Optimizing Clustering Algorithms for Large-Scale Data

Objectives to cover:

• Introduction: Importance, challenges, and objectives of clustering in large-scale data analysis.
• Overview of Clustering Techniques: Review of traditional methods and their limitations for big data.
• Innovations in Clustering: Advances in parallel, distributed, and scalable clustering approaches.
• Hybrid Clustering Models: Integration of clustering with machine learning and ensemble techniques.
• Applications in Real-World Scenarios: Use cases in social networks, genomics, and customer segmentation.
• Performance Evaluation: Metrics and benchmarks comparing traditional and optimized methods.
• Case Studies: Examples showcasing improved outcomes using innovative clustering techniques.
• Challenges and Future Directions: Addressing dimensionality, complexity, and trends in AI-driven clustering.
• Conclusion: Summary of findings and future scope for clustering in large-scale data analysis.

Reference:  IEEE style