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Incremental learning optimization on knowledge discovery in dynamic business intelligent systems

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Abstract

As business information quickly varies with time, the extraction of knowledge from the related dynamically changing database is vital for business decision making. For an incremental learning optimization on knowledge discovery, a new incremental matrix describes the changes of the system. An optimization incremental algorithm induces interesting knowledge when the object set varies over time. Experimental results validate the feasibility of the incremental learning optimization.

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Authors and Affiliations

  1. School of Economics and Management, Southwest Jiaotong University, 610031, Chengdu, People’s Republic of China

    Dun Liu

  2. School of Information Science and Technology, Southwest Jiaotong University, 610031, Chengdu, People’s Republic of China

    Tianrui Li & Junbo Zhang

  3. Belgian Nuclear Research Centre (SCK•CEN), Boeretang 200, 2400, Mol, Belgium

    Da Ruan

  4. Department of Applied Mathematics & Computer Science, Ghent University, 9000, Ghent, Belgium

    Da Ruan

Authors
  1. Dun Liu
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  2. Tianrui Li
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  3. Da Ruan
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  4. Junbo Zhang
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Correspondence to Dun Liu.

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Liu, D., Li, T., Ruan, D. et al. Incremental learning optimization on knowledge discovery in dynamic business intelligent systems. J Glob Optim 51, 325–344 (2011). https://doi.org/10.1007/s10898-010-9607-8

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