article

Detecting localized homogeneous anomalies over spatio-temporal data

Authors:

Prasad Deshpande,

Ranjana RajendranAuthors Info & Claims

Data Mining and Knowledge Discovery, Volume 28, Issue 5-6

Pages 1480 - 1502

https://doi.org/10.1007/s10618-014-0366-x

Published: 01 September 2014 Publication History

Abstract

The last decade has witnessed an unprecedented growth in availability of data having spatio-temporal characteristics. Given the scale and richness of such data, finding spatio-temporal patterns that demonstrate significantly different behavior from their neighbors could be of interest for various application scenarios such as--weather modeling, analyzing spread of disease outbreaks, monitoring traffic congestions, and so on. In this paper, we propose an automated approach of exploring and discovering such anomalous patterns irrespective of the underlying domain from which the data is recovered. Our approach differs significantly from traditional methods of spatial outlier detection, and employs two phases--(i) discovering homogeneous regions, and (ii) evaluating these regions as anomalies based on their statistical difference from a generalized neighborhood. We evaluate the quality of our approach and distinguish it from existing techniques via an extensive experimental evaluation.

References

[1]

Achanta R, Hemami S, Estrada F, Susstrunk S (2009) Frequency-tuned salient region detection. 2012 IEEE Conf Comput Vis Pattern Recognit 0:1597-1604.

[2]

Arbelaez P, Maire M, Fowlkes C, Malik J (2011) Contour detection and hierarchical image segmentation. IEEE Trans Pattern Analy Mach Intell 33(5):898-916.

Digital Library

[3]

Birant D, Kut A (2007) St-dbscan: An algorithm for clustering spatial-temporal data. Data Knowl Eng 60(1):208-221.

Digital Library

[4]

Bonett DG (2006) Confidence interval for a coefficient of quartile variation. Comput Stat Data Anal 50(11):2953-2957.

Digital Library

[5]

Bonnet N, Cutrona J, Herbin M (2002) A no-thresholdhistogram-based image segmentation method. Pattern Recognit 35(10):2319-2322.

[6]

Ceriani L, Verme P (2012) The origins of the gini index: extracts from variabilità e mutabilità (1912) by corrado gini. J Econ Inequal 10(3):421-443.

[7]

Cheng T, Li Z (2004) A hybrid approach to detect spatial-temporal outliers. In Proceedings of the 12th International Conference on Geoinformatics Geospatial Information Research, pp. 173-178.

[8]

Deaton A (1997) The analysis of household surveys: a microeconometric approach to development policy. Johns Hopkins University Press, Baltimore.

[9]

Duczmal L (2004) A simulated annealing strategy for the detection of arbitrarily shaped spatial clusters. Comput Stat Data Anal 45(2):269-286.

[10]

El-Hamdouchi A, Willett P (1989) Comparison of hierarchie agglomerative clustering methods for document retrieval. Comput J 32(3):220-227.

Digital Library

[11]

Ester M, Kriegel H-P, Sander J, Xu X (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. In KDD, pp. 226-231.

Digital Library

[12]

Fan J, Yau DK, Elmagarmid AK, Aref WG (2001) Automatic image segmentation by integrating color-edge extraction and seeded region growing. IEEE Trans Image Process 10(10):1454-1466.

Digital Library

[13]

Felzenszwalb PF, Huttenlocher DP (2004) Efficient graph-based image segmentation. Int J Comput Vis 59(2):167-181.

Digital Library

[14]

Friedman JH, Fisher NI (1999) Bump hunting in high-dimensional data. Stat Comput 9(2):123-143.

[15]

Gajdos T, Weymark JA (2005) Multidimensional generalized Gini indices. Econ Theory 26(3):471-496.

[16]

Grady L, Schwartz EL (2006) Isoperimetric graph partitioning for image segmentation. IEEE Trans Pattern Anal Mach Intell 28(3):469-475.

Digital Library

[17]

Huelsenbeck JP, Crandall KA (1997) Phylogeny estimation and hypothesis testing using maximum likelihood. Ann Rev Ecol Syst 28:437-466.

[18]

Jain AK, Murty MN, Flynn PJ (1999) Data clustering: a review. ACM Comput Surv 31(3):264-323.

Digital Library

[19]

Joseph FL (1971) Measuring nominal scale agreement among many raters. Psychol Bull 76(5):378-382.

[20]

Kisilevich S, Mansmann F, Nanni M, Rinzivillo S (2010) Spatio-temporal clustering: a survey. Data mining and knowledge discovery handbook. Springer, New York, pp 855-874.

[21]

Kou Y, tien Lu C (2006) Spatial weighted outlier detection. In Proceedings of SIAM Conference on Data Mining.

[22]

Kulldorff M (1997) A spatial scan statistic. Commun Stat-Theory Methods 26(6):1481-1496.

[23]

Landis JR, Koch GG (1977) The measurement of observer agreement for categorical data. Biometrics 33(11):159-174.

[24]

Lukasová A (1979) Hierarchical agglomerative clustering procedure. Pattern Recognit 11(5-6):365-381.

[25]

Mankiewicz R (2000) The story of mathematics. Princeton University Department of Art, Princeton.

[26]

Mood A, Graybill F, Boes D (1963) Introduction to the theory of statistics. Mc-graw hill book company. Inc., New York.

[27]

Neill DB, Moore AW (2004) Rapid detection of significant spatial clusters. In Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '04, pp. 256-265, New York, NY. ACM.

