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Metasynthetic Computing and Engineering of Complex Systems pp 313–336Cite as

Learning Complex Behavioral and Social Data

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Part of the book series: Advanced Information and Knowledge Processing ((AI&KP))

Abstract

Behavioral and social applications are ubiquitous, ranging from business and online applications to social and organizational applications and domains. With the increasing and continuous development of such applications, an emerging need is to develop an in-depth understanding of the underlying working mechanism, driving force, dynamics and evolution of a behavioral and/or social system, as well as the impact on business and context. To this end, building on the classic theories and tools available in behavioral science, social science, behavior informatics (Cao L, Inform Sci 180:3067–3085, 2010; Cao L, Yu PS (eds), Behavior computing: modeling, analysis, mining and decision. Springer, Berlin, 2012), and social informatics (Liu H, Salerno J, Young MJ (eds), Social computing, behavioral modeling, and prediction. Springer, Berlin, 2008) have recently been studied to “formalize,” “quantify,” and “compute” complex behavioral and social applications.

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Notes

  1. 1.

    See more from the IEEE Task Force on Behavioral, Economic, and Socio-cultural Computing: www.bsic.info.

References

  1. Cao, L.: In-depth behavior understanding and use: the behavior informatics approach. Inform. Sci. 180, 3067–3085 (2010)

    Article  Google Scholar 

  2. Cao, L., Yu, P.S. (eds.): Behavior Computing: Modeling, Analysis, Mining and Decision. Springer, London (2012)

    Google Scholar 

  3. Liu, H., Salerno, J., Young, M.J. (eds.): Social Computing, Behavioral Modeling, and Prediction. Springer, Berlin (2008)

    Google Scholar 

  4. Steinwart, I., Christmann, A.: Fast learning from non-i.i.d. observations. In: Bengio, Y., Schuurmans, D., Lafferty, J., Williams, C.K.I., Culotta, A. (eds.) Advances in Neural Information Processing Systems, vol. 22, pp. 1768–1776. MIT Press (2009)

    Google Scholar 

  5. Zhang, X., Song, L., Gretton, A., Smola, A.: Kernel measures of independence for non-iid data. In: Proceedings of NIPS2009, pp. 1937–1944. MIT Press (2009)

    Google Scholar 

  6. Pan, Z., Xiao, Q.: Least-square regularized regression with non-iid sampling. J. Statist. Plan Inference 139, 3579–3587 (2009)

    Article  MATH  MathSciNet  Google Scholar 

  7. Guo, Z., Shi, L.: Classification with non-iid Sampling. Math. Comput. Model. 54, 1347–1364 (2011)

    Article  MATH  MathSciNet  Google Scholar 

  8. Cinbis, R., Verbeek, J., Schmid, C.: Image categorization using Fisher kernels of non-iid image models. In: Proceedings of CVPR2012, pp. 2184–2191. IEEE Press (2012)

    Google Scholar 

  9. Mohri, M., Rostamizadeh, A.: Rademacher complexity bounds for non-i.i.d. processes. In: Bengio, Y., Schuurmans, D., Lafferty, J., Williams, C.K.I., Culotta, A. (eds.) Advances in Neural Information Processing Systems, pp. 1097–1104. MIT Press (2009)

    Google Scholar 

  10. Mohri, M., Rostamizadeh, A.: Stability bounds for stationary ϕ-mixing and β-mixing processes. J. Mach. Learn. Res. 11, 789–814 (2010)

    MATH  MathSciNet  Google Scholar 

  11. Chazottes, J., Collet, P., Kulske, C., Redig, F.: Concentration inequalities for random fields via coupling. Probab. Theory Relat. Fields 137, 201–225 (2007)

    Article  MATH  MathSciNet  Google Scholar 

  12. Kontorovich, L., Ramanan, K.: Concentration inequalities for dependent random variables via the Martingale method. Ann. Probab. 36, 2126–2158 (2008)

    Article  MATH  MathSciNet  Google Scholar 

  13. Modha, D., Masry, E.: On the consistency in nonparametric estimation under mixing assumptions. IEEE Trans. Inform. Theory 44, 117–133 (1998)

