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Anomaly Detection in Time-Series of ...

Park, Youngser.

Anomaly Detection in Time-Series of Graphs and Hypergraphs using Graph Features.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
書名/作者:	Anomaly Detection in Time-Series of Graphs and Hypergraphs using Graph Features.
作者:	Park, Youngser.
面頁冊數:	146 p.
附註:	Source: Dissertation Abstracts International, Volume: 72-06, Section: B, page: 3536.
Contained By:	Dissertation Abstracts International72-06B.
標題:	Statistics.
標題:	Computer Science.
ISBN:	9781124549361
摘要、提要註:	The ability to mine data represented as a graph has become important in several domains for detecting various structural patterns, but less work has been done in terms of detecting anomalies in graph-based data. Also, time-series of graphs are becoming more and more common, for example, communication graphs, social networks, etc., and methods for statistical inferences are demanding. While there has been some previous work that used statistical metrics and conditional entropy measurements, the results have been limited to certain types of anomalies and specific domains.
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3445387

Anomaly Detection in Time-Series of Graphs and Hypergraphs using Graph Features.
Park, Youngser.

Anomaly Detection in Time-Series of Graphs and Hypergraphs using Graph Features. - 146 p.

Source: Dissertation Abstracts International, Volume: 72-06, Section: B, page: 3536.

Thesis (Ph.D.)--The George Washington University, 2011.

The ability to mine data represented as a graph has become important in several domains for detecting various structural patterns, but less work has been done in terms of detecting anomalies in graph-based data. Also, time-series of graphs are becoming more and more common, for example, communication graphs, social networks, etc., and methods for statistical inferences are demanding. While there has been some previous work that used statistical metrics and conditional entropy measurements, the results have been limited to certain types of anomalies and specific domains.

ISBN: 9781124549361Subjects--Topical Terms:

145349
Statistics.

Anomaly Detection in Time-Series of Graphs and Hypergraphs using Graph Features.
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020 $a 9781124549361
035 $a (UMI)AAI3445387
035 $a AAI3445387
040 $a UMI $c UMI
100 1 $a Park, Youngser. $3 475349
245 1 0 $a Anomaly Detection in Time-Series of Graphs and Hypergraphs using Graph Features.
300 $a 146 p.
500 $a Source: Dissertation Abstracts International, Volume: 72-06, Section: B, page: 3536.
500 $a Adviser: Abdou Youssef.
502 $a Thesis (Ph.D.)--The George Washington University, 2011.
520 $a The ability to mine data represented as a graph has become important in several domains for detecting various structural patterns, but less work has been done in terms of detecting anomalies in graph-based data. Also, time-series of graphs are becoming more and more common, for example, communication graphs, social networks, etc., and methods for statistical inferences are demanding. While there has been some previous work that used statistical metrics and conditional entropy measurements, the results have been limited to certain types of anomalies and specific domains.
520 $a Moreover, most anomaly-detection methods use a supervised approach, which requires some sort of baseline of information from which comparisons or training can be performed. In general, if one has an idea what is normal behavior, deviations from that behavior could constitute an anomaly. However, the issue with those approaches is that one has to train the system, and the data has to already be labeled. It is also known that no single graph feature is uniformly most powerful.
520 $a This research introduced a theory of scan statistics on time series of graphs and hypergraphs to investigate an effectiveness on anomaly/change point detection problem. The primary research hypothesis of this work is that scan statistic is capable of detecting certain types of anomalies that are not apparent by using other techniques. Also, by combining multiple graph features, the performance of statistical inference can be improved compared to a method that only uses an individual feature separately.
520 $a The result shows that the proposed statistics on time series of graphs and hypergraphs outperforms on certain anomalies. It is further demonstrated that a fusion statistic can provide superior inference compared to individual features alone.
520 $a The major contributions of this work are the introduction of a new graph feature on detecting anomaly in time series of graphs and hypergraphs, and the confirmation of adaptive weighting as a mechanism for combination of features.
590 $a School code: 0075.
650 4 $a Statistics. $3 145349
650 4 $a Computer Science. $3 423143
690 $a 0463
690 $a 0984
710 2 $a The George Washington University. $b Computer Science. $3 475350
773 0 $t Dissertation Abstracts International $g 72-06B.
790 1 0 $a Youssef, Abdou, $e advisor
790 1 0 $a Cheng, Susan $e committee member
790 1 0 $a Choi, Hyung-Ah $e committee member
790 1 0 $a Marchette, David J. $e committee member
790 1 0 $a Priebe, Carey E. $e committee member
790 $a 0075
791 $a Ph.D.
792 $a 2011
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3445387

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