Back close

Course Detail

Learning Objectives

  • LO1 To introduce the concepts of pattern processing
  • LO2 To provide insights on different techniques of pattern processing – supervised and unsupervised
  • LO3 To provide knowledge on techniques of data visualization techniques

Course Outcomes

  • CO1: Ability to understand the basic concepts of data mining
  • CO2: Ability to apply data mining, clustering, classification, and data visualization techniques
  • CO3: Ability to analyse data using mining, clustering, and classification techniques
  • CO4: Ability to evaluate the effectiveness of various algorithms

Course Contents

Challenges in data mining – Data pre-processing – An overview of data cleaning methods – Data integration – Data reduction and data transformation – Dimensionality reduction – Linear regression – Regularisation.

Introduction to classification and clustering – Decision trees and random forests – Bayesian classifier – Support vector machines – Neural networks – Metrics for evaluating classifier performance – Model selection using statistical tests of significance – Comparing classifiers based on cost-benefit and ROC curves – Techniques to improve classification accuracy Cluster analysis – Distance measures – k-means and k-Medoids – Agglomerative versus divisive hierarchical clustering – Detecting outliers.

Data visualisation – Bar plots – Histogram – Box plots – Violin plots – Pairplots – Distplot – Scatter plots – Pie charts – Bubble plots – Regression plots – Quantile plots – Heatmaps – Plotting covariance matrices – Waffle chart – Word cloud – PCA – LDA – Manifold learning for data visualisation – t-SNE – UMAP.


  1. Jiawei Han, Micheline Kamber, Jian Pei, Data Mining: Concepts and Techniques, Third Edition, Morgan Kaufmann Publishers (Elsevier), 2011.
  2. K.P Soman, Shyam Diwakar, V. Ajay, Insight into Data Mining: Theory and Practice, PHI Learning Private Ltd., New Delhi, 2006.
  3. J Vanderplas, Python Data Science Handbook, Nov. 2016, O’Reilly, Media Inc.

DISCLAIMER: The appearance of external links on this web site does not constitute endorsement by the School of Biotechnology/Amrita Vishwa Vidyapeetham or the information, products or services contained therein. For other than authorized activities, the Amrita Vishwa Vidyapeetham does not exercise any editorial control over the information you may find at these locations. These links are provided consistent with the stated purpose of this web site.

Admissions Apply Now