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Volumn 24, Issue 1, 2012, Pages 159-194

Mining spatial colocation patterns: A different framework

Author keywords

Colocation mining; Spatial association patterns; Spatial data mining

Indexed keywords

ALGORITHMIC DESIGN; ALTERNATIVE ALGORITHMS; APPROPRIATE DISTANCES; CO-LOCATED; CO-LOCATION PATTERNS; COLOCATIONS; DATA SETS; EVENT SETS; GEOGRAPHIC PROXIMITY; PATTERN SIZE; PRIOR KNOWLEDGE; SPATIAL ASSOCIATION PATTERNS; SPATIAL CO-LOCATION PATTERNS; SPATIAL DATA; SPATIAL DATA MINING; SPATIAL DATASETS; SPATIAL EVENTS; SPATIAL NEIGHBORHOODS; SYNTHETIC DATASETS; TWO PARAMETER; USER INPUT;

EID: 84856617586     PISSN: 13845810     EISSN: None     Source Type: Journal    
DOI: 10.1007/s10618-011-0223-0     Document Type: Article
Times cited : (53)

References (58)
  • 2
    • 26944494689 scopus 로고    scopus 로고
    • Discovery of spatial association rules in geo-referenced census data: A relational mining approach
    • Appice A, Ceci M, Lanza A (2003) Discovery of spatial association rules in geo-referenced census data: a relational mining approach. In: Proceedings of the intelligent data analysis
    • (2003) Proceedings of the Intelligent Data Analysis
    • Appice, A.1    Ceci, M.2    Lanza, A.3
  • 8
    • 35048836319 scopus 로고    scopus 로고
    • Spatial associative classification at different levels of granularity: A probabilistic approach
    • Springer, Berlin
    • Ceci M, Appice A, Malerba D (2004) Spatial associative classification at different levels of granularity: a probabilistic approach. Lecture notes in computer science, vol 3202. Springer, Berlin
    • (2004) Lecture Notes in Computer Science , vol.3202
    • Ceci, M.1    Appice, A.2    Malerba, D.3
  • 9
    • 70350686982 scopus 로고    scopus 로고
    • Spatial data mining implementation-alternative and performances
    • Chelghoum N, Zeitouni K (2004) Spatial data mining implementation- alternative and performances. In: GeoInfo
    • (2004) GeoInfo
    • Chelghoum, N.1    Zeitouni, K.2
  • 10
    • 4544293405 scopus 로고    scopus 로고
    • Mining frequent itemsets without support threshold: With and without item constraints
    • 10.1109/TKDE.2004.44
    • Y Cheung AW Fu 2004 Mining frequent itemsets without support threshold: with and without item constraints IEEE Trans Knowl Data Eng 16 9 1052 1069 10.1109/TKDE.2004.44
    • (2004) IEEE Trans Knowl Data Eng , vol.16 , Issue.9 , pp. 1052-1069
    • Cheung, Y.1    Fu, A.W.2
  • 12
    • 48249105502 scopus 로고
    • Nearest neighbor pattern classification
    • T Cover 1995 Nearest neighbor pattern classification Knowl Based Syst 6 8 373 389
    • (1995) Knowl Based Syst , vol.6 , Issue.8 , pp. 373-389
    • Cover, T.1
  • 14
    • 84856608397 scopus 로고    scopus 로고
    • Crimestat
    • Crimestat. http://www.icpsr.umich.edu/CrimaStat/
  • 19
    • 84856622743 scopus 로고    scopus 로고
    • ESRI. Arcgis
    • ESRI. Arcgis. http://www.esri.com/software/arcgis/index.html
  • 30
    • 51449110450 scopus 로고    scopus 로고
    • Geographic data mining and knowledge discovery
    • Wilson JP, Fotheringham AS (eds) Blackwell, Oxford
    • Miller HJ (2006) Geographic data mining and knowledge discovery. In: Wilson JP, Fotheringham AS (eds) Handbook of geographic information science. Blackwell, Oxford
    • (2006) Handbook of Geographic Information Science
    • Miller, H.J.1
  • 33
    • 0030800230 scopus 로고    scopus 로고
    • Spatial relations, minimum bounding rectangles, and spatial data structures
    • 10.1080/136588197242428
    • D Papadias Y Theodoridis 1997 Spatial relations, minimum bounding rectangles, and spatial data structures Int J Geogr Inf Sci 11 111 138 10.1080/136588197242428
    • (1997) Int J Geogr Inf Sci , vol.11 , pp. 111-138
    • Papadias, D.1    Theodoridis, Y.2
  • 34
    • 84856608393 scopus 로고    scopus 로고
    • R for Statistical Computing
    • R for Statistical Computing. http://www.r-project.org/index.html
