Abstract
Natural Computing is a consortium of different methods and theories that are emerged from natural phenomena such as brain modeling, self-organization, self-repetition, self-evaluation, self-reproduction, group behavior, Darwinian survival , granulation and perception.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ankerst, M., Breunig, M.M., Kriegel, H.P., Sander, J.: OPTICS: ordering points to identify the clustering structure. In: Proceedings of ACM International Conference Management of Data, pp. 49–60 (1999)
Bandyopadhyay, S., Pal, S.K.: Classification and Learning Using Genetic Algorithms: Applications in Bioinformatics and Web Intelligence (Natural Computing Series). Springer, Heidelberg (2007)
Banerjee, M., Mitra, S., Pal, S.K.: Rough fuzzy MLP: knowledge encoding and classification. IEEE Trans. Neural Netw. 9(6), 1203–1216 (1998)
Banerjee, M., Pal, S.K.: Roughness of a fuzzy set. Inf. Sci. 93(3–4), 235–246 (1996)
Bellman, R.E., Kalaba, R., Zadeh, L.A.: Abstraction and pattern classification. J. Math. Anal. Appl. 13, 1–7 (1966)
Bezdek, J.C., Pal, S.K.: Fuzzy Models for Pattern Recognition: Methods that Search for Structures in Data. IEEE Press, New York (1992)
Booker, L.B., Goldberg, D.E., Holland, J.H.: Classifier systems and genetic algorithms. Artif. Intell. 40, 235–282 (1989)
Carpenter, G.A., Grossberg, S., Rosen, D.B.: ART 2-A: an adaptive resonance algorithm for rapid category learning and recognition. Neural Netw. 4, 493–504 (1991)
Cerro, L.F.D., Prade, H.: Rough sets, two fold fuzzy sets and logic. In: Di Nola, A., Ventre, A.G.S. (eds.) Fuzziness in Indiscernibility and Partial Information, pp. 103–120. Springer, Berlin (1986)
Chiang, J.H., Ho, S.H.: A combination of rough-based feature selection and RBF neural network for classification using gene expression data. IEEE Trans. Nanobiosci. 7(1), 91–99 (2008)
Cornelis, C., Jensen, R., Hurtado, G., Slezak, D.: Attribute selection with fuzzy decision reducts. Inf. Sci. 180(2), 209–224 (2010)
Cover, T.M., Hart, P.E.: Nearest neighbor pattern classification. IEEE Trans. Inf. Theor. 13(1), 21–27 (1967)
Devijver, P.A., Kittler, J.: Pattern Recognition: A Statistical Approach. Prentice Hall, Englewood Cliffs (1982)
Devroye, L., Gyorfi, L., Lugosi, G.: A Probabilistic Theory of Pattern Recognition. Springer, Heidelberg (1996)
Dick, S., Kandel, A.: Granular computing in neural networks. In: Pedrycz, W. (ed.) Granular Computing: An Emerging Paradigm, pp. 275–305. Physica Verlag, Heidelberg (2001)
Dubois, D., Prade, H.: Rough fuzzy sets and fuzzy rough sets. Int. J. Gen. Syst. 17(2–3), 91–209 (1990)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley-Interscience, New York (2000)
Ester, M., Kriegel, H.P., Sander, J., Xu, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of 2nd International Conference on Knowledge Discovery and Data Mining (KDD 1996), pp. 226–231 (1996)
Ganivada, A., Dutta, S., Pal, S.K.: Fuzzy rough granular neural networks, fuzzy granules, and classification. Theoret. Comput. Sci. 412, 5834–5853 (2011)
Ganivada, A., Pal, S.K.: Robust granular neural networks, fuzzy granules and classification. In: Proceedings of 5th International Conference on Rough Sets and Knowledge Technology, pp. 220–227 (2010)
Ganivada, A., Pal, S.K.: A novel fuzzy rough granular neural network for classification. Int. J. Comput. Intell. Syst. 4(5), 1042–1051 (2011)
