ORIGINAL RESEARCH article
Front. Big Data
Sec. Data Mining and Management
Volume 8 - 2025 | doi: 10.3389/fdata.2025.1600267
Sliding Window Based Rare Partial Periodic Pattern Mining Algorithms over Temporal Data Streams
Provisionally accepted
Jyothi Upadhya K
Ronan LoboMini Shail ChhabraAman PalejaB. Dinesh Rao
GEETHA M*Prachi SisodiaBolusani Akshita Reddy- Manipal Academy of Higher Education, Manipal, India
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Periodic pattern mining, a branch of data mining, is expanding to provide insight into the occurrence behaviour of large volumes of data. Recently, a variety of industries, including fraud detection, telecommunications, retail marketing, research, and medical have found applications for rare association rule mining, which uncovers unusual or unexpected combinations. A limited amount of literature demonstrated how periodicity is essential in mining low-support rare patterns.In addition, attention must be placed on temporal datasets that analyze crucial information about the timing of pattern occurrences and stream datasets to manage high-speed streaming data. Several algorithms have been developed that effectively track the cyclic behaviour of patterns and identify the patterns that display complete or partial periodic behaviour in temporal datasets. Numerous frameworks have been created to examine the periodic behaviour of streaming data. Nevertheless, such a method that focuses on the temporal information in the data stream and extracts rare partial periodic patterns has yet to be proposed. With a focus on identifying rare partial periodic patterns from temporal data streams, this paper proposes two novel sliding window-based single scan approaches called R3PStreamSW-Growth and R3PStreamSW-BitVectorMiner. The findings showed that when a dense dataset Accidents is considered, for different threshold variations R3P-StreamSWBitVectorMiner outperformed R3PStreamSW-Growth by about 93%. Similarly, when the sparse dataset T10I4D100K is taken into account, R3P-StreamSWBitVectorMiner exhibits a 90% boost in performance. This demonstrates that on a range of synthetic, real-world, sparse, and dense datasets for different thresholds, R3P-StreamSWBitVectorMiner is significantly faster than R3PStreamSW-Growth.
Keywords: Partial Periodic Mining, Rare Partial Periodic Pattern Mining, Rare Periodic Pattern Mining, Stream Periodic Pattern Mining, Tree-based Stream Mining, List-based Stream Mining
Received: 26 Mar 2025; Accepted: 07 May 2025.
Copyright: © 2025 Upadhya K, Lobo, Chhabra, Paleja, Rao, M, Sisodia and Reddy. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: GEETHA M, Manipal Academy of Higher Education, Manipal, India
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