Get A Practical Guide to Data Mining for Business and Industry PDF

By Shirley Coleman, Andrea Ahlemeyer-Stubbe

ISBN-10: 1118763726

ISBN-13: 9781118763728

Facts mining is easily on its option to changing into a well-known self-discipline within the overlapping components of IT, data, desktop studying, and AI. sensible info Mining for enterprise provides a ordinary method of facts mining equipment, overlaying the common makes use of to which it truly is utilized. The technique is complemented by way of case experiences to create a flexible reference booklet, permitting readers to seem for particular tools in addition to for particular functions. The e-book is formatted to permit statisticians, machine scientists, and economists to
cross-reference from a specific program or approach to sectors of curiosity.

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Extra resources for A Practical Guide to Data Mining for Business and Industry

Example text

The full recipe structure is given in detail below. Not all of the c­ omponents are included for each recipe, and the adaptations just have the components which make them differ from the generic recipe. Industry: This refers to the area or sector of applications, for example, ­mail-order businesses, publishers, online shops, department stores or s­upermarkets (with loyalty cards) or everybody using direct communication to improve business. Areas of interest: This is specific, for example, marketing, sales and online promotions.

This can cause problems with variables that are not static but subject to change, like marital status or address. Unless it is possible to determine if they have changed, these variables should be used with caution, even if they may usually be considered static or slow changing. More stable, improved models are obtained by classifying continuous ­variables. When variables such as turnover or purchase amount are classified, it stresses more strongly the differences in the business process. For example, it has quite different implications if someone spends 0 € compared to someone spending 1 €.

For analytically focused information systems in areas such as Database Marketing (DBM) and Customer Relationship Management (CRM), external data is added frequently; there may be ­specifically purchased additional information about the customer or the customer’s address. Often, the quality of internal data is better than that from external resources, not least because you can control exactly how and when the internal data was generated. Another issue with external data is that it may not match the ­internal data exactly in time (being contemporaneous) or location.

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A Practical Guide to Data Mining for Business and Industry by Shirley Coleman, Andrea Ahlemeyer-Stubbe


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