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Published on: February 15, 2017
Clustering of symbolic objects using gravitational approach
Summary
This study introduces a novel gravitational clustering algorithm for symbolic data, moving beyond traditional hierarchical methods. The approach effectively groups data by simulating gravitational attraction, forming composite objects and reducing samples iteratively.
Area of Science:
- Data Science
- Machine Learning
- Computational Statistics
Background:
- Existing symbolic data clustering predominantly uses hierarchical methods (agglomeration/division).
- A need exists for alternative clustering approaches for symbolic data to enhance analytical capabilities.
Purpose of the Study:
- To propose a novel clustering algorithm for symbolic objects utilizing a gravitational approach.
- To introduce a new methodology for symbolic data analysis inspired by physical phenomena.
Main Methods:
- Developed a multistage clustering scheme based on gravitational attraction between symbolic objects.
- Introduced 'mutual pairs' and utilized 'cluster coglomerate strength' and 'global coglomerate strength' for merging decisions.
- Formed composite symbolic objects during the merging process, reducing the data set iteratively.
Main Results:
- The algorithm successfully clusters symbolic data by simulating particle convergence.
- Demonstrated efficacy across diverse datasets including numeric, fat oil, microcomputers, microprocessors, and botany.
- Comparative analysis showed competitive performance against existing methods.
Conclusions:
- The gravitational approach offers a viable and effective alternative for symbolic data clustering.
- The proposed methodology provides a robust framework for analyzing complex symbolic datasets.
- Further research can explore extensions and applications of this gravitational clustering technique.
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