| Course Name | Data mining |
| Course Code | 25CSA312 |
| Program | B. Sc. in Physics, Mathematics & Computer Science (with Minor in Artificial Intelligence and Data Science) |
| Semester | 6 |
| Credits | 4 |
| Campus | Mysuru |
Syllabus
(In Python) and Use Kaggle Using Pandas Data frames Visualization and plots – seaborn
Data Preparation – Cleaning – Missing data, Data Reduction – PCA, Data Transformation – Normalization, Binning, distance measures, similarity
Association mining Regression – Linear
Naïve Bayes Classifier, Decision tree, KNN KMeans, Hierarchical clustering
Introduction: Introduction to Data Mining-Types of Data and Patterns Mined- Technologies- Applications-Major Issues in Data Mining. Introduction to Data Warehousing: Basic Concepts and Techniques
Knowing about Data-Data Preprocessing: Cleaning–Integration Reduction–Data Transformation and Discretization.
Mining Frequent Patterns: Basic Concept – Frequent Item Set Mining Methods -Apriori and FP Growth algorithms -Mining Association Rules
Classification and Predication: Issues – Algorithms- Decision Tree Induction – Bayesian Classification –k Nearest Neighbor- Prediction – Accuracy- Precision and Recall
Clustering: Overview of Clustering – Types of Data in Cluster Analysis – K Means and K Medoid, Hierarchical Clustering Algorithms
Course Outcomes
| COs | Description |
| CO1 | Explain data mining process and the resulting patterns, types of data, attributes and knowledge discovery process |
| CO2 | Explain the different data preprocessing techniques before applying the data mining process |
| CO3 | Characterize the kinds of patterns that can be discovered by association rule mining |
| CO4 | Demonstrate the different prediction, classification and clustering algorithms |
| CO5 | Categorize and differentiate between situations for applying different data mining techniques for different applications |
TEXTBOOKS / REFERENCES:
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) William H Inmon “Building the Data Warehouse”, Wiley, Fourth Edition 2005.
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