Data Mining: Concepts and Techniques 4th Edition by Jiawei Han, Jian Pei & Hanghang Tong

Buy Data Mining Concepts and Techniques 4th Edition by Han, Jian Pei, and Hanghang Tong from A2Z Book Hub at a student-friendly price in India. Morgan Kaufmann and Elsevier publish this global benchmark textbook, which covers data warehousing OLAP cubes, frequent itemset algorithms, cluster validation, graph mining, and integrated deep learning architectures. In addition, our quality team manually inspects every pre-owned copy before dispatch to ensure clean pages, sturdy binding, and an intact spine. As a result, students receive a verified academic resource that is completely ready for university semester examinations, data science research, and analytics placement tests.

Data Mining Concepts and Techniques 4th Edition by Han Overview

The authors designed Data Mining Concepts and Techniques 4th Edition by Han to establish the definitive foundational methodology for knowledge discovery in massive and heterogeneous databases. Instead of treating data analytics as an ad-hoc collection of heuristics, the textbook details algorithmic convergence proofs, multidimensional OLAP cubing, and statistical preprocessing workflows. Because of this mathematically rigorous yet practical framework, computer science students build strong intuition for discovering actionable intelligence from unstructured data.

This 4th Edition expands significantly to bridge classical pattern mining with modern artificial intelligence, featuring extensive chapters on deep feedforward networks, graph neural networks (GNNs), stream data processing, and high-dimensional outlier detection. Therefore, it remains an indispensable textbook across computing disciplines, especially for B.Tech semester exams, M.Tech data engineering research, and quantitative analytics interviews.

Key Topics Covered in Data Mining Concepts and Techniques 4th Edition by Han

• Knowledge Discovery from Data (KDD) architectures, data cleaning, integration, transformation, and dimensionality reduction.
• Data warehousing concepts, multidimensional data modeling, snowflake schemas, and efficient online analytical processing (OLAP).
• Mining frequent patterns, market basket analysis, Apriori algorithms, FP-growth trees, and correlation analysis.
• Classification algorithms, decision tree induction, rule-based classifiers, Bayesian belief networks, and ensemble methods.
• Cluster analysis, partitioning methods (k-means), hierarchical clustering (BIRCH), density-based models (DBSCAN), and grid systems.
• Outlier and anomaly detection schemes, distance-based approaches, density-based LOF, and high-dimensional challenges.
• Deep learning for data mining, convolutional networks (CNNs), recurrent networks (RNNs), and graph neural networks (GNNs).

What You Will Learn

• Execute rigorous data preprocessing pipelines including normalization, discretization, and feature attribute selection.
• Implement scalable association rule mining algorithms using FP-trees to analyze massive transactional records.
• Construct and evaluate predictive classifiers using cross-validation metrics, confusion matrices, and ROC curves.
• Apply graph neural networks and deep representations to extract latent topological patterns from complex networks.

Key Features of Data Mining Concepts and Techniques 4th Edition by Han

• Integrated Deep Learning Coverage: Incorporates modern neural networks, CNNs, RNNs, and GNNs alongside classic mining algorithms.
• Complex Data Mining Modules: Dedicated treatments for spatiotemporal streams, graph topologies, text corpora, and web data.
• Curriculum Aligned: Perfectly matches Data Mining and Big Data Analytics syllabi across AICTE, IITs, NITs, and central universities.
• Self-Study Friendly: Structured with illustrative pseudocode, worked numerical examples, and end-of-chapter exercises for quick revision.

Who Should Buy Data Mining Concepts and Techniques 4th Edition by Han?

This textbook is particularly recommended for:

• Computer Science Undergraduates: B.Tech and B.E. students taking core Data Mining, Business Intelligence, and Data Warehousing courses.
• Data Science & AI Scholars: BCA, MCA, M.Tech, and M.Sc. Data Analytics candidates seeking algorithmic depth.
• Competitive Exam Aspirants: Candidates preparing for GATE Data Science & AI (DA) papers and technical exams.
• Data Engineers & Analysts: Industry practitioners optimizing large-scale knowledge extraction pipelines and customer clustering models.

About the Authors & Publisher

Prof. Jiawei Han is the Michael Aiken Chair Professor in the Department of Computer Science at the University of Illinois Urbana-Champaign (UIUC) and an ACM/IEEE Fellow recognized globally for pioneering frequent pattern mining and graph discovery. Co-authors Prof. Jian Pei (Duke University) and Prof. Hanghang Tong (UIUC) are prominent leaders in scalable data science and network analysis. In addition, through Morgan Kaufmann and Elsevier, their collaborative work continues to define the worldwide standard for data mining education.

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