Data Mining

Syllabus

1. Introduction
   1. Data Mining Origin
   2. Data Mining & Data Warehousing basics
2. Data Preprocessing
   1. Data Types and Attributes
   2. Data Pre-processing
   3. OLAP & Multidimensional Data Analysis
   4. Various Similarity Measures
3. Classification
   1. Basics and Algorithms
   2. Decision Tree Classifier
   3. Rule Based Classifier
   4. Nearest Neighbor Classifier
   5. Bayesian Classifier
   6. Artificial Neural Network Classifier
   7. Issues : Overfitting, Validation, Model Comparison
4. Association Analysis
   1. Basics and Algorithms
   2. Frequent Itemset Pattern & Apriori Principle
   3. FP-Growth, FP-Tree
   4. Handling Categorical Attributes
   5. Sequential, Subgraph, and Infrequent Patterns
5. Cluster Analysis
   1. Basics and Algorithms
   2. K-means Clustering
   3. Hierarchical Clustering
   4. DBSCAN Clustering
   5. Issues : Evaluation, Scalability, Comparison
6. Anomaly / Fraud Detection
7. Advanced Applications
   1. Mining Object and Multimedia
   2. Web-mining
   3. Time-series data mining