Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Data Mining and Knowledge Discovery
Erschienen in: Data Mining and Knowledge Discovery 5-6/2014

01.09.2014

Detecting localized homogeneous anomalies over spatio-temporal data

verfasst von: Aditya Telang, P. Deepak, Salil Joshi, Prasad Deshpande, Ranjana Rajendran

Erschienen in: Data Mining and Knowledge Discovery | Ausgabe 5-6/2014

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

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.
Vorheriger Artikel Invariant time-series factorization
Nächster Artikel A peek into the black box: exploring classifiers by randomization

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Fußnoten
2
In this paper, we extensively use color-based figures to illustrate the concepts of anomalies. Hence, we request the reader to refer to the electronic version or a colored printout of the paper for better readability.
 
6
For sake of clarity, we illustrate a spatial grid; however, the formulation is extendible to the temporal dimension.
 
11
We do not include outlier detection techniques in our comparative analysis since it is not clear as to how outlier detection techniques that estimate divergent behavior at each data object level may be fairly compared with techniques that discover groups of objects that exhibit divergent behavior.
 
12
It must be noted that conducting user surveys is a difficult task. Hence, we conducted the user survey on \(Dataset_1\) only and not on on \(Dataset_2\).
 
Literatur
Achanta R, Hemami S, Estrada F, Susstrunk S (2009) Frequency-tuned salient region detection. 2012 IEEE Conf Comput Vis Pattern Recognit 0:1597–1604
Arbelaez P, Maire M, Fowlkes C, Malik J (2011) Contour detection and hierarchical image segmentation. IEEE Trans Pattern Analy Mach Intell 33(5):898–916CrossRef
Birant D, Kut A (2007) St-dbscan: An algorithm for clustering spatial-temporal data. Data Knowl Eng 60(1):208–221CrossRef
Bonnet N, Cutrona J, Herbin M (2002) A no-thresholdhistogram-based image segmentation method. Pattern Recognit 35(10):2319–2322CrossRefMATH
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):421–443CrossRef
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.
Deaton A (1997) The analysis of household surveys: a microeconometric approach to development policy. Johns Hopkins University Press, BaltimoreCrossRef
Duczmal L (2004) A simulated annealing strategy for the detection of arbitrarily shaped spatial clusters. Comput Stat Data Anal 45(2):269–286CrossRefMATHMathSciNet
El-Hamdouchi A, Willett P (1989) Comparison of hierarchie agglomerative clustering methods for document retrieval. Comput J 32(3):220–227CrossRef
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
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):1454–1466CrossRefMATH
Felzenszwalb PF, Huttenlocher DP (2004) Efficient graph-based image segmentation. Int J Comput Vis 59(2):167–181CrossRef
Friedman JH, Fisher NI (1999) Bump hunting in high-dimensional data. Stat Comput 9(2):123–143CrossRef
Grady L, Schwartz EL (2006) Isoperimetric graph partitioning for image segmentation. IEEE Trans Pattern Anal Mach Intell 28(3):469–475CrossRef
Huelsenbeck JP, Crandall KA (1997) Phylogeny estimation and hypothesis testing using maximum likelihood. Ann Rev Ecol Syst 28:437–466CrossRef
Jain AK, Murty MN, Flynn PJ (1999) Data clustering: a review. ACM Comput Surv 31(3):264–323CrossRef
Joseph FL (1971) Measuring nominal scale agreement among many raters. Psychol Bull 76(5):378–382CrossRef
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
Kou Y, tien Lu C (2006) Spatial weighted outlier detection. In Proceedings of SIAM Conference on Data Mining
Landis JR, Koch GG (1977) The measurement of observer agreement for categorical data. Biometrics 33(11):159–174
Lukasová A (1979) Hierarchical agglomerative clustering procedure. Pattern Recognit 11(5–6):365–381CrossRefMATH
Mankiewicz R (2000) The story of mathematics. Princeton University Department of Art, PrincetonMATH
Mood A, Graybill F, Boes D (1963) Introduction to the theory of statistics. Mc-graw hill book company. Inc., New York
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.
Neill DB, Moore AW, Cooper GF (2005) A bayesian spatial scan statistic. In NIPS
Ohlander R, Price K, Reddy DR (1978) Picture segmentation using a recursive region splitting method. Comput Gr Image Process 8(3):313–333CrossRef
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
Patil GP, Taillie C (2004) Upper level set scan statistic for detecting arbitrarily shaped hotspots. Environ Ecol Stat 11:183–197CrossRefMathSciNet
Reades J, Calabrese F, Sevtsuk A, Ratti C (2007) Cellular census: explorations in urban data collection. IEEE Pervasive Comput 6(3):30–38CrossRef
Revol C, Jourlin M (1997) A new minimum variance region growing algorithm for image segmentation. Pattern Recognit Lett 18(3):249–258CrossRef
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):190–237CrossRefMATHMathSciNet
Shekar S, Lu C-T, Zhang P (2002) Detecting graph-based spatial outliers. Intell Data Anal 6(5):451–468
Shi J, Malik J (2000) Normalized cuts and image segmentation. IEEE Trans Pattern Anal Mach Intell 22(8):888–905CrossRef
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):261–274CrossRef
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
Tango T, Takahashi K (2005) A flexibly shaped spatial scan statistic for detecting clusters. Int J Health Geogr 4:11CrossRef
Metadaten
Titel
Detecting localized homogeneous anomalies over spatio-temporal data
verfasst von
Aditya Telang
P. Deepak
Salil Joshi
Prasad Deshpande
Ranjana Rajendran
Publikationsdatum
01.09.2014
Verlag
Springer US
Erschienen in
Data Mining and Knowledge Discovery / Ausgabe 5-6/2014
Print ISSN: 1384-5810
Elektronische ISSN: 1573-756X
DOI
https://doi.org/10.1007/s10618-014-0366-x

Weitere Artikel der Ausgabe 5-6/2014

Data Mining and Knowledge Discovery 5-6/2014 Zur Ausgabe

OriginalPaper

Ontology of core data mining entities

OriginalPaper

Confidence bands for time series data

OriginalPaper

Approximating the crowd

OriginalPaper

Invariant time-series factorization

OriginalPaper

Overlapping community detection in labeled graphs

OriginalPaper

Learning about meetings

Premium Partner

    Bildnachweise