A journal of IEEE and CAA , publishes high-quality papers in English on original theoretical/experimental research and development in all areas of automation
Volume 5 Issue 6
Nov.  2018

IEEE/CAA Journal of Automatica Sinica

  • JCR Impact Factor: 11.8, Top 4% (SCI Q1)
    CiteScore: 17.6, Top 3% (Q1)
    Google Scholar h5-index: 77, TOP 5
Turn off MathJax
Article Contents
Witold Pedrycz, "Granular Computing for Data Analytics: A Manifesto of Human-Centric Computing," IEEE/CAA J. Autom. Sinica, vol. 5, no. 6, pp. 1025-1034, Nov. 2018. doi: 10.1109/JAS.2018.7511213
Citation: Witold Pedrycz, "Granular Computing for Data Analytics: A Manifesto of Human-Centric Computing," IEEE/CAA J. Autom. Sinica, vol. 5, no. 6, pp. 1025-1034, Nov. 2018. doi: 10.1109/JAS.2018.7511213

Granular Computing for Data Analytics: A Manifesto of Human-Centric Computing

doi: 10.1109/JAS.2018.7511213
More Information
  • In the plethora of conceptual and algorithmic developments supporting data analytics and system modeling, humancentric pursuits assume a particular position owing to ways they emphasize and realize interaction between users and the data. We advocate that the level of abstraction, which can be flexibly adjusted, is conveniently realized through Granular Computing. Granular Computing is concerned with the development and processing information granules-formal entities which facilitate a way of organizing knowledge about the available data and relationships existing there. This study identifies the principles of Granular Computing, shows how information granules are constructed and subsequently used in describing relationships present among the data.

     

