AD 603 (A)Data Mining & Warehousing
Unit-I: Data Warehousing :
Data Warehousing: Introduction, Delivery Process, Data warehouse Architecture,Data Pre-processing: Data cleaning, Data Integration and transformation, Data reduction. Data warehouse Design: Data warehouse schema, Partitioning strategy Data warehouse Implementation, Data Marts,Meta Data, Example of a Multidimensional Data model.Introduction to Pattern Warehousing.
Unit-II: OLAP Systems:
Basic concepts, OLAP queries, Types of OLAP servers, OLAP operations etc.Data Warehouse Hardware and Operational Design: Security, Backup And Recovery.
Unit-III: Introduction to Data& Data Mining :Data Types, Quality of data, Data Pre-processing, Similaritymeasures, Summary statistics, Data distributions, Basic data mining tasks, Data Mining V/sknowledge discovery in databases. Issues in Data mining. Introduction to Fuzzy sets and fuzzylogic.
Unit-IV: Supervised Learning: Classification: Statistical-based algorithms, Distance-based algorithms,Decision tree-based algorithms, Neural network-based algorithms, Rule-based algorithms,Probabilistic Classifiers
Unit-V: Clustering & Association Rule mining: Hierarchical algorithms, Partitional algorithms,Clustering large databases – BIRCH, DBSCAN, CURE algorithms.Association rules: Parallel and distributed algorithms such as Apriori and FP growth algorithms.
Books Recommended:
Text Books:
1. Pang – ningTan , Steinbach & Kumar, “Introduction to Data Mining”, Pearson Edu, 2019.
2. Jaiwei Han, MichelineKamber, “Data Mining : Concepts and Techniques”, Morgan Kaufmann Publishers.
Reference Books:
1. Margaret H. Dunham, “Data Mining : Introductory and Advanced topics”, Pearson Edu.,2009.
2. Anahory& Murray, “Data Warehousing in the Real World”, Pearson Edu., 2009.
Notes:
AD 603 (A)Data Mining & Warehousing Unit -IV
AD 603 (A)Data Mining & Warehousing Unit -V
Assignments:
RGPV OLD PAPER
LAB Experiments-
Design of Star Schema (Data Warehouse Design)
OLAP Operations (Roll-Up, Drill-Down, Slice, Dice)
Text Preprocessing using Tokenization, Stopword Removal and Stemming
Implementation of Regular Expressions for Text Processing
Implementation of Minimum Edit Distance Algorithm
Implementation of N-grams in NLP
Part-of-Speech (POS) Tagging Implementation
Implementation of Named Entity Recognition (NER)
Sentiment Analysis of Text
Implementation of Chatbot (Simple Rule-Based)
Lab Manual Click Here