DGCC: data-driven granular cognitive computing

Guoyin Wang1
1Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Posts and Telecommunications, Nan’an District, Chongqing, People’s Republic of China

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Ackley DH, Hinton GE, Sejnowski TJ (1985) A learning algorithm for boltzmann machines. Cognit Sci 9(1):147–169

Bargiela A, Pedrycz W (2008) Toward a theory of granular computing for human-centered information processing. IEEE Trans Fuzzy Syst 16(2):320–330

Bellisimo J (2015) What’s the future of cognitive computing? http://www.forbes.com/sites/ibm/2015/02/23/whats-the-future-of-cognitive-computing-ibm-watson

Bengio Y (2009) Learning deep architectures for AI. Found Trends Mach Learn 2(1):1–127

Beni G, Wang J (1993) Swarm intelligence in cellular robotic systems. Robots and biological systems: towards a new bionics. Springer, Berlin, pp 703–712

Brooks RA (1991) Intelligence without representation. Artif Intell 47(1–3):139–159

Broomhead DS, Lowe D (1988) Radial basis functions, multi-variable functional interpolation and adaptive networks. Tech. rep, DTIC Document

Chen L (1982) Topological structure in visual perception. Science 218(4573):699–700

Chen L, Zhang S, Srinivasan MV (2003) Global perception in small brains: topological pattern recognition in honey bees. Proc Natl Acad Sci 100(11):6884–6889

Chen ZF, Aghakhani S, Man J, Dick S (2011) Ancfis: a neurofuzzy architecture employing complex fuzzy sets. IEEE Trans Fuzzy Syst 19(2):305–322

Chicco D, Sadowski P, Baldi P (2014) Deep autoencoder neural networks for gene ontology annotation predictions. In: Proceedings of the 5th ACM conference on bioinformatics, computational biology, and health informatics. ACM, New York, pp 533–540

Chou GF, Ma JM, Yang HZ (2009) Mathematic model of concept granular computing system. Sci China Ser F-Inf Sci 39(12):1239–1247

Cireşan DC, Giusti A, Gambardella LM, Schmidhuber J (2013) Mitosis detection in breast cancer histology images with deep neural networks. In: International conference on medical image computing and computer-assisted intervention. Springer, Berlin, pp 411–418

Crevier D (1993) AI: the tumultuous search for artificial intelligence. Basic Books, New York

De Castro LN, Timmis J (2002) Artificial immune systems: a new computational intelligence approach. Springer Science & Business Media, Berlin

De Jong K (2006) Evolutionary computation: a unified approach. MIT Press, New York

Deng L, Yu D (2014) Deep learning: methods and applications. Found Trends Signal Process 7(3–4):197–387

Deng L, Hinton GE, Kingsbury B (2013) New types of deep neural network learning for speech recognition and related applications: an overview. In: 2013 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, New York, pp 8599–8603

Deng WH, Wang GY, Zhang XR, Xu J, Li GD (2016) A multi-granularity combined prediction model based on fuzzy trend forecasting and particle swarm techniques. Neurocomputing 173:1671–1682

Frayman Y, Wang LP (1998) Data mining using dynamically constructed recurrent fuzzy neural networks. Springer, Berlin

Fukushima K (1980) Neocognitron: a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol Cybern 36(4):193–202

Goller C, Kuchler A (1996) Learning task-dependent distributed representations by backpropagation through structure. In: IEEE international conference on neural networks, 1996, vol 1. IEEE, New York, pp 347–352

Goodfellow I, Bengio Y, Courville A (2016) Deep learning. MIT Press, New York

Han SH, Chen L (1996) The relationship between global properties and local properties-global precedence. Adv Psychol Sci 4(1):36–41

Haykin S (1994) Neural networks: a comprehensive foundation. Macmillan College Publishing Company, Prentice Hall PTR

Hinton GE (2007) Learning multiple layers of representation. Trends Cognit Sci 11(10):428–434

Hinton GE, Salakhutdinov RR (2006) Reducing the dimensionality of data with neural networks. Science 313(5786):504–507

Hinton GE, Dayan P, Frey BJ, Neal R (1995) The wake–sleep algorithm for unsupervised neural networks. Science 268:1158–1161

