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Data Warehousing & Mining Course

This document outlines a course on data warehousing and data mining. It includes 5 units that cover topics like data warehousing concepts and design, data mining techniques, association rule mining, classification methods, and clustering. Assessment will include tests and assignments to evaluate students' abilities to design a data warehouse, apply data preprocessing, mine frequent patterns, develop supervised learning models, and build unsupervised learning models. The course aims to help students apply data mining concepts and techniques.

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124 views2 pages

Data Warehousing & Mining Course

This document outlines a course on data warehousing and data mining. It includes 5 units that cover topics like data warehousing concepts and design, data mining techniques, association rule mining, classification methods, and clustering. Assessment will include tests and assignments to evaluate students' abilities to design a data warehouse, apply data preprocessing, mine frequent patterns, develop supervised learning models, and build unsupervised learning models. The course aims to help students apply data mining concepts and techniques.

Uploaded by

visdag
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
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Download as DOCX, PDF, TXT or read online on Scribd
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DATA WAREHOUSING AND DATA MINING

(Common to ………………..and ……………… branches)


Category L T P Credit
3 0 0 3
Preamble: To

Prerequisites: Database management systems


UNIT – I Data Warehouse 9
Data warehouse -basic concepts- Modeling – Design and usage- Implementation –Data generalization by
Attribute-oriented induction approach

UNIT – II Data Mining 9


Data Mining : Introduction- Kinds of Data and Patterns–Major issues in data mining- Data Objects and
attribute types –Statistical description of data - Measuring data similarity and dissimilarity
Data preprocessing : Overview-Data cleaning- Data integration –Data reduction-Data transformation and
discretization.

UNIT – III Association Rule Mining 9


Association Rule Mining : Basic concepts- Frequent itemset mining methods : Apriori algorithm- A
pattern growth approach for mining frequent item sets—Pattern evaluation methods- Mining multilevel ,
multi dimensional space.

UNIT – IV Classification 9
Basic concepts- Decision Tree Induction - Bayes Classification Methods – Rule Based Classification-Model
evaluation and selection – Support Vector Machines- Classification using frequent patterns-k-NN

UNIT – V Clustering 9
Cluster analysis- Partitioning methods- Hierarchical methods- Density based methods – Grid based methods –
Evaluation of Clustering Methods– Introduction to Outlier Analysis - Data Mining Applications.

Lecture: 45 , Practical: 0 , Total:45


REFERENCE BOOKS/MANUAL:
1. Jiawei Han and Micheline Kamber, “Data Mining Concepts and Techniques”, Third Edition, Elsevier,
2012
2. G.K.Gupta, “Introduction to Data Mining with Case Studies”, Eastern Economy Edition,
Prentice Hall of India, 2006
3. Charu C. Aggarwal, “Data Mining: The Textbook”, Kindle Edition, Springer, 2015
COURSE OUTCOMES BT Mapped
On completion of the course the students will be able to (Highest Level)
CO1: Design a data warehouse Applying (K3)

CO2: apply and analysis of pre processing techniques Applying (K3)

CO3: mine a correlation based frequent patterns in large data sets Applying (K3)

CO4: develop a supervised learning model Applying (K3)

CO5: build an unsupervised learning model Applying (K3)

Mapping of COs with POs and PSOs


COs/POs PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8 PO9 PO10 PO11 PO12 PSO1 PSO2
CO1 3 2 1 1 3 2

CO2 3 2 1 1 3 2

CO3 3 2 1 1 3 2

CO4 3 2 1 1 3 2

CO5 3 2 1 1 3 2

Average 3 2 1 1 3 2

1 – Slight, 2 – Moderate, 3 – Substantial BT – Bloom’s Taxonomy

ASSESSMENT PATTERN – THEORY

Test/Bloom’s Rememberin Understanding Applyin Analyzin Evaluatin Creatin Total


Category* g (K1) % (K2) % g (K3) % g (K4) % g (K5) % g %
(K6) %
CAT 1-50 20 40 40 100
marks
CAT 2-50 20 30 30 20 100
marks
CAT 3-50 20 30 30 20 100
marks
ESE -100 20 30 30 20 100
marks
* +3% may be varied

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