By Ian H. Witten
Data Mining: useful laptop studying instruments and strategies, Fourth variation, offers an intensive grounding in computer studying options, besides functional suggestion on employing those instruments and strategies in real-world information mining occasions. This hugely expected fourth version of the main acclaimed paintings on facts mining and desktop studying teaches readers every thing they should comprehend to get going, from getting ready inputs, examining outputs, comparing effects, to the algorithmic tools on the center of winning information mining approaches.
Extensive updates replicate the technical alterations and modernizations that experience taken position within the box because the final variation, together with huge new chapters on probabilistic equipment and on deep studying. Accompanying the ebook is a brand new model of the preferred WEKA computing device studying software program from the college of Waikato. Authors Witten, Frank, corridor, and buddy comprise modern-day ideas coupled with the tools on the cutting edge of up to date research.
Please stopover at the booklet better half web site at http://www.cs.waikato.ac.nz/ml/weka/book.html
- Powerpoint slides for Chapters 1-12. this can be a very complete educating source, with many PPT slides protecting every one bankruptcy of the book
- Online Appendix at the Weka workbench; back a truly finished studying relief for the open resource software program that is going with the book
- Table of contents, highlighting the various new sections within the 4th version, in addition to reports of the first variation, errata, etc.
- Provides an intensive grounding in computing device studying thoughts, in addition to sensible suggestion on utilizing the instruments and methods to information mining projects
- Presents concrete assistance and methods for functionality development that paintings by means of reworking the enter or output in computer studying methods
- Includes a downloadable WEKA software program toolkit, a accomplished selection of computing device studying algorithms for facts mining tasks-in an easy-to-use interactive interface
- Includes open-access on-line classes that introduce functional functions of the fabric within the book
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Additional info for Data mining : practical machine learning tools and techniques
But when explaining what algorithms do and how they work, we need simple examples that capture the essence of the problem but are small enough to be comprehensible in every detail. We will be working with the illustrations in this section throughout the book, and they are intended to be “academic” in the sense that they will help us to understand what is going on. 3, and many more are covered in the books mentioned in the Further reading section at the end of the chapter. Another problem with actual real-life datasets is that they are often proprietary.
In essence, this reflects both definitions of learning considered previously: the acquisition of knowledge and the ability to use it. Many learning techniques look for structural descriptions of what is learned, descriptions that can become fairly complex and are typically expressed as sets of rules such as the ones described previously or the decision trees described later in this chapter. Because they can be understood by people, these descriptions serve to explain what has been learned and explain the basis for new predictions.
As the world grows in complexity, overwhelming us with the data it generates, data mining becomes our only hope for elucidating the patterns that underlie it. Intelligently analyzed data is a valuable resource. It can lead to new insights and, in commercial settings, to competitive advantages. Data mining is about solving problems by analyzing data already present in databases. Suppose, to take a well-worn example, the problem is fickle customer loyalty in a highly competitive marketplace. A database of customer choices, along with customer profiles, holds the key to this problem.