NDLI: Clustering Time Series with Clipped Data

Content Provider	Springer Nature Link
Author	Bagnall, Anthony Janacek, Gareth
Copyright Year	2005
Abstract	Clustering time series is a problem that has applications in a wide variety of fields, and has recently attracted a large amount of research. Time series data are often large and may contain outliers. We show that the simple procedure of clipping the time series (discretising to above or below the median) reduces memory requirements and significantly speeds up clustering without decreasing clustering accuracy. We also demonstrate that clipping increases clustering accuracy when there are outliers in the data, thus serving as a means of outlier detection and a method of identifying model misspecification. We consider simulated data from polynomial, autoregressive moving average and hidden Markov models and show that the estimated parameters of the clipped data used in clustering tend, asymptotically, to those of the unclipped data. We also demonstrate experimentally that, if the series are long enough, the accuracy on clipped data is not significantly less than the accuracy on unclipped data, and if the series contain outliers then clipping results in significantly better clusterings. We then illustrate how using clipped series can be of practical benefit in detecting model misspecification and outliers on two real world data sets: an electricity generation bid data set and an ECG data set.
Starting Page	151
Ending Page	178
Page Count	28
File Format	PDF
ISSN	08856125
Journal	Machine Learning
Volume Number	58
Issue Number	2-3
e-ISSN	15730565
Language	English
Publisher	Kluwer Academic Publishers
Publisher Date	2005-01-01
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	clustering time series clipping Computing Methodologies Artificial Intelligence (incl. Robotics) Simulation and Modeling Language Translation and Linguistics Automation and Robotics
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Software

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

Correlation Clustering

Optimal Time Bounds for Approximate Clustering

Central Clustering of Attributed Graphs

A New Conceptual Clustering Framework

A Unified View on Clustering Binary Data

Introduction: Special Issue on Theoretical Advances in Data Clustering

Boosting and Microarray Data

Probabilistic clustering of time-evolving distance data

Functional Trees

Clustering Time Series with Clipped Data

Similar Documents

Correlation Clustering

Optimal Time Bounds for Approximate Clustering

Central Clustering of Attributed Graphs

A New Conceptual Clustering Framework

A Unified View on Clustering Binary Data

Introduction: Special Issue on Theoretical Advances in Data Clustering

Boosting and Microarray Data

Probabilistic clustering of time-evolving distance data

Functional Trees

Clustering Time Series with Clipped Data