Data Warehousing and Data Mining Chapters

Lifecycle and types of data, data warehousing concepts, operational vs warehouse database, multidimensional model, OLAP operations.

Motivation, DM system, functionalities, KDD process, data types, statistical descriptions, issues in data mining.

Data cleaning, integration, transformation, reduction, discretization, concept hierarchy generation.

Data cube computation methods, cube materialization, full cube, iceberg cube, closed cube, shell cube.

Frequent itemsets, market basket analysis, association rules, single/multi-dimensional and multilevel rules.

Classification vs prediction, learning and testing models, decision trees (ID3), Bayesian classifiers.

Types of data, similarity measures, k-means, k-means++, mini-batch k-means, k-medoids, hierarchical clustering methods.

Graph mining concepts, beam search, inductive logic programming, link mining, social network metrics.

Spatial association, multimedia mining, similarity search, text mining, web mining techniques.