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Course Detail

Course Name Data Mining and Applications
Course Code 18CA312
Program M. C. A., M. C. A. ( Offered at Mysuru Campus )
Semester Four
Credits Four
Year Taught 2018
Degree Postgraduate (PG)
School School of Arts and Sciences, School of Engineering
Campus Kochi, Mysuru, Amritapuri


Introduction: Evolution and Importance of Data Mining-Types of Data and Patterns Mined-Technologies-Applications-Major Issues in Data Mining. Knowing about Data-Data Preprocessing: Cleaning– Integration–Reduction–PCA, Data Transformation and Discretization. Mining Frequent Patterns: Basic Concept – Frequent Item Set Mining Methods – Mining Association Rules – Association to Correlation Analysis.

Classification and Prediction: Issues – Decision Tree Induction – Bayesian Classification – Rule Based Classification – k-Nearest-Neighbor Classification – Linear SVM – Regression – Linear, Logistic – Accuracy and Error measures –Introduction to Ensemble methods

Clustering: Overview of Clustering – Types of Data in Cluster Analysis – Major Clustering Methods-Partitioning Methods- k-Means, k-Medoids. Hierarchical Methods-Agglomerative and Divisive hierarchical clustering. Density-Based Methods-DBSCAN, Graph-based clustering (CHAMELEON), Evaluation in Clustering

Mining Data Streams- Mining Time-Series Data- Mining Sequence Patterns in Biological Data- Graph Mining – Social network Analysis – Text Mining – Mining the World Wide Web, Applications and Trends in Data Mining Tools :Implementation of Data mining algorithms using Latest Open Source Data mining Tools.Tensorflow, python, R
Network security: At application layer – Email, PGP, S/MIME. At transport layer – SSL architecture, handshake protocol, changecipherspec protocol, Alert protocol, Record protocol, SSL message format, Transport layer security. At network layer – modes, security protocols, security associations, security policy, Internet key exchange, ISAKMP.

Text Books

  1. Jiawei Han, MichelineKamber and Jian Pei, “Data mining concepts and Techniques”, Third Edition, Elsevier Publisher, 2006.
  2. K.P.Soman, ShyamDiwakar and V.Ajay, “Insight into data mining Theory and Practice”, Prentice Hall of India, 2006.
  3. Yanchang Zhao, “R and Data Mining”, Elsevier, 2013
  4. AurélienGéron, Hands-On Machine Learning with Scikit-Learn and TensorFlow, O’Reilly Media, 2017
  5. Itay Lieder, YehezkelResheff, Tom Hope, Learning TensorFlow, O’Reilly Media, 2017

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