Unsupervised Learning with R

Erik Rodriguez Pacheco

  • 出版商: Packt Publishing
  • 出版日期: 2015-11-30
  • 售價: $1,540
  • 貴賓價: 9.5$1,463
  • 語言: 英文
  • 頁數: 192
  • 裝訂: Paperback
  • ISBN: 1785887092
  • ISBN-13: 9781785887093
  • 相關分類: R 語言Data ScienceMachine Learning
  • 海外代購書籍(需單獨結帳)
    無現貨庫存(No stock available)

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商品描述

Work with over 40 packages to draw inferences from complex datasets and find hidden patterns in raw unstructured data

About This Book

  • Unlock and discover how to tackle clusters of raw data through practical examples in R
  • Explore your data and create your own models from scratch
  • Analyze the main aspects of unsupervised learning with this comprehensive, practical step-by-step guide

Who This Book Is For

This book is intended for professionals who are interested in data analysis using unsupervised learning techniques, as well as data analysts, statisticians, and data scientists seeking to learn to use R to apply data mining techniques. Knowledge of R, machine learning, and mathematics would help, but are not a strict requirement.

What You Will Learn

  • Load, manipulate, and explore your data in R using techniques for exploratory data analysis such as summarization, manipulation, correlation, and data visualization
  • Transform your data by using approaches such as scaling, re-centering, scale [0-1], median/MAD, natural log, and imputation data
  • Build and interpret clustering models using K-Means algorithms in R
  • Build and interpret clustering models by Hierarchical Clustering Algorithm's in R
  • Understand and apply dimensionality reduction techniques
  • Create and use learning association rules models, such as recommendation algorithms
  • Use and learn about the techniques of feature selection
  • Install and use end-user tools as an alternative to programming directly in the R console

In Detail

The R Project for Statistical Computing provides an excellent platform to tackle data processing, data manipulation, modeling, and presentation. The capabilities of this language, its freedom of use, and a very active community of users makes R one of the best tools to learn and implement unsupervised learning.

If you are new to R or want to learn about unsupervised learning, this book is for you. Packed with critical information, this book will guide you through a conceptual explanation and practical examples programmed directly into the R console.

Starting from the beginning, this book introduces you to unsupervised learning and provides a high-level introduction to the topic. We quickly move on to discuss the application of key concepts and techniques for exploratory data analysis. The book then teaches you to identify groups with the help of clustering methods or building association rules. Finally, it provides alternatives for the treatment of high-dimensional datasets, as well as using dimensionality reduction techniques and feature selection techniques.

By the end of this book, you will be able to implement unsupervised learning and various approaches associated with it in real-world projects.

Style and approach

This book takes a step-by-step approach to unsupervised learning concepts and tools, explained in a conversational and easy-to-follow style. Each topic is explained sequentially, explaining the theory and then putting it into practice by using specialized R packages for each topic.

商品描述(中文翻譯)

使用超過40個套件來從複雜的數據集中推斷並找出原始非結構化數據中的隱藏模式。

關於本書
通過R中的實際示例,解鎖並發現如何處理原始數據集的集群。
探索您的數據並從頭開始創建自己的模型。
通過這本全面的實用逐步指南,分析無監督學習的主要方面。

本書適合對使用無監督學習技術進行數據分析感興趣的專業人士,以及希望學習使用R應用數據挖掘技術的數據分析師、統計學家和數據科學家。對R、機器學習和數學的了解會有所幫助,但不是必需的。

您將學到什麼
使用R中的探索性數據分析技術(例如摘要、操作、相關性和數據可視化)加載、操作和探索數據。
通過使用方法(例如縮放、重新居中、縮放[0-1]、中位數/MAD、自然對數和插值數據)轉換數據。
在R中構建和解釋K-Means算法的聚類模型。
在R中構建和解釋層次聚類算法的聚類模型。
了解並應用降維技術。
創建和使用學習關聯規則模型,例如推薦算法。
使用和了解特徵選擇技術。
安裝和使用最終用戶工具,作為在R控制台中直接編程的替代方案。

詳細內容
統計計算的R項目提供了一個出色的平台,用於處理數據處理、數據操作、建模和演示。這種語言的能力、它的使用自由和非常活躍的用戶社區使R成為學習和實施無監督學習的最佳工具之一。

如果您對R不熟悉或想了解無監督學習,那麼本書適合您。這本書充滿了重要信息,將引導您通過概念解釋和直接在R控制台中編程的實際示例。

從一開始,本書介紹了無監督學習並對該主題進行了高層次介紹。我們迅速轉向討論關鍵概念和探索性數據分析的技術應用。然後,本書教您如何使用聚類方法或構建關聯規則來識別群組。最後,它提供了處理高維數據集的替代方法,以及使用降維技術和特徵選擇技術。

通過閱讀本書,您將能夠在實際項目中實施無監督學習和相關方法。

風格和方法
本書以對話和易於理解的方式逐步介紹無監督學習的概念和工具。每個主題都按順序解釋,先解釋理論,然後使用每個主題的專門R套件進行實踐。