Processing ......
FreeComputerBooks.com
Links to Free Computer, Mathematics, Technical Books all over the World
 
Data Mining Desktop Survival Guide
网址居 (LinkBasket) - 全球各国中英文新闻网站导航!.
  • Title: Data Mining Desktop Survival Guide
  • Author(s) Graham Williams
  • Publisher: togaware.com (2010)
  • Paperback: N/A
  • eBook: HTML
  • Language: English
  • ISBN-10: N/A
  • ISBN-13: N/A
  • Share This:  

Book Description

The book thoroughly acquaints you with the new generation of data mining tools and techniques and shows you how to use them to make better business decisions. This guide describes techniques for detecting customer behavior patterns useful in formulating marketing, sales and customer support strategies. While database analysts will find more than enough technical information to satisfy their curiosity, technically savvy business and marketing managers will find this book accessible.

Assuming no prior knowledge of R or data mining/statistical techniques, the book also covers a diverse set of problems that pose different challenges in terms of size, type of data, goals of analysis, and analytical tools.

About the Authors
  • N/A
Reviews and Rating:
  • N/A
Related Book Categories: Read and Download Links: Similar Books:
  • Foundations of Machine Learning (Mehryar Mohri, et al)

    This book is a general introduction to machine learning. It covers fundamental modern topics in machine learning while providing the theoretical basis and conceptual tools needed for the discussion and justification of algorithms.

  • Advanced Data Analysis from an Elementary Point of View

    This is a textbook on data analysis methods, intended for advance undergraduate students who have already taken classes in probability, mathematical statistics, and linear regression. It presumes that you can read and write simple functions in R.

  • Hands-On Data Visualization: From Spreadsheets to Code

    This book takes you step-by-step through tutorials, real-world examples, and online resources. This practical guide is ideal for anyone who wants to take data out of spreadsheets and turn it into lively interactive stories.

  • Mining of Massive Datasets (Jure Leskovec, et al)

    It focuses on practical algorithms that have been used to solve key problems in data mining and can be applied successfully to the largest datasets. It begins with a discussion of the map-reduce framework, an important tool for parallelizing algorithms automatically.

  • Bayesian Data Analysis (Andrew Gelman, et al.)

    This classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. It takes an applied approach to analysis using up-to-date Bayesian methods.

  • Data Mining and Analysis: Fundamental Concepts and Algorithms

    This textbook provides a broad yet in-depth overview of data mining, integrating related concepts from machine learning and statistics. The main parts of the book include exploratory data analysis, pattern mining, clustering, and classification.

  • Answering Questions with Data (Matthew Crump, et al.)

    This is a free textbook teaching introductory statistics for undergraduates. Students will learn to select an appropriate data analysis technique, carry out the analysis, and draw appropriate conclusions.

  • Exploratory Data Analysis with R (Roger D. Peng)

    This book covers the essential exploratory techniques for summarizing data with R. These techniques are typically applied before formal modeling commences and can help inform the development of more complex statistical models.

  • Basic Data Analysis and More - A Guided Tour using Python

    In this book, a selection of frequently required statistical tools will be introduced and illustrated. An exemplary implementation of the presented techniques using the Python programming language is provided.

  • Text Mining with R: A Tidy Approach (Julia Silge, et al)

    You'll explore text-mining techniques with tidytext, a package that authors developed using the tidy principles behind R packages like ggraph and dplyr. You'll learn how tidytext and other tidy tools in R can make text analysis easier and more effective.

  • Theory and Applications for Advanced Text Mining (S. Sakurai)

    This book introduces advanced text mining techniques. They are various techniques from relation extraction to under or less resourced language. Text mining techniques have been studied aggressively in order to extract the knowledge from the data.

Book Categories
:
Other Categories
Resources and Links