MNIT Jaipur Syllabus computer science Data Mining

 

 

 

MNIT Jaipur Syllabus computer science   Data Mining

 

 

 

 Data Mining 

Introduction : Basic Data Mining Tasks, Data Mining Issues, Data Mining Metrics, Data Mining from a

Database Perspective.

Data Mining Techniques : A Statistical Perspective on Data Mining, Similarity Measures, Decision

Trees, Neural Networks, Genetic Algorithms.

Classification : Statistical-Based Algorithms, Distance-Based Algorithms, Decision Tree-Based

Algorithms, Neural Network-Based Algorithms, Rule-Based Algorithms, Combining Techniques.

Clustering : Similarity and Distance Measures, Hierarchical Algorithms, Partitional Algorithms,

Clustering Large Databases, Clustering with Categorical Attributes.

Association Rules : Basic Algorithms, Parallel and Distributed Algorithms, Incremental Rules, Advanced

Association Rule Techniques, Measuring the Quality of Rules.Advanced Techniques : Web Mining, Spatial Mining, Temporal Mining.

Text/References:

1. M. H. Dunham. Data Mining: Introductory and Advanced Topics. Pearson Education. 2001.

2. J. Han and M. Kamber. Data Mining: Concepts and Techniques. Morgan Kaufman. 2001.

3. I. H. Witten and E. Frank. Data Mining: Practical Machine Learning Tools and Techniques.

Morgan Kaufmann. 2000.

4. D. Hand, H. Mannila and P. Smyth. Principles of Data Mining. Prentice-Hall. 2001.

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