IT6T4
IT6T4
IT6T4
Objectives:
To provide an overview of the techniques and developments in the data warehousing and
mining.
To explain the role of data warehousing techniques and applicability in commercial data.
To characterize the kinds of patterns using association rule mining and classification.
To introduce basic concepts of clustering and outliers present in data.
Outcomes:
Students will be able to
Understand the basic principles of Data Mining and data preprocessing.
Differentiate the concepts of data warehousing and OLTP.
Relate the learned algorithms in association and pattern mining to the practical issues.
Describe and utilize a range of techniques for classifying the data and accuracy
improvements.
Analyze the data and develop some clustering and outlier methods.
Prerequisite:
Database Systems
Syllabus:
UNIT – I
Introduction: Fundamentals of data mining, Data Mining Functionalities, Classification of Data
Mining systems, Major issues in Data Mining. Data Preprocessing: Needs Preprocessing the Data,
Data Cleaning, Data Integration, Data Reduction, Data Transformation and Discretization.
UNIT – II
Data Warehousing and Online Analytical Processing: Basic Concepts, Data Warehouse Modeling:
Data Cube and OLAP. Data Objects and Attribute Types, Basic Statistical Description of Data,
Measuring Data Similarity and Dissimilarity.
UNIT – III
Mining Frequent Patterns, Associations, and Correlations: Basic Concepts, Frequent Item set
Mining Methods, Pattern Evaluation Methods, and Pattern Mining in Multilevel, Multidimensional
Space.
UNIT-IV
Classification: Basic Concepts, Decision Tree Induction, Bayes Classification Methods, Rule-Based
Classification, Model Evaluation and Selection, Techniques to Improve Classification Accuracy.
UNIT – V
Cluster Analysis: Basic Concepts and Methods, Cluster Analysis, Partitioning Methods, Hierarchical
Methods. Cluster Analysis: Density-Based Methods, Grid-Based Methods, Evaluation of Clustering.
Outlier Detection: Outliers and Outlier Analysis, outlier Detection Methods. Introduction to text
mining.
Text Book:
1. Data Mining – Concepts and Techniques – 3rd Edition, Jiawei Han, Micheline Kamber & Jian
Pei-Elsevier.
Reference Books:
1. Introduction to Data Mining: Pang-Ning Tan, Michael Steinbach, VipinKumar, Pearson
2. Data Mining Techniques – Arun K Pujari, University Press.
3. Data Warehousing in the Real World – Sam Anahory& Dennis Murray. Pearson Edn Asia.
4. Data Warehousing Fundamentals – PaulrajPonnaiah Wiley Student Edition.
5. The Data Warehouse Life cycle Tool kit – Ralph Kimball Wiley Student Edition.
e-Learning Resources:
1. https://weka.waikato.ac.nz/explorer
2. http://rapidminerresources.com
3. https://www.coursera.org