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Volumn 3, Issue 4, 2008, Pages 375-389

KNFCOM-T: A k-nearest features-based co-location pattern mining algorithm for large spatial data sets by using T-trees

Author keywords

Data mining; K nearest features; Spatial association rule mining; Spatial co location pattern; T tree

Indexed keywords


EID: 59549084797     PISSN: 17438187     EISSN: 17438195     Source Type: Journal    
DOI: 10.1504/IJBIDM.2008.022735     Document Type: Article
Times cited : (20)

References (18)
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  • 7
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    • Zurich, Switzerland, September, pp
    • Han, J. and Fu, Y. (1995) 'Discovery of multiple-level association rules from large databases', Proc. of the 21st International Conference on Very Large Databases, Zurich, Switzerland, September, pp.420-431.
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    • Han, J.1    Fu, Y.2
  • 8
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    • Mining frequent patterns without candidate generation: A frequent-pattern tree approach
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    • Han, J.1    Pei, J.2    Yin, Y.3    Mao, R.4
  • 9
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    • Discovering co-location patterns from spatial data sets: A general approach
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    • Huang, Y.1    Shekhar, S.2    Xiong, H.3
  • 10
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    • Mining co-location patterns with rare events from spatial data sets
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  • 11
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    • Can we apply projection based frequent pattern mining paradigm to spatial co-location mining?
    • Huang, Y., Zhang, L. and Yu, P. (2005) 'Can we apply projection based frequent pattern mining paradigm to spatial co-location mining?', Proceedings of PAKDD'05, pp.719-725.
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    • Huang, Y.1    Zhang, L.2    Yu, P.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.