Data Mining
24CSE654 · S6 · 4 credits
Official KTU 2024 syllabus subject — study with practice, PYQs and AI tools.
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Why:Foundation module
Why:High exam relevance
Why:Core module
Why:Applied concepts
Why:Standard exam coverage
Module 01Introduction to Data Mining and Data Warehousing (6 hours)expand
Data warehouse-Differences between Operational Database Systems and Data Warehouses, Multidimensional data model- Warehouse schema, OLAP Operations, Data Warehouse Architecture, Data Warehousing to Data Mining, Data Mining Concepts and Applications, Knowledge Discovery in Database Vs Data mining, Architecture of typical data mining system, Data Mining Functionalities, Data Mining Issues.
Module 02Data Preprocessing (6 hours)expand
Data Preprocessing-Need of data preprocessing, Data Cleaning- Missing values, Noisy data, Data Integration and Transformation, Data Reduction-Data cube aggregation, Attribute subset selection, Dimensionality reduction, Numerosity reduction, Discretization and concept hierarchy generation.
Module 03Advanced classification and Cluster analysis (9 hours)expand
Classification- Introduction, Decision tree construction principle, Splitting indices - Information Gain, Gini indexDecision tree construction algorithms-ID3, Decision tree construction with presorting-SLIQ, Classification Accuracy-Precision, Recall. Introduction to clustering-Clustering Paradigms, Partitioning Algorithm- PAM, Hierarchical Clustering-DBSCAN, Categorical Clustering-ROCK
Module 04Association Rule Analysis (8 hours)expand
Association Rules-Introduction, Methods to discover Association rules, Apriori(Level- wise algorithm), Partition Algorithm, Pincer Search Algorithm, Dynamic Itemset Counting Algorithm, FP-tree Growth Algorithm.
Module 05Advanced Data Mining Techniques (7 hours)expand
Web Mining - Web Content Mining, Web Structure Mining- Page Rank, Clever, Web Usage Mining- Preprocessing, Data structures, Pattern Discovery, Pattern Analysis. Text Mining-Text Data Analysis and information Retrieval, Basic measures for Text retrieval, Text Retrieval methods, Text Indexing Techniques, Query Processing Techniques.
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