Efficient Discovery of Partial Periodic Patterns in Large Temporal Databases

• DOI: 10.3390/electronics11101523
• Publication Date: 2022-05-10T12:31:55Z
• Authors: Rage Uday Kiran; Pamalla Veena; Penugonda Ravikumar; Chennupati Saideep; Koji Zettsu; Haichuan Shang; Masashi Toyoda; Masaru Kitsuregawa; P. Krishna Reddy
• Keywords: Temporal database; Spatiotemporal database

Periodic pattern mining is an emerging technique for knowledge discovery. Most previous approaches have aimed to find only those patterns that exhibit full (or perfect) periodic behavior in databases. Consequently, the existing miss interesting partial a database. With this motivation, paper proposes novel model finding may exist temporal An efficient pattern-growth algorithm, called Partial Pattern-growth (3P-growth), also presented, which can effectively all desired within Substantial experiments on both real-world and synthetic databases showed our algorithm not terms of memory runtime, but highly scalable. Finally, effectiveness demonstrated using two case studies. In first study, was employed identify polluted areas Japan. second road segments people regularly face traffic congestion.