  • 35
    • 0000313850 scopus 로고
    • The second-order analysis of stationary point process
    • 10.2307/3212829 0364.60087 402918
    • B Ripley 1976 The second-order analysis of stationary point process J Appl Probab 13 255 266 10.2307/3212829 0364.60087 402918
    • (1976) J Appl Probab , vol.13 , pp. 255-266
    • Ripley, B.1
  • 36
    • 0002140903 scopus 로고    scopus 로고
    • A bibliography of temporal, spatial and spatio-temporal data mining research
    • 10.1145/846170.846173
    • J Roddick M Spiliopoulou 1999 A bibliography of temporal, spatial and spatio-temporal data mining research Proc SIGKDD Explor 1 1 34 38 10.1145/846170.846173
    • (1999) Proc SIGKDD Explor , vol.1 , Issue.1 , pp. 34-38
    • Roddick, J.1    Spiliopoulou, M.2
  • 37
    • 17244376446 scopus 로고    scopus 로고
    • Geo-spatial data mining in the analysis of a demographic database
    • MY Santos LA Amaral 2005 Geo-spatial data mining in the analysis of a demographic database Soft Comput Fusion Found Methodol Appl 9 5 374 384
    • (2005) Soft Comput Fusion Found Methodol Appl , vol.9 , Issue.5 , pp. 374-384
    • Santos, M.Y.1    Amaral, L.A.2
  • 38
    • 0942299808 scopus 로고    scopus 로고
    • GIS, the US census and neighborhood scale analysis
    • Schlossberg M (2003) GIS, the US census and neighborhood scale analysis. Plan Pract Res 18(2-3):213-218
    • (2003) Plan Pract Res , vol.18 , Issue.2-3 , pp. 213-218
    • Schlossberg, M.1
  • 42
    • 84856609961 scopus 로고    scopus 로고
    • Spatstat
    • Spatstat. http://www.spatstat.org/spatstat/
  • 43
    • 0000565591 scopus 로고
    • A computer movie simulating urban growth in the detroit region
    • 10.2307/143141
    • W Tobler 1970 A computer movie simulating urban growth in the detroit region Econ Geogr 46 234 240 10.2307/143141
    • (1970) Econ Geogr , vol.46 , pp. 234-240
    • Tobler, W.1
  • 45
    • 84856607771 scopus 로고    scopus 로고
    • U. G. Survey
    • U. G. Survey. http://www.usgs.gov/
  • 46
    • 84856622741 scopus 로고    scopus 로고
    • .S.E.P. Agency
    • U.S.E.P. Agency. Environmental Interest Type. http://www.epa.gov/enviro/ html/frs-demo/interest-types.pdf
    • Environmental Interest Type
  • 47
    • 84856608394 scopus 로고    scopus 로고
    • U.S. EPA (Environmental Protection Agency) FRS (Facility Registry System) facilities
    • U.S. EPA (Environmental Protection Agency) FRS (Facility Registry System) facilities. http://www.epa.gov/enviro/html/frs-demo/geospatial-data/geo-data- state-single.html
  • 48
    • 59549084797 scopus 로고    scopus 로고
    • Knfcom-t: A k-nearest features-based co-location pattern mining algorithm for large spatial data sets by using t-trees
    • 10.1504/IJBIDM.2008.022735 2656957
    • Y Wan J Zhou 2008 Knfcom-t: a k-nearest features-based co-location pattern mining algorithm for large spatial data sets by using t-trees Int J Bus Intell Data Min 3 4 375 389 10.1504/IJBIDM.2008.022735 2656957
    • (2008) Int J Bus Intell Data Min , vol.3 , Issue.4 , pp. 375-389
    • Wan, Y.1    Zhou, J.2
  • 49
    • 19944376126 scopus 로고    scopus 로고
    • TFP: An efficient algorithm for mining top-k frequent closed itemsets
    • 10.1109/TKDE.2005.81
    • J Wang J Han Y Lu P Tzvetkov 2005 TFP: an efficient algorithm for mining top-k frequent closed itemsets IEEE Trans Knowl Data Eng 17 5 652 664 10.1109/TKDE.2005.81
    • (2005) IEEE Trans Knowl Data Eng , vol.17 , Issue.5 , pp. 652-664
    • Wang, J.1    Han, J.2    Lu, Y.3    Tzvetkov, P.4
  • 55
    • 33748365438 scopus 로고    scopus 로고
    • A join-less approach for mining spatial co-location patterns
    • 10.1109/TKDE.2006.150
    • JS Yoo S Shekhar 2006 A join-less approach for mining spatial co-location patterns IEEE Trans Knowl Data Eng 18 10 1323 1337 10.1109/TKDE.2006.150
    • (2006) IEEE Trans Knowl Data Eng , vol.18 , Issue.10 , pp. 1323-1337
    • Yoo, J.S.1    Shekhar, S.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.