Cluster Computing

, Volume 22, Supplement 4, pp 9651–9660 | Cite as

Pattern mining model based on improved neural network and modified genetic algorithm for cloud mobile networks

  • Peng ZhangEmail author
  • Qing Guo
  • Shuai Zhang
  • Harry Haoxiang Wang


The need of individual finance has been developing quickly as of late, and along these lines enormous quantities of purchasers’ the credit information are gathered by the bureau of credit that are tied up with the money related division. The individual finance scoring chief frequently assesses the buyer’s credit with instinctive experience. Be that as it may, with the support of the credit grouping model, the chief can precisely assess the candidate’s money related score. Data mining (DM) is turning out to be deliberately vital range for some business associations including budgetary sectoring segment. It is a procedure of breaking down the information from different points of view and outlining it into important data. This study utilized three techniques to develop the cross breed bolster vector machine-based individual finance score models to assess the candidate’s close to home back score from the candidate’s information highlights. Two distinctive acknowledge datasets are chosen as the exploratory information to exhibit the precision of the support vector machine (SVM) classifier of DM. Contrasted and neural systems, genetic programming, and decision tree classifiers, the SVM classifier of DM accomplished an indistinguishable classificatory precision with moderately little information highlights. Also, consolidating genetic algorithms (GAs) with SVM classifier of DM gives the propelled approach. The proposed novel amalgam (NA)-SVM–GA, for recognizing the individual back score alongside money related trick discovery. Test comes about demonstrate that SVM classifier of DM is a promising expansion to the current techniques.


Data mining F-measure Novel amalgam-support vector machine-genetic algorithm (NA-SVM–GA) Personal finance score along with financial scam detection 


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2017

Authors and Affiliations

  • Peng Zhang
    • 1
    Email author
  • Qing Guo
    • 1
  • Shuai Zhang
    • 1
  • Harry Haoxiang Wang
    • 2
  1. 1.School of Aeronautics and Astronautics EngineeringAir Force Engineering UniversityXi’anChina
  2. 2.Cornell UniversityIthacaUSA

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