Adaptive cluster tendency visualization and anomaly detection for streaming data

Kumar, Dheeraj, Bezdek, James C., Rajasegarar, Sutharshan, Palaniswami, Marimuthu, Leckie, Christopher, Chan, Jeffrey and Gubbi, Jayavardhana 2016, Adaptive cluster tendency visualization and anomaly detection for streaming data, ACM transactions on knowledge discovery from data, vol. 11, no. 2, Article Number : 24, pp. 1-40, doi: 10.1145/2997656.

Attached Files
Name Description MIMEType Size Downloads

Title Adaptive cluster tendency visualization and anomaly detection for streaming data
Author(s) Kumar, Dheeraj
Bezdek, James C.
Rajasegarar, SutharshanORCID iD for Rajasegarar, Sutharshan
Palaniswami, Marimuthu
Leckie, Christopher
Chan, Jeffrey
Gubbi, Jayavardhana
Journal name ACM transactions on knowledge discovery from data
Volume number 11
Issue number 2
Season Article Number : 24
Start page 1
End page 40
Total pages 40
Publisher ACM
Place of publication New York, N.Y.
Publication date 2016-12
ISSN 1556-4681
Keyword(s) hierarchical data models
heat maps
visual analytics
sublinear and near linear time algorithms
time series analysis
Summary The growth in pervasive network infrastructure called the Internet of Things (IoT) enables a wide range of physical objects and environments to be monitored in fine spatial and temporal detail. The detailed, dynamic data that are collected in large quantities from sensor devices provide the basis for a variety of applications. Automatic interpretation of these evolving large data is required for timely detection of interesting events. This article develops and exemplifies two new relatives of the visual assessment of tendency (VAT) and improved visual assessment of tendency (iVAT) models, which uses cluster heat maps to visualize structure in static datasets. One new model is initialized with a static VAT/iVAT image, and then incrementally (hence inc-VAT/inc-iVAT) updates the current minimal spanning tree (MST) used by VAT with an efficient edge insertion scheme. Similarly, dec-VAT/dec-iVAT efficiently removes a node from the current VAT MST. A sequence of inc-iVAT/dec-iVAT images can be used for (visual) anomaly detection in evolving data streams and for sliding window based cluster assessment for time series data. The method is illustrated with four real datasets (three of them being smart city IoT data). The evaluation demonstrates the algorithms' ability to successfully isolate anomalies and visualize changing cluster structure in the streaming data.
Language eng
DOI 10.1145/2997656
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 960699 Environmental and Natural Resource Evaluation not elsewhere classified
HERDC Research category C1.1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Grant ID LP120100529
Copyright notice ©2016, ACM
Persistent URL

Connect to link resolver
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 8 times in TR Web of Science
Scopus Citation Count Cited 10 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 521 Abstract Views, 2 File Downloads  -  Detailed Statistics
Created: Thu, 19 Jan 2017, 13:59:41 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact