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GUJARAT TECHNOLOGICAL UNIVERSITY Master in Computer Application Year II – (Semester-IV) (W.E.F. January 2017) Subject Name: Data Mining Subject Code: 3640006 1. Objective To understand the need for Data Mining and advantages to the business world. To get a clear idea of various classes of Data Mining techniques, their need, scenarios situations) and scope of their applicability. To learn the algorithms used for various types of Data Mining problems. 2. Prerequisites: Knowledge of RDBMS, OLTP and OLAP 3. Contents: Unit Content 1 Data Mining: Introduction and Preprocessing Weightage # Lectures 15% 07 20% 09 2 Data Mining and Knowledge Discovery Kinds of Data to be mined ( Database data, DataWarehouse data, Transactional data and Other data) Patterns to be mined ( Concept description, Frequent pattens, association, correlations, classification, clustering, outlier analysis) – upto 1.4.5 Technology used in Mining ( statistics, machine learning, database, information retrieval ) Applications of Data Mining Data Preprocessing and its requirement, Preprocessing steps– Data Cleaning, Data Integration and Transformation, Data Reduction, Sampling. Data Transformation and Data Discretization Mining Frequent Patterns, Associations, and Correlations Basic Concepts: Market-Basket Analysis, Frequent Item sets, Closed Item sets and Association rules. Frequent Itemset Mining methods: Apriori algorithm, generating association rules from frequent itemsets, improving efficiency of apriori. Pattern Evaluation methods : 3 CASE STUDY on Apriori algorithm Classification : Basic Concepts and Methods 4 09 25% 09 15% 06 Introduction to Cluster Analysis, Requirements for Cluster Analysis, Types of Data in Cluster Analysis, Partitioning Methods, Centroid-Based Technique: K-Means Method. Overview of Basic Clustering Methods: Partitioning method, Hierarchical method, Density based method, Grid based method. Partitioning Methods : k-Means, K-Medoids, Density based method : DBSCAN OPTICS, Clustering based on Graph partitioning CASE STUDY on clustering methods. Data Mining Trends and Research 25% Introduction to Classification, general approach to classification: supervised learning, unsupervised learning, prediction and Regression analysis, Decision tree induction, attribute selection methods: information gain, Gain ratio, Gini index, tree pruning. CHAID(Chi-square Automatic Interaction Detection) CART(Classification and Regression trees) Bayes Classification methods : Bayes’ Theorem, Naïve Bayesian Classification, Bayesian Belief Networks Rule based Classification: Using IF-THEN Rules for Classification, Rule Extraction from a Decision Trees, Rule Induction Using a Sequential Covering Algorithm Classification by backpropagation Classification using frequent patterns K-Nearest neighbor classifier CASE STUDY on classification methods. Cluster Analysis : Basic Concepts and Methods 5 Frequent Pattern Mining: A Roadmap Applications of pattern mining. Data Mining for: (a) Financial Data Analysis, (b) The Retail Industry, (c) The Science and Engineering, (d) Biological Data Analysis, (e) Other Scientific Applications, (f) Intrusion detection Mining Time-Series and Sequence Data, Graph Mining, Social Network Analysis and Multi relational Data Mining. Overview of Advanced Techniques: Web Mining, Spatial Mining, and Text Mining. 4. Text Books: 1. Data Mining: Concepts & Techniques, Jiawei Han & Micheline Kamber, Morgan Kaufmann Publishers, Elsevier, Third edition . 2. Insight of Data Mining- theory and Practice by K.P.Soman, Shyam Diwakar and V. Ajay, PHI Publication. 5. Other Reference Books: 1. 2. 3. 4. Data Mining, Vikram Pudi & P. Radhakrishnan, Oxford University Press (2009). Data Mining, Pieter Adriaans & Dolf Zentinge¸ Addison-Wesley, Pearson (2000). Data Mining Methods & Models, Daniel T. Larose, Wiley-India (2007). Data Mining Techniques, Michael J. A. Berry & Gordon S. Linoff, Wiley-India (2008). 5. Data Mining – a Tutorial-based Primer, Richard J. Roiger & Michael W. Geatz, Pearson Education (2005). 6. Data Mining: Introductory and Advanced Topics, Margaret H. Dunham & S. Sridhar, Pearson Education (2008). 7. Introduction to Data Mining with Case Studies, G. K. Gupta, EEE, PHI (2006). 6. Chapter wise Coverage from the Text Books: Unit 1 Book 1 Topics/Subtopics 1.1, 1.2, 1.3, 1.4, 1.6, 3.1.2,3.2,(3.2.1, 3.2.2, 3.2.3), 3.3, 3.4.1, 3.4.8,3.5.1,3.5.2, 3.5.3, 3.5.4, 3.5.5, 3.5.6 2 1 6.1, 6.2, 6.3, 7.1, 7.6.2 3 2 1 Chapter 6- Datasets for practical’s only 8.1, 8.2,8.3,8.4, 9.2, 9.4, 9.5 2 1 2 1 Chapter 4- 4.3 and 4.4 10.1,10.2.1, 10.2.2, 10.3.1, Chapter 11- 11.6, 11.7,11.8 13.1,13.2, 13.3,13.4 4 5