Description

Chapter 1: Data Mining

Data Mining deals with types of data, preprocessing techniques, mining functionalities, system classification, integration with data warehouses, and major issues like scalability and privacy.

Chapter 2: Association Rule Mining

Association Rule Mining focuses on discovering frequent patterns using algorithms like Apriori and FP-Growth, analyzing support and confidence, studying correlations, and mining sequential and graph patterns.

Chapter 3: Classification

Classification involves supervised learning methods such as decision trees, Bayesian classifiers, rule-based classifiers, and lazy learners for prediction and decision-making.

Chapter 4: Clustering and Applications

Clustering groups similar data objects using partitioning, hierarchical, density-based, and grid-based methods, along with identifying outliers and applications.

Chapter 5: Advanced Concepts

Advanced Data Mining Concepts include mining data streams, time-series analysis, sequence pattern mining, and extracting knowledge from spatial, multimedia, text, and web data.

Authors:

Mrs. Palagati Anusha
Dr. B. Santhosh Kumar

—————————————–

Published By: S.P.K Publications