Community trend outlier detection using soft temporal pattern mining

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 7524 LNAI, Issue PART 2, 2012, Pages 692-708

  Gupta, Manish ^a   Gao, Jing ^b   Sun, Yizhou ^a   Han, Jiawei ^a  

^a UIUC   (United States)

^b UNIVERSITY AT BUFFALO   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ACROSS TIME; DATA SETS; EFFECTIVE MEASURES; EFFECTIVE TOOL; EVOLUTION BEHAVIOR; NEWS FEEDS; OUTLIER DETECTION; REAL DATA SETS; ROAD TRAFFIC; SOFT PATTERN; TEMPORAL BEHAVIOR; TEMPORAL PATTERN MININGS; TWO-STEP PROCEDURE;

LEARNING SYSTEMS;

STATISTICS;

EID: 84866852738     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-33486-3_44     Document Type: Conference Paper

References (23)

1. Aggarwal, C.C., Yu, P.S.: Outlier Detection for High Dimensional Data. SIGMOD Records 30, 37-46 (2001)
2. Aggarwal, C.C.: On Abnormality Detection in Spuriously Populated Data Streams. In: SDM, pp. 80-91 (2005)
3. Aggarwal, C.C., Yu, P.S.: Outlier Detection with Uncertain Data. In: SDM, pp. 483-493 (2008)
4. Aggarwal, C.C., Zhao, Y., Yu, P.S.: Outlier Detection in Graph Streams. In: ICDE, pp. 399-409 (2011)
5. Agrawal, R., Srikant, R.: Fast Algorithms for Mining Association Rules in Large Databases. In: VLDB, pp. 487-499 (1994)
6. Alon, J., Sclaroff, S., Kollios, G., Pavlovic, V.: Discovering Clusters in Motion Time-Series Data. In: CVPR, pp. 375-381 (2003)
7. Angiulli, F., Fassetti, F.: Detecting Distance-based Outliers in Streams of Data. In: CIKM, pp. 811-820 (2007)
8. Basharat, A., Gritai, A., Shah, M.: Learning Object Motion Patterns for Anomaly Detection and Improved Object Detection. In: CVPR, pp. 1-8 (2008)
9. Bernecker, T., Kriegel, H.P., Renz, M., Verhein, F., Zuefle, A.: Probabilistic Frequent Itemset Mining in Uncertain Databases. In: KDD, pp. 119-128 (2009)
10. Chandola, V., Banerjee, A., Kumar, V.: Anomaly Detection: A Survey. ACM Computing Surveys 41(3) (2009)
11. Fox, A.J.: Outliers in Time Series. Journal of the Royal Statistical Society. Series B (Methodological) 34(3), 350-363 (1972)
12. Gao, J., Liang, F., Fan, W., Sun, Y., Han, J.: Graph-based Consensus Maximization among Multiple Supervised and Unsupervised Models. In: NIPS, pp. 585-593 (2009)
13. Gao, J., Liang, F., Fan, W., Wang, C., Sun, Y., Han, J.: On Community Outliers and their Efficient Detection in Information Networks. In: KDD, pp. 813-822 (2010)
14. Ge, Y., Xiong, H., Hua Zhou, Z., Ozdemir, H., Yu, J., Lee, K.C.: Top-Eye: Top-K Evolving Trajectory Outlier Detection. In: CIKM, pp. 1733-1736 (2010)
15. Ghoting, A., Otey, M.E., Parthasarathy, S.: LOADED: Link-Based Outlier and Anomaly Detection in Evolving Data Sets. In: ICDM, pp. 387-390 (2004)
16. Gudmundsson, J., Kreveld, M., Speckmann, B.: Efficient Detection of Motion Patterns in Spatio-temporal Data Sets. In: GIS, pp. 250-257 (2004)
17. Gupta, M., Gao, J., Sun, Y., Han, J.: Integrating Community Matching and Outlier Detection for Mining Evolutionary Community Outliers. In: KDD (to appear, 2012)
18. Hodge, V.J., Austin, J.: A Survey of Outlier Detection Methodologies. AI Review 22(2), 85-126 (2004)
19. Lee, J.-G., Han, J., Li, X.: Trajectory Outlier Detection: A Partition-and-Detect Framework. In: ICDE, pp. 140-149 (2008)
20. Li, Z., Ding, B., Han, J., Kays, R.: Swarm: Mining Relaxed Temporal Moving Object Clusters. In: VLDB, pp. 723-734 (2010)
21. Muzammal, M., Raman, R.: Mining Sequential Patterns from Probabilistic Databases. In: Huang, J.Z., Cao, L., Srivastava, J. (eds.) PAKDD 2011, Part II. LNCS, vol. 6635, pp. 210-221. Springer, Heidelberg (2011)
22. Pelleg, D., Moore, A.W.: X-means: Extending K-means with Efficient Estimation of the Number of Clusters. In: ICML, pp. 727-734 (2000)
23. Sun, Y., Tang, J., Han, J., Gupta, M., Zhao, B.: Community Evolution Detection in Dynamic Heterogeneous Information Networks. In: KDD-MLG, pp. 137-146 (2010)