Ganivada, A., Ray, S.S., Pal, S.K.: Fuzzy rough granular self organizing map. In: Proceedings of 6th International Conference on Rough Sets and Knowledge Technology, pp. 659–668 (2011)
Ganivada, A., Ray, S.S., Pal, S.K.: Fuzzy rough granular self-organizing map and fuzzy rough entropy. Theoret. Comput. Sci. 466, 37–63 (2012)
Ganivada, A., Ray, S.S., Pal, S.K.: Fuzzy rough sets, and a granular neural network for unsupervised feature selection. Neural Netw. 48, 91–108 (2013)
Ghosh, A., Shankar, B.U., Meher, S.: A novel approach to neuro-fuzzy classification. Pattern Recogn. 22, 100–109 (2009)
Goldberg, D.: Genetic Algorithms in Optimization, Search, and Machine Learning. Addison Wesley, Reading (1989)
Haykin, S.: Neural Networks: A Comprehensive Foundation, 2nd edn. Prentice Hall, Upper Saddle River (1998)
Herbert, J.P., Yao, J.T.: A granular computing frame work for self-organizing maps. Neurocomputing 72(13–15), 2865–2872 (2009)
Hinneburg, A., Keim, D.A.: An efficient approach to clustering in large multimedia databases with noise. In: Proceedings of 4th International Conference on Knowledge Discovery and Data Mining, pp. 58–65 (1998)
Hirota, K., Pedrycz, W.: OR/AND neuron in modeling fuzzy set connectives. IEEE Trans. Fuzzy Syst. 2(2), 151–161 (1994)
Jang, J.S.R.: ANFIS: adaptive-network-based fuzzy inference systems. IEEE Trans. Syst. Man Cybern. 23(3), 665–685 (1993)
Jensen, R., Shen, Q.: New approaches to fuzzy-rough feature selection. IEEE Trans. Fuzzy Syst. 17(4), 824–838 (2009)
Johnson, S.C.: Hierarchical clustering schemes. Psychometrika 32(3), 241–254 (1967)
Kohonen, T.: Self-organizing maps. Proc. IEEE 78(9), 1464–1480 (1990)
Kuncheva, L.I.: Fuzzy Classifier Design. Springer, Heidelberg (2000)
Lingras, P.: Rough neural networks. In: Proceedings of 6th International Conference on Information Processing and Management of Uncertainty, pp. 1445–1450 (1996)
Lingras, P., Hogo, M., Snorek, M.: Interval set clustering of web users using modified Kohonen self-organizing maps based on the properties of rough sets. Web Intell. Agent Syst. 2(3), 217–225 (2004)
Lingras, P., Jensen, R.: Survey of rough and fuzzy hybridization. In: Proceedings of IEEE International Conference on Fuzzy Systems, pp. 1–6 (2007)
Maji, P., Pal, S.K.: Fuzzy-rough sets for information measures and selection of relevant genes from microarray data. IEEE Trans. Syst. Man. Cybern. Part B 40(3), 741–752 (2010)
Maji, P., Pal, S.K.: Rough-Fuzzy Pattern Recognition: Applications in Bioinformatics and Medical Imaging. Wiley, Hoboken (2011)
Mitra, P., Murthy, C.A., Pal, S.K.: Unsupervised feature selection using feature similarity. IEEE Trans. Pattern Anal. Mach. Intell. 24(4), 301–312 (2002)
Mitra, S., Pal, S.K.: Self-organizing neural network as a fuzzy classifier. IEEE Trans. Syst. Man Cybern. 24(3), 385–399 (1994)
Ouyang, Y., Wang, Z., Zhang, H.P.: On fuzzy rough sets based on tolerance relations. Inf. Sci. 180(4), 532–542 (2010)
Pal, A., Pal, S.K.: Pattern Recognition and Big Data (2017)
Pal, S.K.: Computational theory perception (CTP), rough-fuzzy uncertainty analysis and mining in bioinformatics and web intelligence: a unified framework. In: Transactions on Rough Set. LNCS, vol. 5946, pp. 106–129 (2009)
Pal, S.K.: Granular mining and rough-fuzzy pattern recognition: a way to natural computation. IEEE Intell. Inf. Bull. 13(1), 3–13 (2012)
Pal, S.K., Dasgupta, B., Mitra, P.: Rough self-organizing map. Appl. Intell. 21(1), 289–299 (2004)
Pal, S.K., De, R.K., Basak, J.: Unsupervised feature evaluation: a neuro-fuzzy approach. IEEE Trans. Neural Netw. 11(2), 366–376 (2000)
