Mca535 data mining and data warehousing
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This document outlines the syllabus for an MCA535 Data Mining and Data Warehousing course. The course is divided into 5 units that cover topics such as data preprocessing, data warehousing and OLAP technology, frequent pattern mining, classification and prediction, cluster analysis, and applications of data mining. The first unit introduces data mining concepts and the integration of data mining systems with databases. Later units cover efficient methods for data cube computation, mining frequent itemsets and rules, classification techniques like decision trees and Bayesian classification, clustering methods, and mining time-series and spatial data. The syllabus concludes with applications and trends in data mining.
Mca535 data mining and data warehousing
- 1. MCA535 Data Mining and Data Warehousing Total teaching Hours/Semester: 60 No of Lecture Hours/Week: 04 Unit I. (12) Introduction What Is Data Mining? Data Mining—On What Kind of Data? Data Mining Functionalities, Classification of Data Mining Systems, Data Mining Task Primitives, Integration of a Data Mining System with a Database or DataWarehouse System, Major Issues in Data Mining. Data Preprocessing Why Preprocess the Data? Descriptive Data Summarization – Measuring the central tendency- Measuring the dispersion of data. Unit II. (12) Data Preprocessing (cont.) Data Cleaning-Missing Values-Noisy Data-Data Cleaning as a Process, Data Integration and Transformation, Data Reduction-Data Cube Aggregation-Attribute Subset Selection- Dimensionality Reduction-Numerosity Reduction. DataWarehouse and OLAP Technology What Is a DataWarehouse? A Multidimensional Data Model, DataWarehouse Architecture, DataWarehouse Implementation, From DataWarehousing to Data Mining. Unit III. (12) Data Cube Computation and Data Generalization Efficient Methods for Data Cube Computation – Road map - Multiway array aggregation – Star cubing, Further Development of Data Cube and OLAP Technology. Mining Frequent Patterns and Associations Basic Concepts, Efficient and Scalable Frequent Itemset Mining Methods – Apriori algorithm, Generating Rules – Improving efficiency – Mining frequent itemset without candidate generation. Unit IV. (14) Classification and Prediction What Is Classification? What Is Prediction? Issues Regarding Classification and Prediction, Classification by Decision Tree – Decision tree induction – Attribute selection, Bayesian Classification – Bayesian Theorem - naïve Bayesian, Rule-Based Classification, Prediction, Accuracy and Error Measures. Cluster Analysis What Is Cluster Analysis? Types of Data in Cluster Analysis, A Categorization of Major Clustering Methods, Partitioning Methods – K-Means and K-Medoids, Hierarchical Methods – Agglomerative and Divisive, Density Based Methods - DBSCAN, Outlier Analysis – Statistical based. Syllabus 2009 MCA 84 Christ University, Bangalore, India Unit V. (10) Mining Time-Series and Spatial Data Mining Time-Series Data – Trend analysis – Similarity search, Spatial Data Mining- Spatial Data Cube Construction and Spatial OLAP-Mining Spatial Association and Colocation Patterns-Spatial Clustering, Classification Methods-Mining Raster Databases Applications and Trends in Data Mining
- 2. Data Mining Applications, Data Mining System Products and Research Prototypes, Social Impacts of Data Mining. Text Book: 1. Jiawei Han and Micheline Kamber, Data Mining: Concepts and Techniques, Morgan Kaufmann Publishers, San Francisco, USA, 2nd edition, 2006. Reference Books: 1. Claudia Imhoff, Nicholas & et al, Mastering Data warehouse Design, J Wiley, 2003 2. Berson A & Smith S J, Data warehousing, Data Mining & OLAP, Mc Graw Hall, 1997. 3. Margaret H. Dunham, Data mining-Introductory and Advanced topics Pearson Education, 2003 4. Inmon W H, Building the Data Warehouse, John Wiley & Sons, 3rd edition, 2005