Hinton GE, Osindero S, Teh YW (2006) A fast learning algorithm for deep belief nets. Neural Comput 18(7):1527–1554

Hopfield JJ (1982) Neural networks and physical systems with emergent collective computational abilities. Proc Natl Acad Sci 79(8):2554–2558

Jackson P (1998) Introduction to expert systems, 3rd edn. Addison-Wesley Longman Publishing Co. Inc, Boston

Jang JS (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23(3):665–685

Jankowski A, Skowron A (2007) Toward rough-granular computing. Springer, Berlin

Kelly JE III (2015) Computing, cognition and the future of knowing. Dr Kelly III John IBM Research. Cognitive Computing IBM Corporation, USA

Kohonen T (1982) Self-organized formation of topologically correct feature maps. Biol Cybern 43(1):59–69

Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105

LeCun Y, Boser B, Denker JS, Henderson D, Howard RE, Hubbard W, Jackel LD (1989) Backpropagation applied to handwritten zip code recognition. Neural Comput 1(4):541–551

LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444

Lee SC, Lee ET (1974) Fuzzy sets and neural networks. J Cybern 4(2):83–103

Leondes CT (2001) Expert systems: the technology of knowledge management and decision making for the 21st century. Academic Press, New York

Li DY, Meng HJ, Shi XM (1995) Membership clouds and membership cloud generators. J Comput Res Dev 32(6):15–20

Lin TY (2003) Granular computing. In: International workshop on rough sets, fuzzy sets, data mining, and granular-soft computing. Springer, Berlin, pp 16–24

Lindsay PH, Norman DA (1977) Human information processing: an introduction to psychology. Academic Press, New York

Liu YC, Li DY, He W, Wang GY (2013) Granular computing based on gaussian cloud transformation. Fundamenta Informaticae 127(1–4):385–398

Malekzadeh AA, Akbarzadeh TM (2004) Complex-value adaptive neuro fuzzy inference system-canfis. In: Presented at the proceedings of World Automation Congress. Seville, Spain

Mesnil G, Dauphin Y, Yao K, Bengio Y, Deng L, Hakkani-Tur D, He X, Heck L, Tur G, Yu D et al (2015) Using recurrent neural networks for slot filling in spoken language understanding. IEEE/ACM Tran Audio Speech Lang Process (TASLP) 23(3):530–539

Miller GA (2003) The cognitive revolution: a historical perspective. Trends Cognit Sci 7(3):141–144

Modha DS, Ananthanarayanan R, Esser SK, Ndirango A, Sherbondy AJ, Singh R (2011) Cognitive computing. Commun ACM 54(8):62–71

Navon D (1977) Forest before trees: the precedence of global features in visual perception. Cognit Psychol 9(3):353–383

Newell A, Simon HA (1976) Computer science as empirical inquiry: symbols and search. Commun ACM 19(3):113–126

Pedrycz W (2001) Granular computing: an emerging paradigm. Physica-Verlag GmbH, Heidelberg

Pedrycz W (2006) Granular computing: an overview. Applied soft computing technologies: the challenge of complexity. Springer, Berlin, pp 19–34

Pedrycz W, Aliev RA (2009) Logic-oriented neural networks for fuzzy neurocomputing. Neurocomputing 73(1):10–23

Peters G, Weber R (2016) Dcc: a framework for dynamic granular clustering. Granul Comput 1(1):1–11

Rodriguez A, Laio A (2014) Clustering by fast search and find of density peaks. Science 344(6191):1492–1496

Rumelhart DE (1986) McClelland. Parallel distributed processing, explorations in the microstructures of cognition

Rumelhart DE, Hinton GE, Williams RJ (1986) Learning internal representations by backpropagating errors. Nature 323(99):533–536

Rumelhart DE, Hinton GE, Williams RJ (1988) Learning representations by back-propagating errors. Cognit Model 5(3):1

Schmidhuber J (2015) Deep learning in neural networks: an overview. Neural Netw 61:85–117

