Data mining(I)

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本文介绍了数据挖掘的概念及其过程，即从大量、不完整且可能含噪声的数据中自动抽取隐藏的有用模式。主要内容涵盖监督学习与非监督学习的区别，包括决策树、K近邻、神经网络和支持向量机等分类算法，以及过拟合、交叉验证等概念。

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Learning Notes of Dr.Bo Yuan.THU 《Data：Theory and Algorithm》Part I

Definition:Data Mining is the process of automatically extracting interesting and useful hidden patterns from usually massive,incomplete and noisy data.
Not a fully automatically process.
From data to intelligence.
Data、information、knowledge、decision support
Classification

Algorithms:
Decision Tree、KNN、Neural Networks、SVM
Overfitting
Cross Validation Training data 、Test data

Confusion Matrix 、 TP(True Positive) 、FP（False Positive）、FN（False Negative）、TN（True Negative） 、TPR（True Positive Rate）、 TNR(True Negative Rate)、 Accuracy
TP+FP+FN+TN = number of samples

ROC:Receiver Operating Characteristic
AUC:Area Under ROC Curve #AUC near 1 is good

Cost sensitive learning
Lift analysis
Clustering
Difference:Clustering is Unsupervised Learning,Classification is Supervised Learning

Association Rule
Regression

Underfitting
Overfitting
Data Preprocessing

Garbage Input garbage Output
Cloud Computing
Parallel Computing