Abstract
In this paper, we suggest a pruning technique to discover weighted support affinity patterns in which an objective measure, sequential ws-confidence is developed to detect correlated sequential patterns with weighted support affinity patterns. Based on the pruning technique, we develop a weighted support affinity pattern mining algorithm (WSMiner). Our performance study shows that WSMiner is efficient and scalable for mining weighted support affinity patterns.
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Yun, U. (2006). A Pruning Technique to Discover Correlated Sequential Patterns in Retail Databases. In: Sattar, A., Kang, Bh. (eds) AI 2006: Advances in Artificial Intelligence. AI 2006. Lecture Notes in Computer Science(), vol 4304. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11941439_117
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DOI: https://doi.org/10.1007/11941439_117
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-49787-5
Online ISBN: 978-3-540-49788-2
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