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

By Shirley Coleman, Andrea Ahlemeyer-Stubbe

Info mining is easily on its strategy to changing into a well-known self-discipline within the overlapping parts of IT, statistics, computing device studying, and AI. sensible information Mining for enterprise offers a undemanding method of facts mining tools, masking the common makes use of to which it really is utilized. The technique is complemented by means of case stories to create a flexible reference e-book, permitting readers to appear for particular tools in addition to for particular functions. The publication is formatted to permit statisticians, desktop scientists, and economists to
cross-reference from a selected software or approach to sectors of curiosity.

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

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5 Data Distributions Data mining is carried out on data collected for many people or cases. The way a data item varies is referred to as its distribution. 5). Histograms are used to show the way scale data is distributed. Data, like salaries or customer lifetimes, are asymmetric with most values being below the average and a few values being much higher. Typically, the average salary will be much higher than the median salary because the few very rich people give the salary distribution a positive skew.

Supervised data analysis is used to estimate an unknown dependency from known input–output data. Input variables might include the quantities of ­different articles bought by a particular customer, the date they made the ­purchase, the location and the price they paid. Output variables might include an indication of whether the customer responds to a sales campaign or not. Output variables are also known as targets in data mining. In the supervised environment, sample input variables are passed through a ­learning system, and the subsequent output from the learning system is compared with the output from the sample.

The dataset includes ­purchase details, communication information and demographics and is a subset of a large real dataset used for a major data mining exercise. 3). 3 Example data – 50 000 sample customers and table of order details. 4 Example data – ENBIS Challenge. Introduction  11 Initially, there are around 200 variables, but these will be augmented as described in the following text. Another dataset is from web analytics. 4). Most of the calculations in this book have been carried out using JMP ­software or tools from the SAS analytical software suite.

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