Analyzing the Impact of Feature Selection Methods on Clustering Algorithm Performance in Data Science

1. Introduction 1.1 Background and Motivation 1.2 Objectives of the Study 1.3 Structure of the Paper 2. Data Science and Clustering Algorithms 2.1 Overview of Data Science 2.2 Clustering Algorithms: An Introduction 2.3 Importance of Feature Selection 3. Feature Selection Methods 3.1 Filter Methods 3.2 Wrapper Methods 3.3 Embedded Methods 4. Impact of Feature Selection on Clustering 4.1 Accuracy and Precision 4.2 Computational Efficiency 4.3 Scalability 5. Comparative Analysis 5.1 Methodology of Comparison 5.2 Datasets and Parameters 5.3 Evaluation Metrics 6. Results and Discussion 6.1 Performance Improvement Observations 6.2 Challenges and Limitations 6.3 Implications for Data Science 7. Case Studies 7.1 Healthcare Data Analysis 7.2 Marketing Segmentation 7.3 Real-time Analytics 8. Conclusion 8.1 Summary of Findings 8.2 Future Research Directions 8.3 Final Remarks