    Article  MATH  MathSciNet  Google Scholar 

  14. Ralaivola, L., Szafranski, M., Stempfel, G.: Chromatic PAC-Bayes bounds for non-IID data: applications to ranking and stationary β-mixing processes. J. Mach. Learn. Res. 11, 1927–1956 (2010)

    MATH  MathSciNet  Google Scholar 

  15. Zhou, Z., Sun, Y., Li, Y.: Multi-instance learning by treating instances as non-I.I.D. samples. In: Proceedings of ICML2009, pp. 1249–1256. Omnipress (2009)

    Google Scholar 

  16. Dundar, M., Krishnapuram, B., Bi, J., Rao, R.: Learning classifiers when the training data is not IID. In: Proceedings of IJCAI2007, pp. 756–761. AAAI Press (2007)

    Google Scholar 

  17. Tillman, R.: Structure learning with independent non-identically distributed data. In: Proceedings of ICML2009, pp. 1041–1048. Omnipress (2009)

    Google Scholar 

  18. Ping, W., Xu, Y., Ren, K., Chi, C., Shen, F.: Non-I.I.D. multi-instance dimensionality reduction by learning a maximum bag margin subspace. In: Proceedings of 24th AAAI Conference on Artificial Intelligence, pp. 551–556. AAAI Press (2010)

    Google Scholar 

  19. Song, L., Smola, A., Gretton, A., Bedo, J., Borgwardt, K.: Feature selection via dependence maximization. J. Mach. Learn. Res. 13, 1393–1434 (2012)

    MATH  MathSciNet  Google Scholar 

  20. Wang, C., Cao, L.: Modeling and analysis of social activity process. In: Cao, L., Yu, P.S. (eds.) Behavior Computing, pp. 21–35. Springer, Berlin (2012)

    Chapter  Google Scholar 

  21. Cao, L., Dai, R.: Open Complex Intelligent Systems. Post & Telecom, Beijing (2008)

    Google Scholar 

  22. Cao, L., Dai, R., Zhou, M.: Metasynthesis: M-space, M-interaction and M-computing for open complex giant systems. IEEE Trans. Syst. Man. Cybern. Part A 39, 1007–1021 (2009)

    Article  Google Scholar 

  23. Cao, L., Zhang, C., Zhou, M.: Engineering open complex agent systems: a case study. IEEE Trans. Syst. Man. Cybern. Part C Appl. Rev. 38, 483–496 (2008)

    Article  Google Scholar 

  24. Cao, L., Zhang, C., Dai, R.: Organization-oriented analysis of open complex agent systems. Int. J. Intell. Cont. Syst. 10, 114–122 (2005)

    Google Scholar 

  25. Cao, L., Luo, D., Zhang, C.: Ubiquitous intelligence in agent mining. In: Proceedings of ADMI, pp. 23–35. Springer (2009)

    Google Scholar 

  26. Cao, L., Yu, P.S., Zhao, Y., Zhang, C.: Domain Driven Data Mining. Springer, Berlin (2010)

    Book  MATH  Google Scholar 

  27. Cao, L.: Domain driven data mining: challenges and prospects. IEEE Trans. Knowl. Data. Eng. 22, 755–769 (2010)

    Article  Google Scholar 

  28. Cao, L.: Combined mining: analyzing object and pattern relations for discovering and constructing complex yet actionable patterns. WIREs Data Min. Knowl. Disc. 3, 140–155 (2013)

    Article  Google Scholar 

  29. Fishbein, M., Ajzen, I.: Predicting and Changing Behavior: The Reasoned Action Approach. Psychology Press, New York (2009)

    Google Scholar 

  30. Fisher, W., Piazza, C., Roane, H. (eds.): Handbook of Applied Behavior Analysis. The Guilford Press, New York (2011)

    Google Scholar 

  31. Burton, E., Shah, S.: Behavioral Finance: Understanding the Social, Cognitive, and Economic Debates. Wiley, New York (2013)

    Google Scholar 

  32. Zhao,Y., Zhang, H., Cao, L., Zhang, C., Bohlscheid, H.: Mining both positive and negative impact-oriented sequential rules from transactional data. In: Proceedings of PAKDD 2009, pp. 656–663. Springer (2009)

