Work

A Review of Subsequence Time Series Clustering

Year: 2014

Type: review

Abstract: Clustering of subsequence time series remains an open issue in time series clustering. Subsequence time series clustering is used in different fields, such as e-commerce, outlier detection, speech recognition, biological systems, DNA recognition, and text mining. One of the useful fields in the domain of subsequence time series clustering is pattern recognition. To improve this field, a sequence of time series data is used. This paper reviews some definitions and backgrounds related to subsequence time series clustering. The categorization of the literature reviews is divided into three groups: preproof, interproof, and postproof period. Moreover, various state-of-the-art approaches in performing subsequence time series clustering are discussed under each of the following categories. The strengths and weaknesses of the employed methods are evaluated as potential issues for future studies.

Source: The Scientific World JOURNAL

DOI: 10.1155/2014/312521

Authors: Seyedjamal Zolhavarieh, Saeed Aghabozorgi, Ying Wah Teh

Institution: University of Malaya

Language: English

Topic: Time Series Analysis and Forecasting

Subfield: Signal Processing

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $860

Funders: Ministry of Higher Education, Malaysia, Universiti Malaya