Day 1: Introduction to Data mining
Data-Information-Database-Warehouse,
Un/Labeled Data Supervised-Unsupervised-Semi Supervised ML, Classification,
Regression, Clustering, Association.
Assignment: Depth learning of Classification, Regression, Clustering, Association,
Day 2: Data Pre-processing
KDD(Knowledge
Discovery from data) process, Data preprocessing, data cleaning, smoothing
noisy data (Binning: mean-boundaries), Clustering, Data integration(Tight
Coupling, Loose coupling), Normalization(Min-max, Z-score, Decimal scaling normalization),
Box Plotting.
Assignment: Depth learning of Data integration (Tight Coupling, loose coupling), Normalization.
Day 3: Data Pre-processing
Data-transformation
(Discretization method), Binning, Discretization by Classification &
correlation analysis, Categorical to Numerical Attributes, Data Reduction, Data
Cube Aggregation, Dimensionality Reduction, Principal Component Analysis(PCA),
Heuristic Search in Attribute Selection, Decision Tree Induction,
Assignment: Depth learning of Classification, Regression, Clustering, Association, Data
integration (Tight Coupling, loose coupling), Normalization.
Day 4: Classification &
Prediction
Classification, Prediction,
Clustering, Classification, Learning Issues, Model Construction, Model
Evaluation, Model Use: Classification, Evaluating Models, Cross Validation, Bootstrap
Validation, Confusion
Matrix & Accuracy Metrics
Assignment: Depth learning of Cross Validation, Bootstrap Validation.
Day 5: Classification &
Prediction + Weka Overview
Precision, Recall, F measure,
Linear Regression, Nonlinear Regression.
Assignment: Depth learning of Precision, Recall, F measure, Linear Regression, Nonlinear
Regression.
Day 5: Weka Overview 1
Details overview of weka and
datasets.
Assignment: Practice in weka.
Day 5: Weka Overview 2
Overview of weka.
Assignment: Practice in weka.