research-article

Adaptive Cluster Tendency Visualization and Anomaly Detection for Streaming Data

Authors:

James C. Bezdek,

Sutharshan Rajasegarar,

Marimuthu Palaniswami,

Christopher Leckie,

Jayavardhana GubbiAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 11, Issue 2

Article No.: 24, Pages 1 - 40

https://doi.org/10.1145/2997656

Published: 03 December 2016 Publication History

Abstract

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.

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cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 11, Issue 2

May 2017

419 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/3017677

Editor:
Philip S. Yu
University of Illinois at Chicago, USA

Issue’s Table of Contents

Copyright © 2016 ACM.

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Publication History

Published: 03 December 2016

Accepted: 01 September 2016

Revised: 01 May 2016

Received: 01 June 2015

Published in TKDD Volume 11, Issue 2

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https://doi.org/10.4018/979-8-3693-4240-4.ch012
Deshpande KKumar D(2024)Time and Memory Scalable Algorithms for Clustering Tendency Assessment of Big DataInformation Sciences10.1016/j.ins.2024.120324(120324)Online publication date: Feb-2024
https://doi.org/10.1016/j.ins.2024.120324
Hathaway EHathaway R(2023)Colorized iVAT Images for Labeled DataAdvances in Science, Technology and Engineering Systems Journal10.25046/aj0804138:4(111-121)Online publication date: Aug-2023
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