Syllabus
Unit 1
What is Data Mining? Motivating Challenges; The origins of data mining; Data Mining Tasks. Types of Data; Data Quality. Data Preprocessing; Measures of Similarity and Dissimilarity, Machine learning, Hypothesis, Version space, MAP, Maximum likelihood. Classification: Preliminaries; General approach to solving a classification problem; Decision tree induction; Rule-based classifier; Nearest-neighbor classifier, SVM, Artificial Neural Networks. Association Analysis: Problem Definition; Frequent Itemset generation; Rule Generation; Compact representation of frequent itemset; Alternative methods for generating frequent item-sets, Neural Networks, Cluster Analysis:
Unit 2
Overview, K-means, Agglomerative hierarchical clustering, DBSCAN, Overview of Cluster Evaluation, Further Topics in Data Mining: Multidimensional analysis and descriptive mining of complex data objects; Spatial data mining; Multimedia data mining; Text mining; Mining the WWW. Outlier analysis, data mining applications; Additional themes on Data mining; Social impact of Data mining; Trends in Data mining. Data warehouse ? Difference between Operational DBs and Data warehouses ? Multidimensional Data Model ? Data warehouse Architecture ?
Unit 3
Data warehouse Implementation ? OLAP Techniques Concepts & Disadvantages, Data Mining, Introduction Data Mining ? Knowledge Discovery from Databases (KDD) Process ? Data Processing for Data Mining ? Data Cleaning, Integration, Transformation, Reduction ? Data Mining Primitives ? Data Mining Query Language,
Objectives and Outcomes
Learning Objective: The course gives an idea of the different algorithms to be used to train and test systems, along with mining relevant biological data from a system. Course outcome:CO1: To understand the concept of machine learning. CO2: Learn the different classification and clustering algorithms CO3: Apply data mining techniques to extract information from databases
Text Books / References
Textbooks1. ?Data Mining: Concepts and Techniques (The Morgan Kaufmann Series in Data Management Systems) — by Jiawei Han, Micheline Kamber (2011);2. ?Insight into Data Mining ? Theory and Practice? by K.P. Soman, Shyam Diwakar, V.Ajay, PHI, 2006.
