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Data Warehouse and Data Mining

A comprehensive textbook that teaches the foundational concepts, architectures, and techniques of data warehousing and data mining and their real-world applications.

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What it’s about

Data Warehouse and Data Mining Concepts is a structured guide that takes readers from the fundamental principles of building centralized data repositories through to the advanced techniques used to extract hidden knowledge from large datasets. Spanning seven chapters, it covers data warehouse architecture (single-, two-, and three-tier), schema design (star, snowflake, fact constellation), ETL processes, OLAP/OLTP distinctions, metadata management, and the full lifecycle of data warehouse implementation. It then transitions into data mining—defining its tasks, query languages (DMQL, MDX, SQL), core techniques (classification, clustering, association rules, decision trees, SVM, fuzzy methods), and the mining of complex data objects such as spatial, multimedia, time-series, text, and web data. Designed for both beginners and seasoned professionals, the book equips readers with the conceptual vocabulary and practical understanding necessary to design robust data infrastructure and derive actionable intelligence.

The through-line

Who it’s for: A student or IT/data professional who wants to understand and build effective data infrastructure and extract meaningful insights from large datasets.
The problem: Organizations are overwhelmed by vast volumes of structured and unstructured data they cannot effectively store, integrate, or analyze for decisions. The reader feels intimidated by the complexity of data warehousing and mining concepts and unsure how to apply them practically.
The plan: Learn the fundamentals of data warehousing—definitions, history, types, and schemas.
Understand data warehouse architecture and the distinction between OLTP and OLAP.
Follow the implementation lifecycle from planning through tuning and testing.
Master data mining tasks, techniques, and query languages.
Apply specialized techniques to mine complex data objects across domains.
The payoff: The reader can design efficient data warehouses tailored to business needs. · The reader can extract actionable intelligence and make data-driven decisions. · The reader gains a competitive edge through robust data management and analytics capabilities.

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