  • loading
  • [1]
    A. Bargiela and W. Pedrycz, Granular Computing: An Introduction, Kluwer Academic Publishers, Dordrecht, 2003.
    [2]
    A. Bargiela and W. Pedrycz, "Toward a theory of Granular Computing for human-centered information processing", IEEE Transactions on Fuzzy Systems, vol. 16, no. 2, pp. 320-330, 2008. doi: 10.1109/TFUZZ.2007.905912
    [3]
    L. A. Zadeh, "Towards a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic", Fuzzy Sets and Systems, vol. 90, no. 2, pp. 111-117, 1997. doi: 10.1016/S0165-0114(97)00077-8
    [4]
    L. A. Zadeh, "Toward a generalized theory of uncertainty (GTU)—an outline", Information Sciences, vol. 172, pp. 1-40, 2005. doi: 10.1016/j.ins.2005.01.017
    [5]
    J. Leng, Q. Chen, N. Mao, and P. Jiang, "Combining granular computing technique with deep learning for service planning under social manufacturing contexts", Knowledge-Based Systems, vol. 143, pp. 295-306, 2018. doi: 10.1016/j.knosys.2017.07.023
    [6]
    V. Loia, F. Orciuoli, and W. Pedrycz, "Towards a granular computing approach based on Formal Concept Analysis for discovering periodicities in data", Knowledge-Based Systems, vol. 146, pp. 1-11, 2018. doi: 10.1016/j.knosys.2018.01.032
    [7]
    W. Pedrycz and A. Bargiela, "Granular clustering: a granular signature of data", IEEE Trans. Systems, Man and Cybernetics, 32, pp. 212-224, 2002. doi: 10.1109/3477.990878
    [8]
    W. Pedrycz and A. Gacek, "Temporal granulation and its application to signal analysis", Information Sciences, vol. 143, pp.1-4, 47-71, 2002. doi: 10.1016/S0020-0255(02)00171-8
    [9]
    J. Zhou, Z. Lai, D. Miao, C. Gao, and X. Yue, "Multigranulation rough-fuzzy clustering based on shadowed sets", Information Sciences, in press, available online 30 May 2018. http://www.sciencedirect.com/science/article/pii/S0020025518304274
    [10]
    Q. Wang and Z. Gong, "An application of fuzzy hypergraphs and hypergraphs in granular computing", Information Sciences, vol. 429, pp. 296-314, 2018. doi: 10.1016/j.ins.2017.11.024
    [11]
    G. Chiaselotti, D. Ciucci, and T. Gentile, "Simple graphs in granular computing", Information Sciences, pp. 340-341, 279-304, 2016. http://www.sciencedirect.com/science/article/pii/S0020025516000256
    [12]
    S. K. Pal and D. B. Chakraborty, "Granular flow graph, adaptive rule generation and tracking", IEEE Transactions on Cybernetics, vol. 47, no. 12, pp. 4096-4107, 2017. doi: 10.1109/TCYB.2016.2600271
    [13]
    G. Chiaselotti, T. Gentile, and F. Infusino, "Granular computing on information tables: Families of subsets and operators", Information Sciences, pp. 442-443, 72-102, 2018. http://www.sciencedirect.com/science/article/pii/S0020025518301312
    [14]
    S. Salehi, A. Selamat, and H. Fujita, "Systematic mapping study on granular computing", Knowledge-Based Systems, 80, pp. 78-97, 2015. doi: 10.1016/j.knosys.2015.02.018
    [15]
    G. Chiaselotti, T. Gentile, and F. "Infusino, Knowledge pairing systems in granular computing", Knowledge-Based Systems, vol. 124, pp. 144-163, 2017. doi: 10.1016/j.knosys.2017.03.008
    [16]
    C. Bisi, G. Chiaselotti, D. Ciucci, T. Gentile, and F. G. Infusino, "Micro and macro models of granular computing induced by the indiscernibility relation", Information Sciences, pp. 388-389, 247-273, 2017. http://www.sciencedirect.com/science/article/pii/S0020025517300920
    [17]
    P. Hońko, "Association discovery from relational data via granular computing", Information Sciences, vol. 234, pp.136-149, 2013. doi: 10.1016/j.ins.2013.01.004
    [18]
    H. Wang, J. Yang, Z. Wang, and Q. Wang, "A binary granular algorithm for spatiotemporal meteorological data mining", in Proc. 2nd IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services (ICSDM), NJ, USA, 2015, pp. 5-11. http://ieeexplore.ieee.org/document/7298016/
    [19]
    X.Q. Tang and P. Zhu, "Hierarchical clustering problems and analysis of fuzzy proximity relation on granular space", IEEE Transactions on Fuzzy Systems, vol. 21, no. 5, pp. 814-824, 2013. doi: 10.1109/TFUZZ.2012.2230176
    [20]
