Numerical Time-Series Pattern Extraction Based on Irregular Piecewise Aggregate Approximation and Gradient Specification

This paper proposes and evaluates a method for extracting interesting patterns from numerical time-series data which takes account of user subjectivity. The proposed method conducts irregular sampling on the data preserving the subjectively noteworthy features using a user specified gradient. It also conducts irregular quantization, preserving the intrinsically objective characteristics of the data using statistical distributions. It then extracts representative patterns from the discretized data using group average clustering. Experimental results using benchmark datasets indicate that the proposed method does not destroy the intrinsically objective features, since it has the same performance as the basic subsequence clustering using K-Means algorithm. Results using a dataset from a clinical hepatitis study indicate that it extracts interesting patterns for a medical expert.