By Galit Shmueli, Nitin R. Patel, Peter C. Bruce
Incorporating a brand new concentrate on info visualization and time sequence forecasting, information Mining for company Intelligence, moment version keeps to provide insightful, unique tips on basic information mining ideas. This re-creation publications readers by using the Microsoft place of work Excel add-in XLMiner for constructing predictive types and strategies for describing and discovering styles in data.
From clustering consumers into marketplace segments and discovering the features of widespread flyers to studying what goods are bought with different goods, the authors use attention-grabbing, real-world examples to construct a theoretical and useful knowing of key facts mining tools, together with category, prediction, and affinity research in addition to information relief, exploration, and visualization.
The moment variation now features:
3 new chapters on time sequence forecasting, introducing well known enterprise forecasting tools together with relocating typical, exponential smoothing equipment; regression-based types; and themes comparable to explanatory vs. predictive modeling, two-level versions, and ensembles
A revised bankruptcy on info visualization that now beneficial properties interactive visualization rules and further assignments that show interactive visualization in practice
Separate chapters that every deal with k-nearest pals and Naïve Bayes methods
Summaries at first of every bankruptcy that provide an summary of key topics
The e-book contains entry to XLMiner, permitting readers to paintings hands-on with the supplied facts. in the course of the e-book, purposes of the mentioned subject matters specialize in the enterprise challenge as motivation and steer clear of pointless statistical concept. each one bankruptcy concludes with routines that let readers to evaluate their comprehension of the provided fabric. the ultimate bankruptcy contains a set of instances that require use of different info mining ideas, and a comparable site beneficial properties information units, workout strategies, PowerPoint slides, and case solutions.
Data Mining for company Intelligence, moment version is a superb booklet for classes on information mining, forecasting, and determination help structures on the upper-undergraduate and graduate degrees. it's also a exclusive source for analysts, researchers, and practitioners operating with quantitative tools within the fields of industrial, finance, advertising and marketing, laptop technological know-how, and data 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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Extra info for Data Mining for Business Intelligence: Concepts, Techniques, and Applications in Microsoft Office Excel with XLMiner
Similarly, for categorical variables we 46 use bar charts. We can also look at scatterplots of pairs of numerical variables to learn about possible relationships, the type of relationship, and again, to detect outliers. Visualization can be greatly enhanced by adding features such as color, zooming, and interactive navigation. 3 Supervised and Unsupervised Learning A fundamental distinction among data mining techniques is between supervised and unsupervised methods. Supervised learning algorithms are those used in classification and prediction.
The core of this book focuses on what has come to be called predictive analytics, the tasks of classification and prediction that are becoming key elements of a “business intelligence” function in most large firms. These terms are described and illustrated below. 43 Not covered in this book to any great extent are two simpler database methods that are sometimes considered to be data mining techniques: (1) OLAP (online analytical processing) and (2) SQL (structured query language). OLAP and SQL searches on databases are descriptive in nature (“find all credit card customers in a certain zip code with annual charges > $20,000, who own their own home and who pay the entire amount of their monthly bill at least 95% of the time”) and do not involve statistical modeling.
If many different models are being tried out, it is prudent to save a third sample of known outcomes (the test data) to use with the model finally selected to predict how well it will do. The model can then be used to classify or predict the outcome of interest in new cases where the outcome is unknown. Simple linear regression analysis is an example of supervised learning (although rarely called that in the introductory statistics course where you probably first encountered it). The Y variable is the (known) outcome variable and the X variable is a predictor variable.