Data Warehousing and Mining JNTU-K MCA Second Sem

Syllabus

MCA2104

Data Warehousing and Mining

UNIT-I

Introduction to Data Mining, Types of Data, Data Quality, Data Processing, Measures of Similarity and Dissimilarity, Exploring Data: Data Set, Summary Statistics, Visualization, Data Warehouse, OLAP and Multi Dimensional Data Analysis.

UNIT-II

Classification: Basic Concepts, Decision Trees and Model Evaluation: General Approach for Solving a Classification Problem, Decision Tree Induction, Model Over Fitting: Due to Presence of Noise, Due to Lack of Representation Samples, Evaluating the Performance of Classifier. Nearest Neighborhood Classifier, Bayesian Classifier, Support Vector Machines: Linear SVM, Separable and Non Separable Case.

UNIT-III

Association Analysis: Problem Definition, Frequent Item-Set Generation, Rule Generation, Compact Representation of Frequent item Sets, FP-Growth Algorithms. Handling Categorical, Continuous Attributes, Concept Hierarchy, Sequential, Sub Graph Patterns

UNIT-IV

Clustering: Over View, K-Means, Agglomerative Hierarchical Clustering, DBSCAN, Cluster Evaluation: Overview, Unsupervised Cluster Evaluation using Cohesion and Separation, using Proximity Matrix, Scalable Clustering Algorithm

UNIT-V

Web Data Mining: Introduction, Web Terminology and Characteristics, Web Content Mining, Web usage Mining, Web Structure Mining, Search Engines: Characteristics, Functionality, Architecture, Ranking of WebPages, Enterprise Search