# Data and Data Types

• Attribute (ordimensions, features, variables): a data field, representing a characteristic or feature of a data object.
  属性 (或维度、特征、变量)：数据字段，表示数据对象的特征或特征。

  • E.g., customer_ID, name, address

• Types

  • Categorical
    • Nominal 标称型变量
    • Binary 二元的
    • Ordinal 有序的
  • Numerical
    • Interval-scaled or Ratio-scaled 间隔标度或比率标度
    • Discrete or continuous 离散和连续

一旦有了数据，则可能需要执行数据预处理。

# Data Preprocessing

• Deal with Missing values 处理缺失值
  • What are the possible solutions?

  Missing Values
  如果是数值型变量，则求解缺失数据。
  如果是标称型变量，则填写缺失数据。

• Reduce variance for a numerical variable: binning
  减少数值变量的方差
• Correlation analysis: for different variables
  相关分析
• Data Normalization
  • Min-max, z-score, decimal scaling
• Data Transformation: numerical <--> nominal Discretization
• Feature Selection and Reduction
  • Wrapper, Filter Models
  • PCA
• Outlier Detection

# Supervised vs. Unsupervised Learning

# Supervised Learning

• infer a (predictive) function from data associated with pre-defined targets/classes/labels
  从与预定义目标 / 类别 / 标签相关的数据推断（预测）函数
• Example: group objects by predefined labels
  示例：按预定义标签对对象进行分组
• Goal: Learn a model from labelled data (with multiple features) for future predictions
  目标：从标记数据 (具有多个特征) 中学习模型，以用于未来的预测
• Outcomes: We know outcomes: the predefined labels
  结果：我们知道结果：预定义的标签
• Evaluation: error/accuracy, and other more metrics
  评估：错误 / 准确性和其他更多指标
• Data Mining Task: Classification
  数据挖掘任务：分类

# Unsupervised Learning

• discover or describe underlying structure from unlabelled data
  从未贴标签的数据中发现或描述底层结构
• Example: group objects by multiple features
  示例：按多个特征对对象进行分组
• Goal: Learn the structure from unlabelled data (with multiple features) Outcomes: We do not know the outcomes
  目标：从未标记数据（具有多个特征）中学习结构结果：我们不知道结果
• Evaluation: No clear performance or evaluation methods
  评估：没有明确的绩效或评估方法
• Data Mining Task: Clustering
  数据挖掘任务：聚类

# Classification

• Classification Task

• Standard Classification Process

• Data Splits for Evaluations

  • Hold-out Evaluation
  • N-folds Cross Evaluation

• KNN Classifier

• Naïve Bayes Classifier

• Decision Trees

• SVM

• Logistic Regression

• Neural Networks

• Ensemble Classifier

• Binary, Multi-Class and Multi-Label classifications

• Imbalance Issues and solutions

• Classification Evaluations & Metrics

# Clustering Tasks/Approaches

Partitional Clustering

just group objects to minimize intra-cluster distances and maximize inter-cluster distances, e.g., K-Means
只需将对象分组以最小化簇内距离并最大化簇间距离，例如 K - 均值

Hierarchical Clustering

a clustering process in order to discover the hierarchical structure, like a hierarchical tree Example: categories and subcategories; taxonomies
为了发现层次结构而进行的聚类过程，如层次树示例：类别和子类别；分类法

# Association Rules

• What Is Association Mining?

• Support and Confidence

• Steps in Association Rule Discovery

• Apriori Algorithm - An Example