Large Scale Parallel Data Mining 1759 Lecture Notes in Computer Science 1st edition by Mohammed Zaki, Ching Tien Ho ISBN 3540671943 978-3540671947

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ISBN 10:  3540671943
ISBN 13: 978-3540671947
Author: Mohammed Zaki, Ching Tien Ho 

Withthe unprecedented rate at which data is being collected today in almostall elds of human endeavor, there is an emerging economic and scientic need to extract useful information from it. For example, many companies already have data-warehouses inthe terabyte range (e.g., FedEx, Walmart).The WorldWide Web has an estimated 800 millionweb-pages. Similarly,scienti c data is rea- ing gigantic proportions (e.g., NASA space missions, Human Genome Project). High-performance, scalable, parallel, and distributed computing is crucial for ensuring system scalabilityand interactivityas datasets continue to grow in size and complexity. Toaddress thisneedweorganizedtheworkshoponLarge-ScaleParallelKDD Systems, which was held in conjunction with the 5th ACM SIGKDD Inter- tional Conference on Knowledge Discovery and Data Mining, on August 15th, 1999, San Diego, California. The goal of this workshop was to bring researchers and practitioners together in a setting where they could discuss the design, – plementation,anddeploymentoflarge-scaleparallelknowledgediscovery (PKD) systems, which can manipulate data taken from very large enterprise or sci- tic databases, regardless of whether the data is located centrally or is globally distributed. Relevant topics identie d for the workshop included: { How to develop a rapid-response, scalable, and parallel knowledge discovery system that supports global organizations with terabytes of data.

Large-Scale Parallel Data Mining: 1759 (Lecture Notes in Computer Science) 1st Table of contents:

reface

• Introduction to Large-Scale Data Mining
• Overview of Parallel Computing in Data Mining
• Scope and Objectives of the Book
• Structure of the Volume

Chapter 1: Introduction to Data Mining

• Definition and Concepts of Data Mining
• Key Techniques in Data Mining (Classification, Clustering, Association)
• Challenges in Large-Scale Data Mining
• The Role of Parallelism in Modern Data Mining

Chapter 2: Fundamentals of Parallel Computing

• Introduction to Parallel Computing and Architectures
• Parallel Processing Models: Shared Memory vs. Distributed Memory
• Performance Considerations: Speedup, Scalability, Efficiency
• Parallel Programming Paradigms (MPI, OpenMP, etc.)

Chapter 3: Parallel Algorithms for Data Mining

• Parallelization of Common Data Mining Algorithms
• Parallel Decision Trees
• Parallel Clustering Algorithms
• Parallel Association Rule Mining
• Performance Metrics for Parallel Data Mining Algorithms

Chapter 4: Parallel Data Mining Architectures and Frameworks

• Distributed Computing Frameworks (Hadoop, Spark)
• High-Performance Computing (HPC) Clusters
• Cloud Computing for Large-Scale Data Mining
• Architectures for Scalability and Fault Tolerance in Data Mining

Chapter 5: Large-Scale Data Mining Applications

• Data Mining in Big Data Environments
• Real-Time Data Mining and Stream Mining
• Mining Data from Social Media and Web Data
• Applications in Bioinformatics, Finance, and E-Commerce

Chapter 6: Optimizing Performance in Parallel Data Mining

• Load Balancing and Data Partitioning
• Reducing Communication Overhead in Parallel Systems
• Memory Management and I/O Optimization
• Advanced Techniques for Speeding Up Parallel Data Mining

Chapter 7: Case Studies in Large-Scale Parallel Data Mining

• Case Study 1: Parallel Data Mining for Healthcare Data
• Case Study 2: Parallel Mining in Financial Data Analysis
• Case Study 3: E-commerce Recommendation Systems using Parallel Data Mining

Chapter 8: Emerging Trends and Future Directions

• Machine Learning and Deep Learning in Parallel Data Mining
• Artificial Intelligence Integration with Data Mining
• Advances in Distributed and Cloud Computing for Data Mining
• Ethical and Privacy Concerns in Large-Scale Data Mining

Appendices

• Tools and Frameworks for Parallel Data Mining
• Mathematical Foundations and Algorithms
• Glossary of Key Terms
• Further Reading and Resources

Index

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