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International Conference on Mining Intelligence and Knowledge Exploration

MIKE 2016: Mining Intelligence and Knowledge Exploration pp 1–12Cite as

An Efficient Incremental Mining Algorithm for Dynamic Databases

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10089))

Abstract

Data mining is aimed to extract hidden acknowledge from large dataset, in order to exploit it for predicting future trends and make decisions. Extracting meaningful and useful candidate optimally is handled by several algorithms, mainly those based on exploring incoming data, which can lose information. To address this issue, this paper proposes an algorithm named Incremental Apriori (IncA) for discovering frequent itemsets in transaction databases, which is in fact a variant of the well-known Apriori algorithm. In IncA, we introduce a notion of promising items generated from the original database, an incremental technique applied on incremental database and a health check process to ensure candidate generation completeness. On the theoretical side, our algorithm exhibits the best computational complexity compared to the recent state-of-the-art algorithms. On the other hand, we tested the proposed approach on large synthetic databases. The obtained results prove that IncA reduces the running time as well as the search space and also show that our algorithm performs better than the Apriori algorithm.

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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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Correspondence to Lydia Nahla Driff .

Editor information

Editors and Affiliations

  1. Norwegian University of Science and Technology, Trondheim, Norway

    Rajendra Prasath

  2. Center for Computing Research, CIC, National Polytechnic Institute, IPN, Mexico City, Distrito Federal, Mexico

    Alexander Gelbukh

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Driff, L.N., Drias, H. (2017). An Efficient Incremental Mining Algorithm for Dynamic Databases. In: Prasath, R., Gelbukh, A. (eds) Mining Intelligence and Knowledge Exploration. MIKE 2016. Lecture Notes in Computer Science(), vol 10089. Springer, Cham. https://doi.org/10.1007/978-3-319-58130-9_1

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  • DOI: https://doi.org/10.1007/978-3-319-58130-9_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-58129-3

  • Online ISBN: 978-3-319-58130-9

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