NDLI: Anomaly Detection from Incomplete Data

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Anomaly Detection from Incomplete Data

Content Provider	ACM Digital Library
Author	Liu, Siyuan Ni, Lionel M. Chen, Lei
Copyright Year	2014
Abstract	Anomaly detection (a.k.a., outlier or burst detection) is a well-motivated problem and a major data mining and knowledge discovery task. In this article, we study the problem of population anomaly detection, one of the key issues related to event monitoring and population management within a city. Through studying detected population anomalies, we can trace and analyze these anomalies, which could help to model city traffic design and event impact analysis and prediction. Although a significant and interesting issue, it is very hard to detect population anomalies and retrieve anomaly trajectories, especially given that it is difficult to get actual and sufficient population data. To address the difficulties of a lack of real population data, we take advantage of mobile phone networks, which offer enormous spatial and temporal communication data on persons. More importantly, we claim that we can utilize these mobile phone data to infer and approximate population data. Thus, we can study the population anomaly detection problem by taking advantages of unique features hidden in mobile phone data. In this article, we present a system to conduct Population Anomaly Detection (PAD). First, we propose an effective clustering method, correlation-based clustering, to cluster the incomplete location information from mobile phone data (i.e., from mobile call volume distribution to population density distribution). Then, we design an adaptive parameter-free detection method, $\textit{R-scan},$ to capture the distributed dynamic anomalies. Finally, we devise an efficient algorithm, $\textit{BT-miner},$ to retrieve anomaly trajectories. The experimental results from real-life mobile phone data confirm the effectiveness and efficiency of the proposed algorithms. Finally, the proposed methods are realized as a pilot system in a city in China.
Starting Page	1
Ending Page	22
Page Count	22
File Format	PDF
ISSN	15564681
e-ISSN	1556472X
DOI	10.1145/2629668
Volume Number	9
Issue Number	2
Journal	ACM Transactions on Knowledge Discovery from Data (TKDD)
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-09-23
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Anomaly detection Anomaly trajectory Correlation-based clustering Mobile phone
Content Type	Text
Resource Type	Article
Subject	Computer Science

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

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