Recently, the research on efficient extraction of previously unknown, frequently appearing patterns in a time-series data has received much attention. These patterns are called 'motifs'. Motifs are useful for various time-series data mining tasks. In this paper, we propose a motif discovery algorithm to extract a motif that represents a characteristic pattern of the given data based on Minimum Description Length (MDL) principle. In addition, the algorithm can extract motifs from multi-dimensional time-series data by using Principal Component Analysis (PCA). In experimental evaluation, we show the efficiency of the motif discovery algorithm, and the usefulness of extracted motifs to various data mining tasks.
Tanaka et al. proposed an algorithm that could discover time-series motifs from multi-dimensional human motion data based on the minimum description length (MDL) principle REF .
Discovery of Time-Series Motif from Multi-Dimensional Data Based on MDL Principle
Machine Learning
Machine Learning
Computer Science Mathematics