Mining temporal fluctuating patterns

Shan Yun Teng, Cheng Kuan Ou, Kun Ta Chuang

Research output: Chapter in Book/Report/Conference proceedingConference contribution


In this paper, we explore a new mining paradigm, called Temporal Fluctuating Patterns (abbreviated as TFP), to discover potentially fluctuating and useful feature sets from temporal data. These feature sets have some properties which are variant through time series. Once TFPs are discovered, we can find the turning points of patterns, which enables anomaly detection and transformation discovery over time. For example, the discovery of TFPs can possibly figure out the phenomenon of virus variation during the epidemic outbreak, further providing the government the clue for the epidemic control. However, previous work on mining temporal data computes frequent sets iteratively for different time periods, which is time-consuming. We, therefore, develop a union-based mining structure to speed up the mining process and dynamically compute the fluctuations of patterns through time series. As shown in our experimental studies, the proposed framework can efficiently discover TFPs on a real epidemic disease dataset, showing its prominent advantages to be utilized in real applications.

Original languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining - 21st Pacific-Asia Conference, PAKDD 2017, Proceedings
EditorsKyuseok Shim, Jae-Gil Lee, Longbing Cao, Xuemin Lin, Jinho Kim, Yang-Sae Moon
PublisherSpringer Verlag
Number of pages13
ISBN (Print)9783319574530
Publication statusPublished - 2017
Event21st Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2017 - Jeju, Korea, Republic of
Duration: 2017 May 232017 May 26

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10234 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other21st Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2017
Country/TerritoryKorea, Republic of

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


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