Download Detailed Syllabus Lecture-wise Breakup Subject Code Semester

Detailed Syllabus
Lecture-wise Breakup
Subject
Code
Subject
Name
Semester Odd
2013
Information Retrieval & Data Mining
Credits
Contact Hours
3
ContactCoordinator
Sangeeta
Instructor
Sangeeta
Module No.
Subtitle of the Module
Topics in the module
1.
Introduction to
Information Retrieval
3
2.
Boolean Retrieval
3.
Dictionary and tolerant
retrieval
Theory of information retrieval,
Information retrieval on data and
information retrieval on the web
Information retrieval tools and their
architecture.
An example information retrieval
problem,
Processing
Boolean
queries, The extended Boolean
model versus ranked retrieval
Wild card queries, Spelling
correction , Phonetic correction
4.
Scoring Term weighting
and the vector space
model
Term frequency and weighting,
Vector space model, Variant tf-idf
scoring
2
5.
Link analysis
Web as graph, PageRank
2
6.
Information retrieval
tools
5
7.
Web Crawling
Web directory, Search engine, Meta
search engines, Web searching and
search
engine
architecture,
Searching algorithms (Fish, Shark
etc...), and Page ranking algorithms.
WebCrawler architecture and Web
crawling (parallel, distributed and
focused web crawling). Nearduplicates and shingling.
JIIT University, Noida
No. of
Lectures for
the module
2
2
4
8.
Q&A system
Enhancing Technical Q&A System
with Cite History [Paper]
Design Lessons from the Fastest
Q&A Site in the West [Paper]
Avaaj Otalo — A Field Study of an
Interactive Voice Forum for Small
Farmers in Rural India [Paper]
Introduction to data mining, data
ware house architecture, metrics
and security.
4
9.
Introduction to data
mining
10.
Data Preprocessing
Data extraction, Data cleaning, Data
Integration and transformation,
Data reduction, loading and post
loading.
2
11.
Classification
Algorithms
Usability and complexity analysis
of Bayesian, Nearest neighbor,
Decision tree based and rule based
algorithms.
5
12.
Clustering Algorithms
4
13.
Association Algorithms
Usability and complexity analysis
of Agglomerative Hierarchical, Kmeans partitioning algorithms.
Usability and complexity analysis
of Apriori, sampling, partitioning,
and multiple minimum support
algorithms.
Total number of Lectures
42
2
5
Recommended Reading material: Author(s), Title, Edition, Publisher, Year of Publication etc.
( Text books, Reference Books, Journals, Reports, Websites etc. in the IEEE format)
1.
Jiawei Han and Micheline Kamber, ”Data Mining, Concepts and Techniques”, Elsevier
2nd edition.
2.
Christopher D. Manning, Prabhakar Raghavan and Hinrich Schütze, “An introduction
to Information Retrieval”, 2009 Cambridge University Press UP.
3.
Margaret H. Dunham, “data mining: introduction and advanced Topics”, Pearson
Education..
4.
Pang-Ning Tan, Michael Steinbach, Vipin Kumar, “Introduction to Data Mining “,
Pearson Education.
5.
Richard O. Duda, Peter E. Hart, David G. Stork , “Pattern Classification”, 2nd Edition,
Wiley Publication, November 2000,
JIIT University, Noida
6.
Rijsbergen C. J. ,”Information Retrieval”, 2nd edition.
7.
Salton, G. and McGill, M.J., “Introduction to Modern Information Retrieval”, Computer
Series. McGraw-Hill, New York, NY.
8.
ACM Transaction on Internet Technology.
9.
ACM Transactions on Database Systems.
10. IEEE Transaction on Knowledge and Data Engineering.
11
ACM Transactions on Information Systems.
JIIT University, Noida