    X. Wang, X. Liu, and L. Zhang, "A rapid fuzzy rule clustering method based on granular computing", Applied Soft Computing, vol. 24, pp. 534-542, 2014. doi: 10.1016/j.asoc.2014.08.004
    [21]
    H. Liu, S. Xiong, and C.-A. Wu, "Hyperspherical granular computing classification algorithm based on fuzzy lattices", Mathematical and Computer Modelling, vol. 57, pp. 3-4, 661-670, 2013. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=JJ0228548781
    [22]
    A. V. Savchenko, "Fast multi-class recognition of piecewise regular objects based on sequential three-way decisions and granular computing", Knowledge-Based Systems, vol. 91, pp. 252-262, 2016. doi: 10.1016/j.knosys.2015.09.021
    [23]
    P. Singh and G. Dhiman, "A hybrid fuzzy time series forecasting model based on granular computing and bio-inspired optimization approaches", J. of Computational Science, In press, 2018. http://www.sciencedirect.com/science/article/pii/S1877750317300923
    [24]
    O. Hryniewicz and K. Kaczmarek, "Bayesian analysis of time series using granular computing approach", Applied Soft Computing, vol. 47, pp. 644-652, 2016. doi: 10.1016/j.asoc.2014.11.024
    [25]
    Z. Han, J. Zhao, H. Leung, and W. Wang, "Construction of prediction intervals for gas flow systems in steel industry based on granular computing", Control Engineering Practice, vol. 78, pp. 79-88, 2018. doi: 10.1016/j.conengprac.2018.06.012
    [26]
    J. Li, C. Mei, W. Xu, and Y. Qian, "Concept learning via granular computing: A cognitive viewpoint", Information Sciences, vol. 298, pp. 447-467, 2015. doi: 10.1016/j.ins.2014.12.010
    [27]
    H. Hu, L. Pang, D. Tian, and Z. Shi, "Perception granular computing in visual haze-free task", Expert Systems with Applications, vol. 41, pp. 2729-2741, 2014. doi: 10.1016/j.eswa.2013.11.006
    [28]
    J. Martínez-Frutos, P. J. Martínez-Castejón, and D. Herrero-Pérez, "Efficient topology optimization using GPU computing with multilevel granularity", Advances in Engineering Software, 106, pp. 47-62, 2017. doi: 10.1016/j.advengsoft.2017.01.009
    [29]
    M. Saberi, M. S. Mirtalaie, F. K. Hussain, A. Azadeh, and B. Ashjari, "A granular computing-based approach to credit scoring modeling", Neurocomputing, vol. 122, pp. 100-115, 2013. doi: 10.1016/j.neucom.2013.05.020
    [30]
    S. S. Ray, A. Ganivada, and S. K. Pal, "A granular Self-Organizing map for clustering and gene selection in microarray data", IEEE Transactions on Neural Networks and Learning Systems, vol. 27, no. 9, pp. 1890-1906, 2016. doi: 10.1109/TNNLS.2015.2460994
    [31]
    Y. Tang, Y.Q. Zhang, Z. Huang, X. Hu, and Y. Zhao, "Recursive fuzzy granulation for gene subsets extraction and cancer classification", IEEE Transactions on Information Technology in Biomedicine, vol. 12, no. 6, pp. 723-730, 2008. doi: 10.1109/TITB.2008.920787
    [32]
    G. Alefeld and J. Herzberger, Introduction to Interval Computations, Academic Press, New York, 1983.
    [33]
    R. Moore, Interval Analysis, Prentice Hall, Englewood Cliffs, 1966.
    [34]
    R. Moore, R. B. Kearfott, and M.J. Cloud, Introduction to Interval Analysis, SIAM, Philadelphia, 2009.
    [35]
    D. Dubois and H. Prade, Outline of fuzzy set theory: An introduction, In Advances in Fuzzy Set Theory and Applications, M. M. Gupta, R. K. Ragade, and R. R. Yager (eds. ), North-Holland, Amsterdam, pp. 27-39, 1979.
    [36]
    D. Dubois and H. Prade, "The three semantics of fuzzy sets", Fuzzy Sets and Systems, vol. 90, pp. 141-150, 1997. doi: 10.1016/S0165-0114(97)00080-8
    [37]
    D. Dubois and H. Prade, "An introduction to fuzzy sets", Clinica Chimica Acta, vol. 70, pp. 3-29, 1998. doi: 10.1016-S0165-0114(96)00076-0/
    [38]
    G. Klir and B. Yuan, Fuzzy Sets and Fuzzy Logic: Theory and Applications, Prentice-Hall, Upper Saddle River, 1995.
    [39]
    H. Nguyen and E. Walker, A First Course in Fuzzy Logic, Chapman Hall, CRC Press, Boca Raton, 1999.
    [40]
    A. Pedrycz, F. Dong, and K. Hirota, "Finite α cut-based approximation of fuzzy sets and its evolutionary optimization", Fuzzy Sets and Systems, vol. 160, pp. 3550-3564, 2009. doi: 10.1016/j.fss.2009.06.011
    [41]
    W. Pedrycz and F. Gomide, Fuzzy Systems Engineering: Toward Human-Centric Computing, John Wiley, Hoboken, NJ, 2007.