[28]

Neill DB, Moore AW, Cooper GF (2005) A bayesian spatial scan statistic. In NIPS.

[29]

Ohlander R, Price K, Reddy DR (1978) Picture segmentation using a recursive region splitting method. Comput Gr Image Process 8(3):313-333.

[30]

Pang LX, Chawla S, Liu W, Zheng Y (2011) On mining anomalous patterns in road traffic streams. In Advanced Data Mining and Applications, pp. 237-251. Springer.

Digital Library

[31]

Patil GP, Taillie C (2004) Upper level set scan statistic for detecting arbitrarily shaped hotspots. Environ Ecol Stat 11:183-197.

[32]

Reades J, Calabrese F, Sevtsuk A, Ratti C (2007) Cellular census: explorations in urban data collection. IEEE Pervasive Comput 6(3):30-38.

Digital Library

[33]

Revol C, Jourlin M (1997) A new minimum variance region growing algorithm for image segmentation. Pattern Recognit Lett 18(3):249-258.

Digital Library

[34]

Schubert E, Zimek A, Kriegel H-P (2014) Local outlier detection reconsidered: a generalized view on locality with applications to spatial, video, and network outlier detection. Data Min Know Discov 28(1):190-237.

Digital Library

[35]

Shekar S, Lu C-T, Zhang P (2002) Detecting graph-based spatial outliers. Intell Data Anal 6(5):451-468.

[36]

Shi J, Malik J (2000) Normalized cuts and image segmentation. IEEE Trans Pattern Anal Mach Intell 22(8):888-905.

Digital Library

[37]

Sindhu B, Suresh I, Unnikrishnan A, Bhatkar N, Neetu S, Michael G (2007) Improved bathymetric datasets for the shallow water regions in the indian ocean. J Earth Syst Sci 116(3):261-274.

[38]

Stolorz PE, Nakamura H, Mesrobian E, Muntz RR, Shek EC, Santos JR, Yi J, Ng KW, Chien S-Y, Mechoso CR, Farrara JD (1995) Fast spatio-temporal data mining of large geophysical datasets. In KDD, pp. 300-305.

[39]

Tango T, Takahashi K (2005) A flexibly shaped spatial scan statistic for detecting clusters. Int J Health Geogr 4:11.

Cited By

Zhang SWan J(2019)Anomaly Region Detection Based on DMSTInternational Journal of Data Warehousing and Mining10.4018/IJDWM.201901010315:1(39-57)Online publication date: 1-Jan-2019
https://dl.acm.org/doi/10.4018/IJDWM.2019010103
Lee KSuh Y(2019)A pattern-based outlier region detection method for two-dimensional arraysThe Journal of Supercomputing10.1007/s11227-018-2418-275:1(170-188)Online publication date: 1-Jan-2019
https://dl.acm.org/doi/10.1007/s11227-018-2418-2
Shah SGoyal M(2019)Anomaly Detection in Social Media Using Recurrent Neural NetworkComputational Science – ICCS 201910.1007/978-3-030-22747-0_6(74-83)Online publication date: 12-Jun-2019
https://dl.acm.org/doi/10.1007/978-3-030-22747-0_6
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cover image Data Mining and Knowledge Discovery

Data Mining and Knowledge Discovery Volume 28, Issue 5-6

September 2014

482 pages

ISSN:1384-5810

Issue’s Table of Contents

Copyright © Copyright © 2014 The Author(s).

Publisher

Kluwer Academic Publishers

United States

Publication History

Published: 01 September 2014

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Cited By

Zhang SWan J(2019)Anomaly Region Detection Based on DMSTInternational Journal of Data Warehousing and Mining10.4018/IJDWM.201901010315:1(39-57)Online publication date: 1-Jan-2019
https://dl.acm.org/doi/10.4018/IJDWM.2019010103
Lee KSuh Y(2019)A pattern-based outlier region detection method for two-dimensional arraysThe Journal of Supercomputing10.1007/s11227-018-2418-275:1(170-188)Online publication date: 1-Jan-2019
https://dl.acm.org/doi/10.1007/s11227-018-2418-2
Shah SGoyal M(2019)Anomaly Detection in Social Media Using Recurrent Neural NetworkComputational Science – ICCS 201910.1007/978-3-030-22747-0_6(74-83)Online publication date: 12-Jun-2019
https://dl.acm.org/doi/10.1007/978-3-030-22747-0_6
Ramachandra BDutton BVatsavai R(2019)Anomalous cluster detection in spatiotemporal meteorological fieldsStatistical Analysis and Data Mining10.1002/sam.1139812:2(88-100)Online publication date: 20-Mar-2019
https://dl.acm.org/doi/10.1002/sam.11398
Krishnan AP. DRanu SMehta S(2018)Leveraging semantic resources in diversified query expansionWorld Wide Web10.1007/s11280-017-0468-721:4(1041-1067)Online publication date: 1-Jul-2018
https://dl.acm.org/doi/10.1007/s11280-017-0468-7
Duffy CP. DLong CSatish Kumar MThorat ADubey A(2018)Fast Identification of Interesting Spatial Regions with Applications in Human Development ResearchDatabase and Expert Systems Applications10.1007/978-3-319-98812-2_37(408-416)Online publication date: 3-Sep-2018
https://dl.acm.org/doi/10.1007/978-3-319-98812-2_37

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