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Volumn 5711 LNAI, Issue PART 1, 2009, Pages 209-218

A non-sequential representation of sequential data for churn prediction

Author keywords

[No Author keywords available]

Indexed keywords

DATA SETS; EVENT SEQUENCE; K NEAREST NEIGHBOR ALGORITHM; SEQUENTIAL DATA; TEMPORAL DATA;

EID: 70849126652     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-04595-0_26     Document Type: Conference Paper
Times cited : (5)

References (13)
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  • 3
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    • Fast and versatile algorithm for nearest neighbor search based on a lower bound tree
    • Chen, Y.-S., Hung, Y.-P., Yen, T.-F., Fuh, C.-S.: Fast and versatile algorithm for nearest neighbor search based on a lower bound tree. Pattern Recogn. 40(2), 360-375 (2007)
    • (2007) Pattern Recogn , vol.40 , Issue.2 , pp. 360-375
    • Chen, Y.-S.1    Hung, Y.-P.2    Yen, T.-F.3    Fuh, C.-S.4
  • 4
    • 84951778046 scopus 로고    scopus 로고
    • Machine learning for sequential data: A review
    • Caelli, T.M, Amin, A, Duin, R.P.W, Kamel, M.S, de Ridder, D, eds, SPR 2002 and SSPR 2002, Springer, Heidelberg
    • Dietterich, T.G.: Machine learning for sequential data: A review. In: Caelli, T.M., Amin, A., Duin, R.P.W., Kamel, M.S., de Ridder, D. (eds.) SPR 2002 and SSPR 2002. LNCS, vol. 2396, pp. 15-30. Springer, Heidelberg (2002)
    • (2002) LNCS , vol.2396 , pp. 15-30
    • Dietterich, T.G.1
  • 8
    • 33744526493 scopus 로고    scopus 로고
    • Bagging and boosting classification trees to predict churn
    • Lemmens, A., Croux, C.: Bagging and boosting classification trees to predict churn. Journal of Marketing Research XLIII, 276-286 (2006)
    • (2006) Journal of Marketing Research , vol.43 , pp. 276-286
    • Lemmens, A.1    Croux, C.2
  • 10
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    • Quinlan, J.R.: C4.5: programs for machine learning. Morgan Kaufmann Publishers Inc., San Francisco (1993)
    • Quinlan, J.R.: C4.5: programs for machine learning. Morgan Kaufmann Publishers Inc., San Francisco (1993)
  • 11
    • 33750548838 scopus 로고    scopus 로고
    • K nearest sequence method and its application to churn prediction
    • Corchado, E, Yin, H, Botti, V, Fyfe, C, eds, IDEAL 2006, Springer, Heidelberg
    • Ruta, D., Nauck, D., Azvine, B.: K nearest sequence method and its application to churn prediction. In: Corchado, E., Yin, H., Botti, V., Fyfe, C. (eds.) IDEAL 2006. LNCS, vol. 4224, pp. 207-215. Springer, Heidelberg (2006)
    • (2006) LNCS , vol.4224 , pp. 207-215
    • Ruta, D.1    Nauck, D.2    Azvine, B.3
  • 12
    • 0036696364 scopus 로고    scopus 로고
    • Turning telecommunications call details to churn prediction: A data mining approach
    • Wei, C.-P., Chiu, I.-T.: Turning telecommunications call details to churn prediction: a data mining approach. Expert Systems with Applications 23, 103-112 (2002)
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.