Predictive Analytics and Data Mining: Concepts and Practice with RapidMiner (Paperback) (預測分析與資料探勘:概念與實務應用於 RapidMiner)
Vijay Kotu, Bala Deshpande
- 出版商: Morgan Kaufmann
- 出版日期: 2014-12-17
- 定價: $2,100
- 售價: 6.0 折 $1,260
- 語言: 英文
- 頁數: 446
- 裝訂: Paperback
- ISBN: 0128014601
- ISBN-13: 9780128014608
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相關分類:
Data-mining、Machine Learning
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相關翻譯:
預測分析與數據挖掘 RapidMiner 實現 (Predictive Analytics and Data Mining: Concepts and Practice with RapidMiner) (簡中版)
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其他版本:
Predictive Analytics and Data Mining: Concepts and Practice
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商品描述
Put Predictive Analytics into Action Learn the basics of Predictive Analysis and Data Mining through an easy to understand conceptual framework and immediately practice the concepts learned using the open source RapidMiner tool. Whether you are brand new to Data Mining or working on your tenth project, this book will show you how to analyze data, uncover hidden patterns and relationships to aid important decisions and predictions. Data Mining has become an essential tool for any enterprise that collects, stores and processes data as part of its operations. This book is ideal for business users, data analysts, business analysts, business intelligence and data warehousing professionals and for anyone who wants to learn Data Mining. You'll be able to: 1. Gain the necessary knowledge of different data mining techniques, so that you can select the right technique for a given data problem and create a general purpose analytics process. 2. Get up and running fast with more than two dozen commonly used powerful algorithms for predictive analytics using practical use cases. 3. Implement a simple step-by-step process for predicting an outcome or discovering hidden relationships from the data using RapidMiner, an open source GUI based data mining tool
Predictive analytics and Data Mining techniques covered: Exploratory Data Analysis, Visualization, Decision trees, Rule induction, k-Nearest Neighbors, Naïve Bayesian, Artificial Neural Networks, Support Vector machines, Ensemble models, Bagging, Boosting, Random Forests, Linear regression, Logistic regression, Association analysis using Apriori and FP Growth, K-Means clustering, Density based clustering, Self Organizing Maps, Text Mining, Time series forecasting, Anomaly detection and Feature selection. Implementation files can be downloaded from the book companion site at www.LearnPredictiveAnalytics.com
- Demystifies data mining concepts with easy to understand language
- Shows how to get up and running fast with 20 commonly used powerful techniques for predictive analysis
- Explains the process of using open source RapidMiner tools
- Discusses a simple 5 step process for implementing algorithms that can be used for performing predictive analytics
- Includes practical use cases and examples
商品描述(中文翻譯)
將預測分析付諸實踐
透過易於理解的概念框架學習預測分析和數據挖掘的基礎知識,並立即使用開源的RapidMiner工具實踐所學概念。無論您是初次接觸數據挖掘還是已經進行了十個項目,本書將向您展示如何分析數據,發現隱藏的模式和關係,以幫助重要的決策和預測。數據挖掘已成為任何收集、存儲和處理數據的企業的必備工具。本書適合商業用戶、數據分析師、業務分析師、商業智能和數據倉儲專業人士,以及任何想學習數據挖掘的人。您將能夠:1. 獲得不同數據挖掘技術的必要知識,以便您可以為給定的數據問題選擇合適的技術並創建通用的分析流程。2. 使用實際應用案例快速上手使用超過二十種常用的強大預測分析算法。3. 使用RapidMiner,一個基於圖形用戶界面的開源數據挖掘工具,實施一個簡單的逐步過程,用於預測結果或從數據中發現隱藏的關係。
涵蓋的預測分析和數據挖掘技術包括:探索性數據分析、可視化、決策樹、規則歸納、k最近鄰算法、朴素貝葉斯、人工神經網絡、支持向量機、集成模型、Bagging、Boosting、隨機森林、線性回歸、邏輯回歸、使用Apriori和FP Growth的關聯分析、K-Means聚類、基於密度的聚類、自組織映射、文本挖掘、時間序列預測、異常檢測和特徵選擇。實施文件可從書籍伴侶網站www.LearnPredictiveAnalytics.com下載。
- 用易於理解的語言解密數據挖掘概念
- 展示如何快速上手使用20種常用的強大預測分析技術
- 解釋使用開源RapidMiner工具的過程
- 討論用於執行預測分析的簡單5步流程
- 包含實際應用案例和示例