal SK, Ray SS, Ganivada A (2017) Granular neural networks, pattern recognition and bioinformatics. Springer, Berlin
Zhang YQ, Jin B, Tang Y (2008) Granular neural networks with evolutionary interval learning. IEEE Trans Fuzzy Syst 16:309–319
Pal SK, Dasgupta B, Mitra P (2004) Rough self organizing map. Appl Intel 21:289–299
Yeung DS, Chen D, Tsang ECC, Lee JWT, Xizhao W (2005) On the generalization of fuzzy rough sets. IEEE Trans Fuzzy Syst 13:343–361
Banerjee M, Pal SK (1996) Roughness of a fuzzy set. Inf Sci 93:235–246
Pal SK, Meher SK, Skowron A (2015) Data science, big data and granular mining. Pattern Recognit Lett 67(2):109–112
Zadeh LA (2001) A new direction in AI: toward a computational theory of perceptions. AI Magazine 22:73–84
Pal SK, Banerjee R (2013) Context granularization and subjective-information quantification. Theoret Comput Sci 448:2–14
Zadeh LA (2011) A note on Z-numbers. Inf Sci 18(14):2923–2932
Banerjee R, Pal SK (2015) Z*-numbers: augmented Z-numbers for machine-subjectivity representation. Inf Sci 323:143–178
Banerjee R, Pal SK (2017) A computational model for the endogenous arousal of thoughts through Z*-numbers. Inf Sci 405:227–258
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, India
Acknowledgements
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.
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Pal, S.K. Granular Mining and Big Data Analytics: Rough Models and Challenges. Proc. Natl. Acad. Sci., India, Sect. A Phys. Sci. 90, 193–208 (2020). https://doi.org/10.1007/s40010-018-0578-3
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DOI: https://doi.org/10.1007/s40010-018-0578-3