Size matters: choosing the most informative set of window lengths for mining patterns in event sequences

DOI10.1007/S10618-014-0397-3zbMath1405.62080OpenAlexW1988936374MaRDI QIDQ1715925

Kai Puolamäki, Panagiotis Papapetrou, Jefrey Lijffijt

Publication date: 29 January 2019

Published in: Data Mining and Knowledge Discovery (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10618-014-0397-3

zbMATH Keywords

exploratory data analysispattern miningwindow lengthevent sequenceoutput-space clustering

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Pattern recognition, speech recognition (68T10) Computational difficulty of problems (lower bounds, completeness, difficulty of approximation, etc.) (68Q17)

Related Items (2)

Size matters: choosing the most informative set of window lengths for mining patterns in event sequences ⋮ SAZED: parameter-free domain-agnostic season length estimation in time series data

Uses Software

• k-means++
• clusfind

Cites Work

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• NP-hardness of Euclidean sum-of-squares clustering
• Size matters: choosing the most informative set of window lengths for mining patterns in event sequences
• Finding Groups in Data

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