Numerical time-series pattern extraction based on irregular piecewise aggregate approximation and gradient specification

Miho Ohsaki, Hidenao Abe, Takahira Yamaguchi

Research output: Contribution to journalArticle

9 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)213-222
Number of pages10
JournalNew Generation Computing
Volume25
Issue number3
DOIs
Publication statusPublished - 2007 Aug 27

Keywords

  • Data mining
  • Knowledge discovery in databases
  • Numerical time-series
  • Pattern extraction
  • Piecewise aggregate approximation

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Hardware and Architecture
  • Computer Networks and Communications

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