Daniel T. Larose

DiscoveringKnowledgeAffinity analysis is the study of attributes or characteristics that “go together.” Methods for affinity analysis, also known as market basket analysis, seek to uncover associations among these attributes; that is, it seeks to uncover rules for quantifying the relationship between two or more attributes. Association rules take the form “If antecedent, then consequent,” along with a measure of the support and confidence as- sociated with the rule. For example, a particular supermarket may find that of the 1000 customers shopping on a Thursday night, 200 bought diapers, and of the 200 who bought diapers, 50 bought beer. Thus, the association rule would be: “If buy diapers, then buy beer,” with a support of 50/1000 = 5% and a confidence of 50/200 = 25%.

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  1. shinichi Post author

    Discovering Knowledge in Data: An Introduction to Data Mining

    by Daniel T. Larose

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    Learn Data Mining by doing data mining

    Data mining can be revolutionary—but only when it’s done right. The powerful black box data mining software now available can produce disastrously misleading results unless applied by a skilled and knowledgeable analyst. Discovering Knowledge in Data: An Introduction to Data Mining provides both the practical experience and the theoretical insight needed to reveal valuable information hidden in large data sets.

    Employing a “white box” methodology and with real-world case studies, this step-by-step guide walks readers through the various algorithms and statistical structures that underlie the software and presents examples of their operation on actual large data sets. Principal topics include:

    ・ Data preprocessing and classification
    ・ Exploratory analysis
    ・ Decision trees
    ・ Neural and Kohonen networks
    ・ Hierarchical and k-means clustering
    ・ Association rules
    ・ Model evaluation techniques

    Complete with scores of screenshots and diagrams to encourage graphical learning, Discovering Knowledge in Data: An Introduction to Data Mining gives students in Business, Computer Science, and Statistics as well as professionals in the field the power to turn any data warehouse into actionable knowledge.

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