Anomaly Detection in Transactional Sequential Data


	Information Technology Journal
	Year: 2012 \| Volume: 11 \| Issue: 7 \| Page No.: 782-787 DOI: 10.3923/itj.2012.782.787

Anomaly Detection in Transactional Sequential Data

Jingwei Zhang, Yuming Lin, Huibing Zhang and Qing Yang

Abstract: A transactional system often has a steady distribution based on their decisions or responses on various transactions. If the system is changed, the distribution also often changes. It is a valuable work to detect anomaly caused by system changes based on different distributions. In this study, we modeled those decision or response signals into a series of time-related distributions and then proposed a method combining distance metrics and anomaly detection to discover whether changes have happened in some systems. Distance metrics on different distributions can decide whether changes have happened and anomaly detection can find what happened further. Extensive experiments show that our method has a good performance and can locate the anomaly accurately.

PDF Fulltext XML References Citation Report Citation

RELATED ARTICLES:

•	Integrated Approach of Reduct and Clustering for Mining Patterns from Clusters

•	Distance Based Outlier for Data Streams Using Grid Structure

•	TPMS Data Analysis for Enhancing Intelligent Vehicle Performance

•	Novel Data Fusion Approach for Drowsiness Detection

How to cite this article:

Jingwei Zhang, Yuming Lin, Huibing Zhang and Qing Yang, 2012. Anomaly Detection in Transactional Sequential Data. Information Technology Journal, 11: 782-787.

DOI: 10.3923/itj.2012.782.787

URL: https://scialert.net/abstract/?doi=itj.2012.782.787

COMMENT ON THIS PAPER

Navigation


		Online First
		Current Issue
		Previous Issues
		Editorial Board
		Submit a Manuscript
		Guide to Authors
		Subscribe to E-alerts

Indexed In


		ASCI-Database
		Asian Digital Library
		Google Scholar

Follow @scialert

Science Alert