Syllabus
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Course Code: PC-CS-202 Course Name: Discrete Mathematics |
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| MODULE NO / UNIT | COURSE SYLLABUS CONTENTS OF MODULE | NOTES |
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| 1 | Basics of Data Mining Need for data mining, Data Mining as the Evolution of Information Technology, Data mining as a step in the process of knowledge discovery, Transactional Database, Major issues in data mining, Data Preprocessing, Data cleaning, Data integration, Data reduction, Data transformation, Data Warehousing and Online Analytical Processing (OLAP). |
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| 2 | Mining Frequent Itemsets with Associations and Correlations Data cube technology, Multidimensional data mining, Multidimensional data analysis, Mining Frequent Patterns, Associations, and Correlations : Basic Concepts and Methods, Market Basket Analysis Example with rule of Support and Confidence, Frequent Itemsets, Closed Itemsets, and association Rules, Frequent Itemset Mining Methods – Apriori Algorithm. |
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| 3 | Classification Methods and Cluster Analysis Advanced pattern mining, Mining multilevel patterns, multidimensional patterns, Classification : Basic Concepts, Decision Tree Induction, Naïve Bayesian Classification Methods, Rule-Based Classification, Cluster Analysis : Basic Concepts and Methods, Partitioning Methods, Hierarchical Methods, Density-Based Methods, Grid-Based Methods. |
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| 4 | Data Mining Trends Mining Spatial Data, Mining Spatiotemporal Data, Mining Multimedia Data, Mining Text Data, Mining Web Data, Statistical Data Mining, Data Mining Applications – Data Mining for Financial Data Analysis, Intrusion Detection and Prevention, Retail and Telecommunication Industries, Science and Engineering, Privacy, Security and Social Impacts of Data Mining, Data Mining Trends. |
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