An efficient parallel algorithm for mining weighted clickstream patterns

•We propose a parallel depth-first search with dynamic load balancing.•We propose a parallel algorithm called PCompact-SPADE for mining weighted frequent clickstream patterns.•We experiment on various datasets to illustrate the algorithm’s performance and scalability. In the Internet age, analyzing...

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Bibliographic Details
Published in:	Information sciences Vol. 582; pp. 349 - 368
Main Authors:	Huynh, Huy M., Nguyen, Loan T.T., Vo, Bay, Oplatková, Zuzana Komínková, Fournier-Viger, Philippe, Yun, Unil
Format:	Journal Article
Language:	English
Published:	Elsevier Inc 01.01.2022
Subjects:	Frequent pattern mining Parallelism Weighted clickstream patterns Frequent pattern mining Parallelism Weighted clickstream patterns
ISSN:	0020-0255, 1872-6291
Online Access:	Get full text
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Summary:	•We propose a parallel depth-first search with dynamic load balancing.•We propose a parallel algorithm called PCompact-SPADE for mining weighted frequent clickstream patterns.•We experiment on various datasets to illustrate the algorithm’s performance and scalability. In the Internet age, analyzing the behavior of online users can help webstore owners understand customers’ interests. Insights from such analysis can be used to improve both user experience and website design. A prominent task for online behavior analysis is clickstream mining, which consists of identifying customer browsing patterns that reveal how users interact with websites. Recently, this task was extended to consider weights to find more impactful patterns. However, most algorithms for mining weighted clickstream patterns are serial algorithms, which are sequentially executed from the start to the end on one running thread. In real life, data is often very large, and serial algorithms can have long runtimes as they do not fully take advantage of the parallelism capabilities of modern multi-core CPUs. To address this limitation, this paper presents two parallel algorithms named DPCompact-SPADE (Depth load balancing Parallel Compact-SPADE) and APCompact-SPADE (Adaptive Parallel Compact-SPADE) for weighted clickstream pattern mining. Experiments on various datasets show that the proposed parallel algorithm is efficient, and outperforms state-of-the-art serial algorithms in terms of runtime, memory consumption, and scalability.
ISSN:	0020-0255 1872-6291
DOI:	10.1016/j.ins.2021.08.070