Spatial neighborhood based anomaly detection in sensor datasets

• ARC (2002) ARC IMS 4.0, ArcView 8.3. http://www.esri.com/

• Aurenhammer F (1991) Voronoi diagrams—a survey of a fundamental geometric data structure. ACM Comput Surv 23(3): 345–405

  Article  Google Scholar 

• Birant D, Kut A (2006) Spatio-temporal outlier detection in large databases. J Comput Inf Technol 14(4): 291–297

  Google Scholar 

• Chatfield C (1983) Statistics for technology, a course in applied statistics. Science Paperbacks. Chapman & Hall/CRC, Boca Raton, FL

  Google Scholar 

• Dasgupta D, Forrest S (1999) Novelty detection in time series data using ideas from immunology. In: International conference on intelligent systems

• Ester M, Kriegel HP, Sander J, Xu X (1996) A density-based algorithm for discovering clusters in large spatial databases. In: KDD, AAAI Press, USA, pp 44–49

• Ester M, Kriegel H, Sander J (1997) Spatial data mining: a database approach. In: 5th International symposium on advances in spatial databases, Springer, London, pp 47–66

• Ester M, Frommelt A, Kriegel HP, Sander J (1998) Algorithms for characterization and trend detection in spatial databases. In: 4th International conference on KDD

• Ester M, Kriegel HP, Sander J (1999) Knowledge discovery in spatial databases. In: KI ’99: proceedings of the 23rd annual German conference on artificial intelligence, Springer, London, pp 61–74

• Estivill-Castro V, Lee I (2000) Autoclust: automatic clustering via boundary extraction for mining massive point—data sets. In: 5th International conference on geocomputation

• Griffith D (1987) Spatial autocorrelation: a primer. Assoc Am Geogr

• Haining R (2003) Spatial data analysis: theory and practice. Cambridge University Press, Cambridge

  Google Scholar 

• Huang Y, Shekhar S, Xiong H (2004) Discovering colocation patterns from spatial data sets: a general approach. IEEE Trans Knowl Data Eng 16(12): 1472–1485

  Article  Google Scholar 

• Huang Y, Pei J, Xiong H (2006) Mining co-location patterns with rare events from spatial data sets. GeoInformatica 10(3): 239–260

  Article  Google Scholar 

• Kang I, Kim T, Li K (1997) A spatial data mining method by delaunay triangulation. In: 5th ACM international workshop on advances in geographic information systems, pp 35–39. doi:10.1145/267825.267836

• Kang JM, Shekhar S, Wennen C, Novak P (2008) Discovering flow anomalies: a sweet approach. In: ICDM, IEEE computer society, pp 851–856

• Kaufman L, Rousseeuw PJ (1990) Finding groups in data: an introduction to cluster analysis. John Wiley & Sons Inc., Hoboken, NJ

  Google Scholar 

• Keogh E, Lonardi S, Chiu BY (2002) Finding surprising patterns in a time series database in linear time and space. In: 8th ACM international conference on knowledge discovery and data mining, ACM Press, New York, NY, pp 550–556. doi:10.1145/775047.775128

• Knorr EM, Ng RT (1998) Algorithms for mining distance-based outliers in large datasets. In: 24th International conference on very large data bases, NY, USA, pp 392–403. http://www.vldb.org/conf/1998/p392.pdf

• Kou Y, Lu CT, Santos RFD (2007) Spatial outlier detection: a graph-based approach. In: ICTAI ’07: proceedings of the 19th IEEE international conference on tools with artificial intelligence, vol 1 (ICTAI 2007), IEEE Computer Society, Washington, DC, pp 281–288. doi:10.1109/ICTAI.2007.169

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

  Article  MATH  MathSciNet  Google Scholar 

• Kulldorff M, Athas WF, Feurer EJ, AMiller B, Key CR (1998) Evaluating cluster alarms: a space-time scan statistic and brain cancer in los alamos, new mexico. Am J Public Health 88(9): 1377–1380

  Article  Google Scholar 

• Lu C, Chen D, Kou Y (2003) Detecting spatial outliers with multiple attributes. In: 15th IEEE international conference on tools with artificial intelligence, p 122

• Lu CT, Kou Y, Zhao J, Chen L (2007) Detecting and tracking regional outliers in meteorological data. Inf Sci 177(7): 1609–1632

  Article  Google Scholar 

• McGuire MP, Janeja V, Gangopadhyay A (2008) Spatiotemporal neighborhood discovery for sensor data. In: Proceedings of the 2nd international workshop on knowledge discovery from sensor data (Sensor-KDD 2007), held in conjunction with the 14th international conference on knowledge discovery and data mining (ACM SIG-KDD 2008)

• Miller HJ, Han J (2001) Geographic data mining and knowledge discovery. Taylor & Francis Inc., New York, NY

  Book  Google Scholar 

• Moran P (1948) The interpretation of statistical maps. J R Stat Soc B 10(243): 51

  Google Scholar 

• NASQAN (2002) USGS, National stream water quality network (NASQAN), published data. http://pubs.usgs.gov/dds/wqn96cd/html/wqn/wq/region05.htm. Accessed 25 Aug 2009

• Naus J (1965) The distribution of the size of the maximum cluster of points on the line. J Am Stat Assoc 60: 532–538

  Article  MathSciNet  Google Scholar 

• Ng RT, Han J (1994) Efficient and effective clustering methods for spatial data mining. In: 20th International conference on very large data bases, Morgan Kaufmann, Los Altos, CA, pp 144–155

• Okabe A, Boots B, Sugihara K, Chiu S (2000) Spatial tessellations: concepts and applications of Voronoi diagrams. John Wiley & Sons Ltd., West Sussex, England

  MATH  Google Scholar 

• Shahabi C, Tian X, Zhao W (2000) TSA-tree: a wavelet-based approach to improve the efficiency of multi-level surprise and trend queries on time-series data. In: 12th International conference on scientific and statistical database management

• Shekhar S, Lu C, Zhang P (2001) Detecting graph-based spatial outliers: algorithms and applications (a summary of results). In: 7th ACM international conference on knowledge discovery and data mining, pp 371–376. doi:10.1145/502512.502567

• Shekhar S, Schrater P, Vatsavai R, Wu W, Chawla S (2002) Spatial contextual classification and prediction models for mining geospatial data. In: IEEE transaction on multimedia

• Shekhar S, Lu CT, Zhang P, Shekhar S, Lu CT, Zhang P (2003) A unified approach to spatial outliers detection. GeoInformatica 7: 139–166

  Article  Google Scholar 

• Shewchuk JR (1996) Triangle: engineering a 2d quality mesh generator and delaunay triangulator. In: Selected papers from the workshop on applied computational geormetry, towards geometric engineering, Springer, London, pp 203–222

• Sun P, Chawla S (2004) On local spatial outliers. In: 4th IEEE international conference on data mining, pp 209–216

• Unwin D (1982) Introductory spatial analysis. Methuen, London

  Google Scholar 

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