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Artificial Neural Network for Incremental Data Mining

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Recent Advances in Information Systems and Technologies (WorldCIST 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 569))

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Abstract

In this paper, we present a Health Check process (HC) based on artificial neural network (ANN). Our approach aim is to detect Incremental Apriori deviation (IncA) proposed in previous work used in order to minimize processing time and explore new incoming data only. HC process use germinated infrequent items and generated frequent itemset to run correction according to predicted value. Experiments on datasets show that deviations are detected and IncA generate same results as classic Apriori while saving processing time. Also, experiment results show that HC learning ameliorate with time.

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Author information

Authors and Affiliations

  1. Artificial Intelligence Laboratory (LRIA), Department of Computer Science, USTHB, Bab Ezzouar, Algeria

    Lydia Nahla Driff & Habiba Drias

Authors
  1. Lydia Nahla Driff
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  2. Habiba Drias
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Corresponding authors

Correspondence to Lydia Nahla Driff or Habiba Drias .

Editor information

Editors and Affiliations

  1. DEI/FCT, Universidade de Coimbra, Coimbra, Baixo Mondego, Portugal

    Álvaro Rocha

  2. Nova IMS, Universidade Nova de Lisboa, Lisboa, Portugal

    Ana Maria Correia

  3. College of Engineering, The Ohio State University, Columbus, Ohio, USA

    Hojjat Adeli

  4. DSI/EEUM, Universidade do Minho, Guimarães, Portugal

    Luís Paulo Reis

  5. DIMES, Università della Calabria, Arcavacata di Rende, Italy

    Sandra Costanzo

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Driff, L.N., Drias, H. (2017). Artificial Neural Network for Incremental Data Mining. In: Rocha, Á., Correia, A., Adeli, H., Reis, L., Costanzo, S. (eds) Recent Advances in Information Systems and Technologies. WorldCIST 2017. Advances in Intelligent Systems and Computing, vol 569. Springer, Cham. https://doi.org/10.1007/978-3-319-56535-4_14

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  • DOI: https://doi.org/10.1007/978-3-319-56535-4_14

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-56534-7

  • Online ISBN: 978-3-319-56535-4

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