Data Mining Techniques and Applications

The book contains: Concept of Data Mining overview Data Warehouse and OLAP Technology Data Warehouse Architecture Steps for the Design and Construction of Data Warehouses A Three-Tier Data Warehouse Architecture OLAP OLAP queries metadata repository Data Pre-processing – Data Integration and Transformation Data Reduction Data Mining Primitives: What Defines a Data Mining Task? Task-Relevant Data the Kind of knowledge to be Mined KDD. What is Cluster Analysis Types of Data in Cluster Analysis A Categorization of Major Clustering Methods Classical Partitioning Methods: k-Means and k-Medoids Partitioning Methods in Large Databases: From k-Medoids to CLARANS Hierarchical Methods Agglomerative and Divisive Hierarchical Clustering Density-Based Methods Wave Cluster: Clustering Using Wavelet Transformation CLIQUE: Clustering High-Dimensional Space Model-Based Clustering Methods Statistical Approach Neural Network Approach.