메뉴 건너뛰기




Volumn 6, Issue 3, 2015, Pages

Significant correlation pattern mining in smart homes

Author keywords

Correlation pattern; Sequential pattern; Smart home; Time interval based data; Usage representation

Indexed keywords

AUTOMATION; DATA MINING; INTELLIGENT BUILDINGS;

EID: 84928552467     PISSN: 21576904     EISSN: 21576912     Source Type: Journal    
DOI: 10.1145/2700484     Document Type: Article
Times cited : (17)

References (42)
  • 2
    • 0020849266 scopus 로고
    • Maintaining knowledge about temporal intervals
    • J. Allen. 1983. Maintaining knowledge about temporal intervals. Communications of the ACM 26, 11, 832-843.
    • (1983) Communications of the ACM , vol.26 , Issue.11 , pp. 832-843
    • Allen, J.1
  • 8
    • 0033343493 scopus 로고    scopus 로고
    • Using a pattern recognition approach to disaggregate the total electricity consumption in a house into the major end-uses
    • L. Farinaccio and R. Zmeureanu. 1999. Using a pattern recognition approach to disaggregate the total electricity consumption in a house into the major end-uses. Energy and Buildings 30, 3, 245-259.
    • (1999) Energy and Buildings , vol.30 , Issue.3 , pp. 245-259
    • Farinaccio, L.1    Zmeureanu, R.2
  • 9
    • 33749319347 scopus 로고    scopus 로고
    • Interestingness measures for data mining: A survey
    • L. Geng and H. Hamilton. 2006. Interestingness measures for data mining: A survey. ACM Computing Surveys 38, 3, 9.
    • (2006) ACM Computing Surveys , vol.38 , Issue.3 , pp. 9
    • Geng, L.1    Hamilton, H.2
  • 13
    • 33750381070 scopus 로고    scopus 로고
    • Finding informative rules in interval sequences
    • F. Hopper. 2002. Finding informative rules in interval sequences. Intelligent Data Analysis 6, 3, 237-255.
    • (2002) Intelligent Data Analysis , vol.6 , Issue.3 , pp. 237-255
    • Hopper, F.1
  • 27
    • 34548085733 scopus 로고    scopus 로고
    • Efficient mining of understandable patterns frommultivariate interval time series
    • F. Morchen and A. Ultsch. 2007. Efficient mining of understandable patterns frommultivariate interval time series. Data Mining and Knowledge Discovery 15, 2, 181-215.
    • (2007) Data Mining and Knowledge Discovery , vol.15 , Issue.2 , pp. 181-215
    • Morchen, F.1    Ultsch, A.2
  • 29
    • 33244463289 scopus 로고    scopus 로고
    • On characterization and discovery of minimal unexpected patterns in rule discovery
    • B. Padmanabhan and A. Tuzhilin. 2006. On characterization and discovery of minimal unexpected patterns in rule discovery. IEEE Transactions on Knowledge and Data Engineering 18, 2, 202-216.
    • (2006) IEEE Transactions on Knowledge and Data Engineering , vol.18 , Issue.2 , pp. 202-216
    • Padmanabhan, B.1    Tuzhilin, A.2
  • 33
    • 0036085718 scopus 로고    scopus 로고
    • A neuron nets based procedure for identifying domestic applicances pattern-of-use from energy recordings at meter panel
    • A. Prudenzi. 2002. A neuron nets based procedure for identifying domestic applicances pattern-of-use from energy recordings at meter panel. In Proceedings of the IEEE Power Engineering SocietyWinterMeeting. 491-496.
    • (2002) Proceedings of the IEEE Power Engineering Society Winter Meeting , pp. 491-496
    • Prudenzi, A.1
  • 34
    • 84880108764 scopus 로고    scopus 로고
    • Efficient method to discover interval-based sequential patterns
    • R. Sadasivam and K. Duraiswamy. 2013. Efficient method to discover interval-based sequential patterns. Journal of Computer Science 9, 2, 225-234.
    • (2013) Journal of Computer Science , vol.9 , Issue.2 , pp. 225-234
    • Sadasivam, R.1    Duraiswamy, K.2
  • 37
    • 34250202878 scopus 로고    scopus 로고
    • ARMADA-an algorithm for discovering richer relative temporal association rules from interval-based data
    • E. Winarko and J. Roddick. 2007. ARMADA-an algorithm for discovering richer relative temporal association rules from interval-based data. Data and Knowledge Engineering 63, 1, 76-90.
    • (2007) Data and Knowledge Engineering , vol.63 , Issue.1 , pp. 76-90
    • Winarko, E.1    Roddick, J.2
  • 39
    • 34247574865 scopus 로고    scopus 로고
    • Mining nonambiguous temporal patterns for interval-based events
    • S. Wu and Y. Chen. 2007. Mining nonambiguous temporal patterns for interval-based events. IEEE Transactions on Knowledge and Data Engineering 19, 6, 742-758.
    • (2007) IEEE Transactions on Knowledge and Data Engineering , vol.19 , Issue.6 , pp. 742-758
    • Wu, S.1    Chen, Y.2
  • 40
    • 71749112568 scopus 로고    scopus 로고
    • Discovering hybrid temporal patterns from sequences consisting of point- and interval-based events
    • S. Wu and Y. Chen. 2009. Discovering hybrid temporal patterns from sequences consisting of point- and interval-based events. Data and Knowledge Engineering 68, 11, 1309-1330.
    • (2009) Data and Knowledge Engineering , vol.68 , Issue.11 , pp. 1309-1330
    • Wu, S.1    Chen, Y.2
  • 41
    • 3543082150 scopus 로고    scopus 로고
    • InfoMiner: Mining surprising periodic patterns
    • J. Yang, W. Wang, and P. Yu. 2004. InfoMiner: Mining surprising periodic patterns. Data Mining and Knowledge Discovery 9, 2, 189-216.
    • (2004) Data Mining and Knowledge Discovery , vol.9 , Issue.2 , pp. 189-216
    • Yang, J.1    Wang, W.2    Yu, P.3
  • 42
    • 36148947566 scopus 로고    scopus 로고
    • Discovering during-temporal patterns (DTPs) in large temporal databases
    • L. Zhang, G. Chen, T. Brijs, and X. Zhang. 2008. Discovering during-temporal patterns (DTPs) in large temporal databases. Expert Systems with Applications 34, 1178-1189.
    • (2008) Expert Systems with Applications , vol.34 , pp. 1178-1189
    • Zhang, L.1    Chen, G.2    Brijs, T.3    Zhang, X.4


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.