UNIVERSITY OF KERALA - Marian Engineering College

B.Tech Comp. Sc. & Engg., University of Kerala 70
08.705 (4) DATA MINING TECHNIQUES (ELECTIVE II) 3 – 1 – 0
(Common with F 08.706C)
Module I (17 hours)
Fundamentals of data mining -Basic data mining tasks, Issues, DM versus KDD Data preprocessing-
Aggregation, Sampling, Dimensionality reduction, Feature subset selection, Feature creation, Discretization
and Binarization, Variable transformation Data warehousing and OLAP Technology – Introduction to Data
warehouse, Multidimensional data model, Data warehouse architecture and implementation, Data
warehousing and data mining, System architecture.
Module II (17 hours)
Association and Correlation -Basic algorithms, Advanced association rule techniques, Measuring the quality
rules, From association mining to correlation analysis, Constraint based association mining
Association and Prediction - Classification and prediction, Issues, Algorithms-Decision tree-based, statisticalbased,
Distance-based, Nueral network and rule-based. Support vector machines, Other classification
methods, Prediction, Accuracy and Error measures, Evaluation of accuracy of classifier or predictor,
Increasing the accuracy, model selection.
Module III (18 hours)
Cluster analysis –Types of data in cluster analysis, classification of major clustering methods. Partitional
algorithms -Hierarchical methods, Density based methods, Grid based methods, Model based clustering
methods. Clustering large data bases, Constraint based cluster analysis
Advanced Topics -Multidimensional analysis and descriptive mining of complex data objects, Spatial mining,
Multimedia mining, Text mining, Web mining, Temporal mining.
Text Books:
1 Data Mining:Concepts and Techniques – Jiawei Han and Micheline Kamber, Morgan Kaufmann Publishers
2. Data Mining:Introductory and Advanced Topics – Margaret H.Dunham and S.Sridhar, Pearson Education
Reference Books:
1 Building the Data Warehouse – William H.Inmon, Wiley Publishing
2 Data mining techniques – Arun K Pujari, Universities Press
3 Data Warehousing, Data Mining and OLAP – A. Berson and S.J. Smith, TMH
4 Data Mining Methods and Models – D.T. Larose, Wiley
Internal Continuous Assessment (Maximum Marks-50)
25 Marks - Tests (minimum 2)
15 Marks - Assignments (minimum 3) such as home work, problem solving, literature survey, seminar,
term-project, software exercises, etc.
10 Marks - Regularity in the class
University Examination Pattern
PART A: Short answer questions 10 x 4 marks=40 marks
All questions are compulsory. There should be at least three questions
from each module and not more than four questions from any module.
PART B: Descriptive/Analytical/Problem solving questions 3 x 20 marks=60 marks
Candidates have to answer one question out of two or two questions
out of four from each module
Maximum Total Marks: 100