4- Course Content :-

• Introduction:  Models, methodologies, and processes.
• Broad themes (search, induction, querying, approximation, and compression).
• Discrete Structures :-Item set mining. Concept lattices. Borders and level wise theories.- Condensed representations.
• Frequent pattern mining. Re-description mining.
• Graphs and other structures. Combinatorial tiles.
• Customized data structures for speeding up data mining algorithms.
• Attribute-Value Learning Techniques: Decision trees. Decision lists. Classification and regression trees.
• Association rules. Correlations. Rule-based mining. Sequential versus simultaneous paradigms.
• Relational Mining Techniques: Inductive logic programming. Main approaches to ILP.
• Rule induction, beam search, logical decision trees.
• Inverse resolution, relative least general generalization.
• Operators for efficient search of relational spaces.
• Comparative merits of attribute value and relational mining techniques.
• Domain theories and incorporating prior background knowledge.
• Probabilistic Techniques: Conditional independence and its modeling. Inference and representational complexity.
• Sequences and order: Total and partial orders.
• Episodes and event streams. Frequent episode mining.
• Order-theoretic methods. Modeling sequential data.
• Discovering sequence information from non-sequential data. Connections with HMMs.
• Bi-clustering. Compositional data mining.
• Mining chains of relations.
• Integrated query/mining languages.
• Paradigms for interfacing with database systems.
• Applications: Data mining applications in bioinformatics, personalization, information retrieval, web modeling, filtering, and text processing.