Overview: Meaning of Data Mining and Knowledge Discovery, Data Mining Tasks, Sequence Discovery, Development of Data Mining, Data Mining Issues and Mining Metrics, Social Implications of Data Mining. Data
Mining Techniques: Statistical Perspective on Data Mining, Models Based on Summarization, Bayes Theorem, Hypothesis Testing. Similarity Measures, Decision Tree.
Neural Network: Background, Learning, Basic Neuron Model, Perception, Multiplayer Perception.
Classification: Issues in Classification, Statistical-Based Algorithms, Regression, Bayesian Classification, Distance-Based Algorithms.
Clustering: Similarity and Distance Measures, Hierarchical Algorithms, Agglomerative Algorithms, Divisive Clustering, K-Means Clustering. Association Rules: Meaning of Association, Large Item Sets, Basic Algorithms, Apriori Algorithm.