Pal, S.K., Ghosh, A.: Neuro-fuzzy computing for image processing and pattern recognition. Int. J. Syst. Sci. 27, 1179–1193 (1996)
Pal, S.K., Kundu, S.: Granular social network: model and applications. In: Zomaya, A., Sakr, S. (eds.) Handbook of Big Data Technologies. Springer, Heidelberg (2017, to appear)
Pal, S.K., Majumder, D.D.: Fuzzy Mathematical Approach to Pattern Recognition. Wiley, New York (1986)
Pal, S.K., Meher, S.K.: Natural computing: a problem solving paradigm with granular information processing. Appl. Soft Comput. 13(9), 3944–3955 (2013)
Pal, S.K., Meher, S.K., Dutta, S.: Class-dependent rough-fuzzy granular space, dispersion index and classification. Pattern Recogn. 45(7), 2690–2707 (2012)
Pal, S.K., Mitra, P.: Multispectral image segmentation using rough set initialized EM algorithm. IEEE Trans. Geosci. Remote Sens. 40(11), 2495–2501 (2002)
Pal, S.K., Mitra, P.: Case generation using rough sets with fuzzy representation. IEEE Trans. Knowl. Data Eng. 16(3), 292–300 (2004)
Pal, S.K., Mitra, P.: Pattern Recognition Algorithms for Data Mining. Chapman and Hall/CRC, Boca Raton (2004)
Pal, S.K., Mitra, S.: Multilayer perceptron, fuzzy sets and classification. IEEE Trans. Neural Netw. 3(5), 683–697 (1992)
Pal, S.K., Mitra, S.: Neuro-Fuzzy Pattern Recognition: Methods in Soft Computing. Wiley-Interscience, New York (1999)
Pal, S.K., Mitra, S., Mitra, P.: Rough fuzzy MLP: modular evolution, rule generation and evaluation. IEEE Trans. Knowl. Data Eng. 15(1), 14–25 (2003)
Pal, S.K., Peters, J.F.: Rough Fuzzy Image Analysis: Foundations and Methodologies. Chapman and Hall/CRC, Boca Raton (2010)
Pal, S.K., Polkowski, L., Skowron, A.: Rough-Neural Computing: Techniques for Computing with Words. Springer, Heidelberg (2004)
Pal, S.K., Skowron, A.: Rough-Fuzzy Hybridization: A New Trend in Decision Making. Springer, New York (1999)
Parthalain, N.M., Jensen, R.: Unsupervised fuzzy-rough set-based dimensionality reduction. Inf. Sci. 229(3), 106–121 (2013)
Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning About Data. Kluwer Academic Publishers, Dordrecht (1992)
Pawlak, Z., Skowron, A.: Rough sets and Boolean reasoning. Inf. Sci. 177(3), 41–73 (2007)
Pawlak, Z., Skowron, A.: Rough sets: some extensions. Inf. Sci. 177(3), 28–40 (2007)
Pedrycz, W., Vukovich, G.: Granular neural networks. Neurocomputing 36, 205–224 (2001)
Peng, H.C., Long, F., Ding, C.: Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans. Pattern Anal. Mach. Intell. 27(8), 1226–1238 (2005)
Peters, J.F., Skowron, A., Han, L., Ramanna, S.: Towards rough neural computing based on rough membership functions: theory and application. In: Ziarko, W., Yao, Y. (eds.) Rough Sets and Current Trends in Computing, pp. 611–618. Springer, Heidelberg (2001)
Polkowski, L., Skowron, A.: Rough mereology: a new paradigm for approximate reasoning. Int. J. Approx. Reason. 15, 333–365 (1996)
Polkowski, L., Tsumoto, S., Lin, T.Y.: Rough Set Methods and Applications. Physica, Heidelberg (2001)
Qian, Y., Liang, J., Yao, Y., Dang, C.: MGRS: a multi-granulation rough set. Inf. Sci. 180, 949–970 (2010)
Ray, S.S., Bandyopadhyay, S., Pal, S.K.: Gene ordering in partitive clustering using microarray expressions. J. Biosci. 32(5), 1019–1025 (2007)
Ray, S.S., Bandyopadhyay, S., Pal, S.K.: Genetic operators for combinatorial optimization in TSP and microarray gene ordering. Appl. Intell. 26(3), 183–195 (2007)
