Discovering Knowledge in Data: An Introduction to Data Mining

Daniel T. Larose




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:


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".




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