An analysis framework for clustering algorithm selection with applications to spectroscopy.

Area of Science:

• Machine learning
• Data science
• Computational statistics

Background:

• Cluster analysis is a key unsupervised learning method for identifying patterns in unlabeled data.
• The effectiveness of clustering algorithms varies significantly with data characteristics, necessitating careful selection.
• No single clustering algorithm universally outperforms others across all datasets.

Purpose of the Study:

• To develop and validate a comprehensive framework for selecting appropriate clustering algorithms.
• To address the challenge of algorithm selection by considering user needs, data properties, and potential cluster structures.
• To move beyond algorithm-centric evaluations towards a holistic, application-driven approach.

Main Methods:

• A qualitative approach was used to identify key characteristics for matching algorithms to applications.
• A subset of suitable clustering algorithms was generated based on these characteristics.
• Quantitative cluster validity indices were employed to evaluate the performance of the selected algorithms.
• The framework was validated across diverse datasets, including spectroscopy, genomics, and classic machine learning benchmarks.

Main Results:

• The analysis framework successfully recommended distinct subsets of clustering algorithms for each tested data type.
• Quantitative evaluations confirmed that the recommended algorithms consistently included the top-performing options for each application.
• The framework demonstrated adaptability and effectiveness across varied data complexities and domains.

Conclusions:

• The proposed framework provides a robust method for selecting optimal clustering algorithms tailored to specific analytical tasks.
• This holistic approach enhances the reliability and efficiency of cluster analysis in practical applications.
• The validated framework offers a valuable tool for researchers and practitioners in data science and machine learning.