Ray, S.S., Bandyopadhyay, S., Pal, S.K.: Combining multi-source information through functional annotation based weighting: gene function prediction in yeast. IEEE Trans. Biomed. Eng. 56(2), 229–236 (2009)
Ray, S.S., Ganivada, A., Pal, S.K.: A granular self-organizing map for clustering and gene selection in microarray data. IEEE Trans. Neural Netw. Learn. Syst. 27(9), 1890–1906 (2016)
Safavian, S.R., Landgrebe, D.: A survey of decision tree classifier methodology. IEEE Trans. Syst. Man Cybern. 21(3), 660–674 (1991)
Saha, S., Murthy, C.A., Pal, S.K.: Rough set based ensemble classifier for web page classification. Fundamenta Informaticae 76(1–2), 171–187 (2007)
Schölkopf, B., Smola, A.J.: Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. MIT Press, Cambridge (2001)
Sen, D., Pal, S.K.: Generalized rough sets, entropy and image ambiguity measures. IEEE Trans. Syst. Man Cybern. Part B 39(1), 117–128 (2009)
Szczuka, M.: Refining classifiers with neural networks. Int. J. Comput. Inf. Sci. 16(1), 39–55 (2001)
Vasilakos, A., Stathakis, D.: Granular neural networks for land use classification. Soft Comput. 9(5), 332–340 (2005)
Verikas, A., Bacauskiene, M.: Feature selection with neural networks. Pattern Recogn. Lett. 23(11), 1323–1335 (2002)
Wacker, A.G.: Minimum distance approach to classification. Ph.D. thesis, Purdue University, Lafayette, Indiana (1972)
Yang, X., Song, X., Dou, H., Yang, J.: Multigranulation rough set: from crisp to fuzzy case. Ann. Fuzzy Math. Inf. 1, 55–70 (2011)
Yao, Y.Y.: A partition model of granular computing. In: Transactions on Rough Sets. LNCS, vol. 3100, pp. 232–253 (2004)
Yasdi, R.: Combining rough sets and neural learning method to deal with uncertain and imprecise information. Neurocomputing 7(1), 61–84 (1995)
Zadeh, L.A.: Fuzzy sets. Inf. Control 8, 338–353 (1965)
Zadeh, L.A.: Outline of a new approach to the analysis of complex systems and decision processes. IEEE Trans. Syst. Man Cybern. SMC 3, 28–44 (1973)
Zadeh, L.A.: The concept of a linguistic variable and its application to approximate reasoning- II. Inf. Sci. 8, 301–357 (1975)
Zadeh, L.A.: Fuzzy logic, neural networks, and soft computing. Commun. ACM 37(3), 77–84 (1994)
Zadeh, L.A.: Fuzzy logic = computing with words. IEEE Trans. Fuzzy Syst. 4, 103–111 (1996)
Zadeh, L.A.: Towards a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy Sets Syst. 90, 111–127 (1997)
Zadeh, L.A.: A new direction in AI: towards a computational theory of perceptions. AI Mag. 22, 73–84 (2001)
Zhang, Y.Q., Fraser, M.D., Gagliano, R., Kandel, A.: Granular neural networks for numerical-linguistic data fusion and knowledge discovery. IEEE Trans. Neural Netw. 11(3), 658–667 (2000)
Zhang, Y.Q., Jin, B., Tang, Y.: Granular neural networks with evolutionary interval learning. IEEE Trans. Fuzzy Syst. 16(2), 309–319 (2008)
Zhu, W., Wang, F.Y.: On three types of covering-based rough sets. IEEE Trans. Knowl. Data Eng. 19(8), 1649–1667 (2007)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this chapter
Cite this chapter
Pal, S.K., Ray, S.S., Ganivada, A. (2017). Introduction to Granular Computing, Pattern Recognition and Data Mining. In: Granular Neural Networks, Pattern Recognition and Bioinformatics. Studies in Computational Intelligence, vol 712. Springer, Cham. https://doi.org/10.1007/978-3-319-57115-7_1
Download citation
DOI: https://doi.org/10.1007/978-3-319-57115-7_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-57113-3
Online ISBN: 978-3-319-57115-7
eBook Packages: EngineeringEngineering (R0)