By Galit Shmueli, Nitin R. Patel, Peter C. Bruce
Incorporating a brand new specialise in information visualization and time sequence forecasting, info Mining for enterprise Intelligence, moment variation keeps to provide insightful, designated advice on basic info mining thoughts. This new version publications readers by using the Microsoft workplace Excel add-in XLMiner for constructing predictive types and strategies for describing and discovering styles in data.
From clustering clients into industry segments and discovering the features of widespread flyers to studying what goods are bought with different goods, the authors use fascinating, real-world examples to construct a theoretical and useful figuring out of key information mining tools, together with category, prediction, and affinity research in addition to facts relief, exploration, and visualization.
The moment version now features:
3 new chapters on time sequence forecasting, introducing renowned company forecasting tools together with relocating ordinary, exponential smoothing tools; regression-based versions; and subject matters reminiscent of explanatory vs. predictive modeling, two-level types, and ensembles
A revised bankruptcy on information visualization that now gains interactive visualization rules and extra assignments that show interactive visualization in practice
Separate chapters that every deal with k-nearest acquaintances and Naïve Bayes methods
Summaries initially of every bankruptcy that provide an summary of key topics
The booklet contains entry to XLMiner, permitting readers to paintings hands-on with the supplied info. during the ebook, purposes of the mentioned themes specialize in the enterprise challenge as motivation and stay away from pointless statistical conception. every one bankruptcy concludes with routines that let readers to evaluate their comprehension of the provided fabric. the ultimate bankruptcy features a set of instances that require use of the several information mining concepts, and a comparable website gains info units, workout ideas, PowerPoint slides, and case solutions.
Data Mining for company Intelligence, moment version is a wonderful ebook for classes on information mining, forecasting, and selection aid platforms on the upper-undergraduate and graduate degrees. it's also a one of a kind source for analysts, researchers, and practitioners operating with quantitative equipment within the fields of industrial, finance, advertising, desktop technology, and knowledge know-how.
Read or Download Data Mining for Business Intelligence: Concepts, Techniques, and Applications in Microsoft Office Excel with XLMiner PDF
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Additional resources for Data Mining for Business Intelligence: Concepts, Techniques, and Applications in Microsoft Office Excel with XLMiner
54 Categorical variables, however, often cannot be used as is. In those cases they must be decomposed into a series of dummy binary variables. For example, a single variable that can have possible values of “student,” “unemployed,” “employed,” or “retired” would be split into four separate variables: Student—Yes/No Unemployed—Yes/No Employed—Yes/No Retired—Yes/No Note that only three of the variables need to be used; if the values of three are known, the fourth is also known. For example, given that these four values are the only possible ones, we can know that if a person is neither student, unemployed, nor employed, he or she must be retired.
However, we can assess variability and the performance of our data mining technique, using the validation data, and therefore this need not present a major problem. Some datasets contain variables that have a very large number of missing values. In other words, a measurement is missing for a large number of records. In that case, dropping records with missing values will lead to a large loss of data. Imputing the missing values might also be useless, as the imputations are based on a small number of existing records.
Similarly, we can add predictors to a model to sharpen its performance with the data at hand. Consider a database of 100 individuals, half of whom have contributed to a charitable cause. Information about income, family size, and zip code might do a fair job of predicting whether or not someone is a contributor. If we keep adding additional predictors, we can improve the performance of the model with the data at hand and reduce the misclassification error to a negligible level. ” 58 For example, one of the variables might be height.