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

By Shirley Coleman, Andrea Ahlemeyer-Stubbe

ISBN-10: 1118763726

ISBN-13: 9781118763728

Information mining is definitely on its technique to turning into a well-known self-discipline within the overlapping parts of IT, statistics, laptop studying, and AI. sensible information Mining for enterprise offers a simple method of info mining equipment, overlaying the common makes use of to which it's utilized. The technique is complemented through case stories to create a flexible reference publication, permitting readers to appear for particular equipment in addition to for particular functions. The e-book is formatted to permit statisticians, machine scientists, and economists to
cross-reference from a selected program or solution to sectors of curiosity.

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

Sample text

A point to note is that information available in the training analysis period may not be available at the time of applying the model. If this problem is known in advance, then variables related to that information should be o ­ mitted from the model. If a variable concerns a specific feature that we know is likely to change, then it should be replaced with a more generic variable. For e­ xample, consider the purchase of a yellow pen. If we know that at the time of ­application, yellow pens will not be sold as they will no longer be fashionable, then we would be well advised to replace ‘yellow pen’ with a more generic variable, such as ‘any pen’.

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. Challenge: This could be, for example, to find and address the right number of customers to optimise the return on investment of a marketing campaign.

These discrepancies should be noted, but usually, even poorly matched external data can be useful to supply additional relevant information. 3 Measurement Level and Types of Data There are different types of quantitative data, all of which can have good ­information content. There are many different terms used to describe the ­different data types, and the most common words are detailed in the following. The simplest level of measurement is expressed as nominal data which ­indicates which named category is applicable.

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


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