Advanced Clustering Algorithms for Large-scale Data

Objectives to cover:

• Introduction: Overview of clustering algorithms and their importance in analyzing large-scale data.
• The challenges of scalability and efficiency in traditional clustering methods.
• Exploration of distributed and parallel computing techniques for clustering.
• Role of dimensionality reduction in improving clustering performance.
• Adoption of approximate algorithms for faster clustering on big data.
• Integration of machine learning with clustering for adaptive data processing.
• Case studies showcasing successful clustering in large-scale applications.
• Evaluation metrics to assess clustering quality and scalability.
• Conclusion: Future directions and the potential impact of advanced clustering on big data analytics.

Reference:  IEEE style