Data Mining

UNIT I

Data Mining Basics- What is Data Mining, Kinds of Patterns to be Mined, Tasks of Data Mining,

Data Mining Applications- The Business Context of Data Mining, Data Mining as a Research Tool,

Data Mining for Marketing, Benefits of Data Mining, Date Warehousing vs Data Mining.

UNIT II

Data Pre-processing- Review of Data Pre-processing: Types of Data, Data Quality, Measurement and Data Collection Issues, Feature Subset Selection, Feature Creation, Data Discretization and Binning, Knowledge Discovery in Databases.

UNIT III

Machine Learning in Data Mining - Types of classifiers, Rule based classifiers, Model Selection, Model Evaluation, Ensemble Methods, Bias-Variance trade-off, Handling Class Imbalance Problem, Association Rule Mining - Mining Frequent Patterns, Market Basket Analysis, Apriori algorithm, Data Mining using decision trees and KNN algorithm.

UNIT IV

Cluster Analysis- Different Types of Clusters, Hierarchical Methods of Clustering, Density based Clustering: DBSCAN algorithm, Cluster Evaluation. Outlier Analysis, Outlier Detection Methods, Mining Complex Data Types, avoiding False Discoveries.

Textbooks:

1. Tan Pang- Ning, Steinbach M., Viach, Kumar V., “Introduction to Data Mining”, Second Edition, Pearson, 2013.
2. Han J., Kamber M. and Pei J., “Data Mining Concepts and Techniques”, Second Edition, Hart Court India P. Ltd., Elsevier Publications, 2001.

Reference Books:

1. Zaki M.J., Meira W., “Data Mining and Machine Learning: Fundamental Concepts and Algorithms”, Second Edition, Cambridge University Press, 2020
2. Witten, E. Frank, M. Hall, “Data Mining: Practical Machine Learning Tools and Techniques”, Morgan Kaufmann Publishers, 2011.