Data Mining : An Overview

Data Preparation

• Data, Dataset and Database

• Data Extraction, Transformation and Loading (ETL)

• Data Cleansing

Data Exploration

• Univariate Statistical Analysis

• Bivariate Statistical Analysis

• Principal Components Analysis (PCA)

• Visualization

• Case Study

Classification & Regression

• Models based on Frequency Table 

  • ZeroR & OneR

  • Naive Bayesian (NB)

  • Bayesian Belief Network (BBN)

  • Decision Trees (DT)

• Models based on Covariance Matrix

  • Multiple Linear Regression (MLR)

  • Principal Components Regression (PCR)

  • Linear Discriminant Analysis (LDA)

  • Logistic Regression (LR)

• Models based on Similarity Functions

  • K-Nearest Neighbors (KNN)

• Others

  • Artificial Neural Networks (ANN)

  • Support Vector Machines (SVM)

Clustering

• Hierarchical

• K-Means

• Self Organizing Map (SOM)

Association Rules (AR)

Model Evaluation

• Confusion Matrix

• Gain, Lift, K-S and ROC Charts

• Mean Squared Error (MSE)

Model Deployment

Text and Web Mining (external source)

Data Mining Map