    Google Scholar 

  33. Zhao, Y., Zhang, H., Wu, S., Pei J., Cao, L., Zhang, C., Bohlscheid, H.: Debt detection in social security by sequence classification using both positive and negative patterns. In: Proceedings of ECML-PKDD2009. Lecture Notes in Artificial Intelligence, vol. 5782, pp. 648–663. Springer (2009)

    Google Scholar 

  34. Zheng, Z., Zhao, Y., Zuo, Z., Cao, L., Zhang, H., Zhao, Y., Zhang, C.: An efficient GA-based algorithm for mining negative sequential patterns. In: Proceedings of PAKDD 2010, pp. 262–273. Springer (2010)

    Google Scholar 

  35. Dong, X., Zhao, Y., Cao, L., Zhao, Y., Zhang, C., Li, J., Wei, W., Ou, Y.: e-NSP: efficient negative sequential pattern mining based on identified positive patterns without database rescanning. In: Proceedings of CIKM 2011, pp. 825–830. ACM Press (2011)

    Google Scholar 

  36. Breese, J., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. In: UAI-98, pp. 43–52. AAAI Press (1998)

    Google Scholar 

  37. Wang, C., Cao, L. et al.: Coupled nominal similarity in unsupervised learning. In: Proceedings of CIKM 2011, pp. 973–978. ACM Press (2011)

    Google Scholar 

  38. Scott, J.: Social Network Analysis: A Handbook. Sage, Newbury Park (1991)

    Google Scholar 

  39. Kadushin, C.: Understanding Social Networks: Theories, Concepts, and Findings. Oxford University Press, Oxford (2012)

    Google Scholar 

  40. Cao, L., Ou, Y., Yu, P.S.: Coupled behavior analysis with applications. IEEE Trans. Knowl. Data Eng. 24, 1378–1392 (2012)

    Article  Google Scholar 

  41. Wang, C., She, Z., Cao, L.: Coupled attribute analysis on numerical data. In: Proceedings of IJCAI 2013. AAAI Press (2013)

    Google Scholar 

  42. Wang, C., She, Z., Cao, L.: Coupled clustering ensemble: incorporating coupling relationships both between base clusterings and objects. In: Proceedings ICDE 2013, pp. 374–385. IEEE Press (2013)

    Google Scholar 

  43. Li, J., Wang, C., Cao, L., Yu, P.: Efficient selection of globally optimal rules on large imbalanced data based on rule coverage relationship analysis. In: Proceedings of SDM 2013. SIAM/Omni Press (2013)

    Google Scholar 

  44. Yu, Y., Wang, C., Gao, Y., Cao, L., Sun, J.: A coupled clustering approach for items recommendation. In: Proceedings of PAKDD 2013, pp. 365–376. Springer (2013)

    Google Scholar 

  45. Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: GroupLens: an open architecture for collaborative filtering of netnews. In: CSCW Proceedings, pp. 175–186. Morgan Kaufmann (1994)

    Google Scholar 

  46. Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: Proceedings of WWW 2001, pp. 285–295. ACM Press (2001)

    Google Scholar 

  47. Al Mamunur Rashid, S.K.L., Karypis, G., Riedl, J.: ClustKNN: a highly scalable hybrid model- and memory-based CF algorithm. In: Proceedings of WebKDD 2006. ACM Press (2006)

    Google Scholar 

  48. Huang, A., Milne, D., Frank, E., Witten, I.: Clustering documents using a Wikipedia-based concept representation. Adv. Knowl. Disc. Data Min. 5476, 628–636 (2009)

    Google Scholar 

  49. Cheng, X., Miao, D., Wang, C., Cao, L.: Coupled term–term relation analysis for document clustering. In: Proceedings of IJCNN 2013. IEEE Press (2013)

    Google Scholar 

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Authors and Affiliations

  1. Advanced Analytics Institute, University of Technology, Sydney, Sydney, New South Wales, Australia

    Longbing Cao

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  1. Longbing Cao
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Cao, L. (2015). Learning Complex Behavioral and Social Data. In: Metasynthetic Computing and Engineering of Complex Systems. Advanced Information and Knowledge Processing. Springer, London. https://doi.org/10.1007/978-1-4471-6551-4_15

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  • DOI: https://doi.org/10.1007/978-1-4471-6551-4_15

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-6550-7

  • Online ISBN: 978-1-4471-6551-4

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