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Sliding window based rare partial periodic pattern mining algorithms over temporal data streams.

K Jyothi Upadhya1, Ronan Lobo1, Mini Shail Chhabra1

  • 1Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, India.

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This study introduces two new methods, R3PStreamSW-Growth and R3PStreamSW-BitVectorMiner, for finding rare partial periodic patterns in temporal data streams. R3PStreamSW-BitVectorMiner significantly outperforms R3PStreamSW-Growth in speed and efficiency across various datasets.

Keywords:
list-based stream miningpartial periodic miningrare partial periodic pattern miningrare periodic pattern miningstream periodic pattern miningtree-based stream mining

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Area of Science:

  • Data Mining
  • Pattern Recognition
  • Big Data Analytics

Background:

  • Periodic pattern mining is crucial for understanding large datasets across various industries.
  • Existing methods struggle to extract rare partial periodic patterns from temporal data streams.
  • Temporal and stream datasets require specialized algorithms for analyzing pattern occurrences.

Purpose of the Study:

  • To propose novel algorithms for extracting rare partial periodic patterns from temporal data streams.
  • To address the limitations of current methods in handling temporal information in data streams.
  • To develop efficient single-scan approaches for real-time pattern mining.

Main Methods:

  • Introduced two sliding window-based single scan algorithms: R3PStreamSW-Growth and R3PStreamSW-BitVectorMiner.
  • Focused on mining rare partial periodic patterns within temporal data streams.
  • Evaluated algorithm performance on dense and sparse datasets, including Accidents and T10I4D100K.

Main Results:

  • R3PStreamSW-BitVectorMiner demonstrated superior performance over R3PStreamSW-Growth.
  • Performance gains of approximately 93% were observed on the dense Accidents dataset.
  • A 90% performance boost was noted on the sparse T10I4D100K dataset for R3PStreamSW-BitVectorMiner.

Conclusions:

  • R3PStreamSW-BitVectorMiner is significantly faster and more efficient than R3PStreamSW-Growth.
  • The proposed methods effectively extract rare partial periodic patterns from temporal data streams.
  • The findings highlight the potential of R3PStreamSW-BitVectorMiner for real-world applications in data stream analysis.