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    Applications of Data Mining to Electronic Commerce (Hardback) Edited by Ronny Kohavi, Edited by Foster Provost

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    DescriptionApplications of Data Mining to Electronic Commerce brings together in one place important contributions and up-to-date research results in this fast moving area. Applications of Data Mining to Electronic Commerce serves as an excellent reference, providing insight into some of the most challenging research issues in the field.

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    Applications of Data Mining to Electronic Commerce
    Authors and contributors
    Edited by Ronny Kohavi, Edited by Foster Provost
    Physical properties
    Format: Hardback
    Number of pages: 153
    Width: 155 mm
    Height: 239 mm
    Thickness: 15 mm
    Weight: 363 g
    ISBN 13: 9780792373032
    ISBN 10: 0792373030

    BIC E4L: COM
    Nielsen BookScan Product Class 3: S10.2
    B&T Book Type: NF
    B&T Modifier: Region of Publication: 01
    B&T Modifier: Academic Level: 01
    BIC subject category V2: KNP
    LC subject heading: ,
    B&T Modifier: Subject Development: 10
    LC subject heading:
    Ingram Subject Code: BE
    BISAC V2.8: COM031000
    Libri: I-BE
    B&T General Subject: 229
    LC classification: QA76.9.D35
    BIC subject category V2: UN
    B&T Merchandise Category: COM
    BISAC V2.8: COM014000
    B&T Approval Code: A93661400
    Warengruppen-Systematik des deutschen Buchhandels: 17870
    BISAC V2.8: COM060000
    DC22: 658.8/4
    LC subject heading:
    BISAC V2.8: BUS043040
    DC22: 658.84
    BISAC V2.8: COM064000
    LC subject heading:
    BISAC V2.8: COM021000, BUS073000
    B&T Approval Code: A93662400
    LC subject heading:
    LC classification: QA76.9.D343 A36 2001
    DC21: 380.102855741
    LC classification: HF5415.1265, QA75.5-76.95
    LC subject heading:
    Thema V1.0: UDB, KNP, UYZM, GPF, KJE, UN
    2, New edition
    Edition statement
    Illustrations note
    Kluwer Academic Publishers
    Imprint name
    Kluwer Academic Publishers
    Publication date
    28 February 2001
    Publication City/Country
    Boston, MA
    Table of contents
    Applications of Data Mining to Electronic Commerce; R. Kohavi, F. Provost. Personalization of Supermarket Product Recommendations; R.D. Lawrence, et al. Expert-Driven Validation of Rule-Based User Models in Personalization Applications; G. Adomavicius, A. Tuzhilin. Visualization and Analysis of Clickstream Data of Online Stores for Understanding Web Merchandising; J. Lee, et al. Data Mining for Measuring and Improving the Success of Web Sites; M. Spiliopoulou, C. Pohle. E-Commerce Recommendation Applications; J.B. Schafer, et al.