NDLI: An angle-based dissimilarity for accelerating the clustering of dynamic data in networks

Content Provider	IEEE Xplore Digital Library
Author	Cangqi Zhou Qianchuan Zhao Ruixi Yuan
Copyright Year	2013
Description	Author affiliation: Dept. of Autom., Tsinghua Univ., Beijing, China (Cangqi Zhou; Qianchuan Zhao; Ruixi Yuan)
Abstract	Mining time series data is of great significance in various areas. In order to efficiently find representative patterns in these data sets, this paper focuses on the definition of a valid dissimilarity and the acceleration of partitioning clustering, a common technique used to discover typical shapes of time series. Following the analysis of adopting the angle between two time series as the measure of dissimilarity, our definition, which is invariant to specific transformations, has been proposed. Moreover, our dissimilarity obeys the triangle inequality with specific restrictions. This property can be employed to accelerate clustering. An integrated algorithm is proposed. Experiments show that the angle-based dissimilarity captures the essence of time series patterns that are invariant to amplitude scaling. In addition, our algorithm provides a feasible way to update cluster centers, as well as an effective approach to accelerating clustering. Our accelerated algorithm reduces the number of dissimilarity calculations by almost an order of magnitude.
Sponsorship	IEEE Syst., Man, Cybern. Soc.
Starting Page	199
Ending Page	204
File Size	216673
Page Count	6
File Format	PDF
ISBN	9781467351980
e-ISBN	9781467352000
e-ISBN	9781467351997
DOI	10.1109/ICNSC.2013.6548736
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-04-10
Publisher Place	France
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Time series analysis Acceleration Vectors Clustering algorithms Shape Time measurement Indexes triangle inequality Time series data dissimilarity measure clustering acceleration
Content Type	Text
Resource Type	Article

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

A similarity measure for time series of spatial lines intersection relations

Using fuzzy neural network clustering algorithm in the symbolization of time series

AFFINITY: Efficiently querying statistical measures on time-series data

A dissimilarity measure for clustering high- and infinite dimensional data that satisfies the triangle inequality (2002).

A comparative analysis of dissimilarity measures for clustering categorical data

A dissimilarity measure for clustering high- and infinite dimensional data that satisfies the triangle inequality

A novel feature measure for fuzzy clustering algorithm on microarray data

Time series clustering based on shape dynamic time warping using cloud models

An incremental clustering of gene expression data

An angle-based dissimilarity for accelerating the clustering of dynamic data in networks

Similar Documents

A similarity measure for time series of spatial lines intersection relations

Using fuzzy neural network clustering algorithm in the symbolization of time series

AFFINITY: Efficiently querying statistical measures on time-series data

A dissimilarity measure for clustering high- and infinite dimensional data that satisfies the triangle inequality (2002).

A comparative analysis of dissimilarity measures for clustering categorical data

A dissimilarity measure for clustering high- and infinite dimensional data that satisfies the triangle inequality

A novel feature measure for fuzzy clustering algorithm on microarray data

Time series clustering based on shape dynamic time warping using cloud models

An incremental clustering of gene expression data

An angle-based dissimilarity for accelerating the clustering of dynamic data in networks