Discovering Knowledge in Data: An Introduction to Data Mining

Description:

Learn Data Mining by doing data mining
Data mining can be revolutionary-but only when it's done right. The powerful black box data mining software now available can produce disastrously misleading results unless applied by a skilled and knowledgeable analyst. Discovering Knowledge in Data: An Introduction to Data Mining provides both the practical experience and the theoretical insight needed to reveal valuable information hidden in large data sets.
Employing a "white box" methodology and with real-world case studies, this step-by-step guide walks readers through the various algorithms and statistical structures that underlie the software and presents examples of their operation on actual large data sets. Principal topics include:
* Data preprocessing and classification
* Exploratory analysis
* Decision trees
* Neural and Kohonen networks
* Hierarchical and k-means clustering
* Association rules
* Model evaluation techniques
Complete with scores of screenshots and diagrams to encourage graphical learning, Discovering Knowledge in Data: An Introduction to Data Mining gives students in Business, Computer Science, and Statistics as well as professionals in the field the power to turn any data warehouse into actionable knowledge. 

 

Table of Contents:

Preface.

1. An Introduction to Data Mining.

2. Data Preprocessing.

3. Exploratory Data Analysis.

4. Statistical Approaches to Estimation and Prediction.

5. k-Nearest Neighbor.

6. Decision Trees.

7. Neural Networks.

8. Hierarchical and k-Means Clustering.

9. Kohonen networks.

10. Association Rules.

11. Model Evaluation Techniques.

Epilogue: "We've Only Just Begun".

Index. 

描述：
數據挖掘是一門革命性的學科，但只有在正確應用時才能發揮作用。現在可用的強大黑盒數據挖掘軟件，除非由熟練且知識豐富的分析師應用，否則可能產生具有誤導性的結果。《在數據中發現知識：數據挖掘入門》提供了實踐經驗和理論洞察力，揭示了大型數據集中隱藏的有價值信息。本書採用“白盒”方法，並通過真實案例研究，逐步引導讀者了解軟件背後的各種算法和統計結構，並展示它們在實際大型數據集上的操作示例。主要主題包括：數據預處理和分類、探索性分析、決策樹、神經網絡和Kohonen網絡、分層和k-means聚類、關聯規則、模型評估技術。《在數據中發現知識：數據挖掘入門》配有大量屏幕截圖和圖表，以促進圖形學習，為商業、計算機科學和統計學專業的學生以及該領域的專業人士提供將任何數據倉庫轉化為可操作知識的能力。

目錄：
前言
1. 數據挖掘簡介
2. 數據預處理
3. 探索性數據分析
4. 統計方法的估計和預測
5. k最近鄰算法
6. 決策樹
7. 神經網絡
8. 分層和k-means聚類
9. Kohonen網絡
10. 關聯規則
11. 模型評估技術
結語：我們才剛剛開始
索引