    [42]
    L. A. Zadeh, "Fuzzy sets", Information and Control, vol. 8, pp. 33-353, 1965. http://d.old.wanfangdata.com.cn/Periodical/xtgcydzjs-e201504014
    [43]
    L. A. Zadeh, "The concept of linguistic variables and its application to approximate reasoning Ⅰ, Ⅱ, Ⅲ", Information Sciences, vol. 8, pp. 43-80, 199-249, 301-357, 1975.
    [44]
    L. A. Zadeh, "From computing with numbers to computing with words-from manipulation of measurements to manipulation of perceptions", IEEE Trans. on Circuits and Systems, vol. 45, pp. 105-119, 1999. http://europepmc.org/abstract/MED/11357866
    [45]
    P. Klement, R. Mesiar, and E. Pap, Triangular Norms. Kluwer Academic Publishers, Dordrecht, 2000.
    [46]
    B. Schweizer and A. Sklar, Probabilistic Metric Spaces, North-Holland, New York, 1983.
    [47]
    W. Pedrycz, "Shadowed sets: representing and processing fuzzy sets", IEEE Trans. on Systems, Man, and Cybernetics, Part B, vol. 28, pp. 103-109, 1998. doi: 10.1109/3477.658584
    [48]
    W. Pedrycz, "Interpretation of clusters in the framework of shadowed sets", Pattern Recognition Letters, vol. 26, no. 15, pp. 2439-2449, 2005. doi: 10.1016/j.patrec.2005.05.001
    [49]
    Z. Pawlak, "Rough sets", International Journal of Information and Computer Science, vol. 11, no. 15, pp. 341-356, 1982. http://d.old.wanfangdata.com.cn/Periodical/zdhxb200103002
    [50]
    Z. Pawlak, Rough Sets. Theoretical Aspects of Reasoning About Data, Kluwer Academic Publishers, Dordrecht, 1991.
    [51]
    Z. Pawlak, "Rough sets and fuzzy sets", Fuzzy Sets and Systems, vol. 17, no. 1, pp. 99-102, 1985. doi: 10.1016/S0165-0114(85)80029-4
    [52]
    Z. Pawlak and A. Skowron, "Rough sets and Boolean reasoning", Information Sciences, vol. 177, no. 1, pp. 41-73, 2007. doi: 10.1016/j.ins.2006.06.007
    [53]
    Z. Pawlak and A. Skowron, "Rudiments of rough sets", Information Scences, vol. 177, no. 1, pp. 3-27, 2007. doi: 10.1016/j.ins.2006.06.003
    [54]
    K. Hirota, "Concepts of probabilistic sets", Fuzzy Sets and Systems, vol. 5, no. 1, pp. 31-46, 1981. doi: 10.1016/0165-0114(81)90032-4
    [55]
    X. Liu and W. Pedrycz, Axiomatic Fuzzy Set Theory and Its Applications, Springer-Verlag, Berlin, 2009.
    [56]
    K. Forbus. "Qualitative process theory", Artificial Intelligence, vol. 24, pp. 85-168, 1984. doi: 10.1016/0004-3702(84)90038-9
    [57]
    W. Abou-Jaoudé, D. Thieffry, and J. Feret, "Formal derivation of qualitative dynamical models from biochemical networks", Biosystems, vol. 149, pp. 70-112, 2016. doi: 10.1016/j.biosystems.2016.09.001
    [58]
    N. Bolloju, "Formulation of qualitative models using fuzzy logic", Decision Support Systems, vol. 17, no. 4, pp. 275-298, 1996. doi: 10.1016/0167-9236(96)00005-X
    [59]
    F. Guerrin. "Qualitative reasoning about an ecological process: Interpretation in hydroecology", Ecological Modeling, vol. 59, pp. 165-201, 1991. doi: 10.1016/0304-3800(91)90177-3
    [60]
    W. Haider, J. Hu, J. Slay, B. P. Turnbull, and Y. Xie, "Generating realistic intrusion detection system dataset based on fuzzy qualitative modeling", Journal of Network and Computer Applications, 87, pp. 185-192, 2017. doi: 10.1016/j.jnca.2017.03.018
    [61]
    Y. H. Wong, A. B. Rad, and Y. K. Wong, "Qualitative modeling and control of dynamic systems", Engineering Applications of Artificial Intelligence, vol. 10, no. 5, pp. 429-439, 1997. doi: 10.1016/S0952-1976(97)00029-8
    [62]
    J. Žabkar, M. Možina, I. Bratko, and J. Demšar, "Learning qualitative models from numerical data", Artificial Intelligence, 175, pp. 9-10, 1604-1619, 2011. http://d.old.wanfangdata.com.cn/NSTLQK/NSTL_QKJJ0222081431/
    [63]
    M. Li and D. Wang, "Insights into randomized algorithms for neural networks: practical issues and common pitfalls", Information Sciences, pp. 382-383, 170-178, 2017. http://www.sciencedirect.com/science/article/pii/S002002551631917X

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Figures(3)

    Article Metrics

    Article views (2428) PDF downloads(235) Cited by()

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return