UNIT I Data Mining Concepts and Outlier Analysis
Data Mining Concepts – Roots – Process – Data Collection to Data Preprocessing – Business aspects of Data Mining – Preparing the Data – Representation – Characteristics – Transformation of Raw Data – Missing Data – Outlier Analysis
UNIT II Feature Reduction and Learning from Machine
Data Reduction – Dimension of Large Data Sets – Features Reduction – Relief Algorithm – Principal Component Analysis – Value Reduction – Learning from Data – Learning Machine – Types of Learning Methods – Support Vector Machines – Semi Supervised Support Vector Machines – Model Selection
UNIT III Predictive Statistical Methods
Statistical Methods – Bayesian Inference – Predictive Regression – Analysis of Variance – Logistic Regression – Log-Linear Models – Linear Discriminant Analysis
UNIT IV Decision Trees and Artificial Neural Networks
Decision Trees – Trees – C4.5 Algorithm – Decision Rules – Cart Algorithm – Artificial Neural Networks – Model of an Artificial Neuron – Learning Process – Self-Organizing Maps – Deep Learning – Convolution Neural Networks
UNIT V Web Mining & Text Mining
Web Mining & Text Mining – Web Content, Structure and Usage Mining – HITS and LOGSOM Algorithms – Mining Path – Traversal Patterns – Page Rank Algorithm – Recommender Systems – Text Mining – Latent Semantic Analysis
Learning Resources
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