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Adaptive Self-Sufficient Itemset Miner for Transactional Data Streams

  • Feiyang TangEmail author
  • David Tse Jung HuangEmail author
  • Yun Sing Koh
  • Philippe Fournier-Viger
Conference paper
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Part of the Lecture Notes in Computer Science book series (LNCS, volume 11671)

Abstract

Most studies on pattern mining consider itemsets that have a high frequency of occurrence as useful, often determined by the support of the itemsets. However, current research has shown that we need to move beyond a pure “support-confidence” framework for pattern mining. Recently, there is an interest on finding statistically significant patterns and one of the most popular type of patterns is self-sufficient itemsets. One limitation is that these works do not consider concept drifts and cannot be used in a data stream. Learning in the online environment requires us to develop efficient and effective mechanisms to address the online characteristics of non-static data and non-stationary data distributions. In our research we will concentrate on detecting self-sufficient itemsets from data streams. These patterns have a frequency that is significantly different from the frequency of their subsets and supersets. We present a comprehensive framework for mining self-sufficient itemsets from data streams along with a drift detector. This supports mining self-sufficient itemsets in an online environment and provides the ability to adapt to changes in the stream. Our experimental evaluations show that our framework can mine self-sufficient itemsets faster in an online environment and with better precision and recall.

Keywords

Data stream mining Batch processing Self-sufficient itemsets Association rule mining Drift detection 
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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.School of Computer ScienceThe University of AucklandAucklandNew Zealand
  2. 2.School of Humanities and Social SciencesHarbin Institute of Technology (Shenzhen)ShenzhenChina

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