NDLI: Similarity Measure Selection for Clustering Time Series Databases

Content Provider	IEEE Xplore Digital Library
Author	Mori, U. Mendiburu, A. Lozano, J.A.
Copyright Year	1989
Abstract	In the past few years, clustering has become a popular task associated with time series. The choice of a suitable distance measure is crucial to the clustering process and, given the vast number of distance measures for time series available in the literature and their diverse characteristics, this selection is not straightforward. With the objective of simplifying this task, we propose a multi-label classification framework that provides the means to automatically select the most suitable distance measures for clustering a time series database. This classifier is based on a novel collection of characteristics that describe the main features of the time series databases and provide the predictive information necessary to discriminate between a set of distance measures. In order to test the validity of this classifier, we conduct a complete set of experiments using both synthetic and real time series databases and a set of five common distance measures. The positive results obtained by the designed classification framework for various performance measures indicate that the proposed methodology is useful to simplify the process of distance selection in time series clustering tasks.
Sponsorship	IEEE IEEE Comput. Soc. Tech. Committee on Data Eng IEEE Computer Society
Starting Page	181
Ending Page	195
Page Count	15
File Size	902422
File Format	PDF
ISSN	10414347
Volume Number	28
Issue Number	1
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2016-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Time series analysis Databases Correlation Time measurement Noise Market research Standards Multi-label classification Time Series Distance measures Clustering multi-label classification Time series distance measures clustering
Content Type	Text
Resource Type	Article
Subject	Information Systems Computational Theory and Mathematics Computer Science Applications

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Document Clustering in Correlation Similarity Measure Space

The Move-Split-Merge Metric for Time Series

Clustering with Multiviewpoint-Based Similarity Measure

Highly Comparative Feature-Based Time-Series Classification

A Similarity Measure for Text Classification and Clustering

A Comment on “A Similarity Measure for Text Classification and Clustering”

A Novel Distance Measure and Semantic Segmentation for Time Series Data

Querying time series data based on similarity

Haar wavelets for efficient similarity search of time-series: with and without time warping

Similarity Measure Selection for Clustering Time Series Databases

Similar Documents

Document Clustering in Correlation Similarity Measure Space

The Move-Split-Merge Metric for Time Series

Clustering with Multiviewpoint-Based Similarity Measure

Highly Comparative Feature-Based Time-Series Classification

A Similarity Measure for Text Classification and Clustering

A Comment on “A Similarity Measure for Text Classification and Clustering”

A Novel Distance Measure and Semantic Segmentation for Time Series Data

Querying time series data based on similarity

Haar wavelets for efficient similarity search of time-series: with and without time warping

Similarity Measure Selection for Clustering Time Series Databases