Simon AH (1996) The sciences of the artificial. MIT Press, New York

Skinner B (2011) About behaviorism. Knopf Doubleday Publishing Group, Vintage

Skowron A, Jankowski A, Dutta S (2016) Interactive granular computing. Granul Comput 1(2):95–113

Song ML, Wang YB (2016) A study of granular computing in the agenda of growth of artificial neural networks. Granul Comput 1(4):247–257

Thagard P (2014) Cognitive science. In: Zalta EN (ed) The Stanford encyclopedia of philosophy, fall, 2014th edn. Stanford University, Metaphysics Research Lab, USA

Wang FY (1992) Knowledge structure in neural nets using fuzzy logic. In: Jamshidi M (ed) Robotics and manufacturing: recent trends in research. Education and applications. ASME Press, New York

Wang FY, Hm Kim (1995) Implementing adaptive fuzzy logic controllers with neural networks: a design paradigm. J Intell Fuzzy Syst 3(2):165–180

Wang GY (1996) Study of the neural network models and algorithms in an integrated intelligent system. PhD thesis, Xian Jiaotong University

Wang GY, Shi HB (1996) Three valued logic neural network. In: Proc. of int. conf. on neural information processing, Hong Kong, pp 1112–1115

Wang GY, Shi HB (1998) TMLNN: triple-valued or multiple-valued logic neural network. IEEE Trans Neural Netw 9(6):1099–1117

Wang GY, Wang Y (2009) 3DM: domain-oriented data-driven data mining. Fundamenta Informaticae 90(4):395–426

Wang GY, Xu CL (2012) Cloud model-a bidirectional cognition model between concept’s extension and intension. In: International conference on advanced machine learning technologies and applications. Springer, Berlin, pp 391–400

Wang GY, Xu CL, Li DY (2014) Generic normal cloud model. Inf Sci 280:1–15

Wang GY, Yang J, Xu J (2016) Granular computing: from granularity optimization to multi-granularity joint problem solving. Granul Comput doi: 10.1007/s41066-016-0032-3

Wilke G, Portmann E (2016) Granular computing as a basis of human–data interaction: a cognitive cities use case. Granul Comput 1(3):181–197

Wille R (1982) Restructuring lattice theory: an approach based on hierarchies of concepts. Ordered sets. Springer, Berlin, pp 445–470

Xu J, Wang GY, Deng WH (2016) Denpehc: density peak based efficient hierarchical clustering. Inf Sci 373:200–218

Xu J, Wang GY, Li TR, Deng WH, Gou GL (2017) Fat node leading tree for data stream clustering with density peaks. Knowl Based Syst 120:99–117

Yao JT, Vasilakos AV, Pedrycz W (2013) Granular computing: perspectives and challenges. IEEE Trans Cybern 43(6):1977–1989

Yao YY (2004) A partition model of granular computing. Transactions on rough sets I. Springer, Berlin, pp 232–253

Yao YY (2005) Perspectives of granular computing. In: 2005 IEEE international conference on granular computing, vol 1. IEEE, New York, pp 85–90

Yao YY (2011) Artificial intelligence perspectives on granular computing. Granular computing and intelligent systems. Springer, Berlin, pp 17–34

Yao YY (2016a) Three-way decisions and cognitive computing. Cognit Comput 8(4):543–554

Yao YY (2016b) A triarchic theory of granular computing. Granul Comput 1(2):145–157

Yu H, Zhang C, Wang GY (2016) A tree-based incremental overlapping clustering method using the three-way decision theory. Knowl Based Syst 91:189–203

Zadeh LA (1996) Fuzzy sets, fuzzy logic, and fuzzy systems: selected papers by Lotfi A. Zadeh. World Scientific Publishing Co. Inc, River Edge

Zadeh LA (1997) Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy Sets Syst 90(2):111–127

Zadeh LA (2007) Granular computing–computing with uncertain, imprecise and partially true data. In: Proc 5th int sym spat data qual (ISSDQ 2007)

Zhang L, Zhang B (2014) Quotient space based problem solving: a theoretical foundation of granular computing. Elsevier Science, Amsterdam

Zhang YQ, Kandel A (1998) Compensatory genetic fuzzy neural networks and their applications. World Scientific Publishing Co., Inc, Singapore