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Some new indexes of cluster validity.
1Div. of Comput. Sci., Univ. of West Florida, Pensacola, FL.
Summary
This study enhances Dunn's index for cluster validity, improving its robustness against noisy data. Generalized versions of Dunn's index offer superior validation for cloud-like clusters in data mining.
Area of Science:
- Computer Science
- Data Mining
- Machine Learning
Background:
- Clustering algorithms like hard c-means and single linkage are widely used.
- Assessing the quality of clusters using validity indexes is crucial.
- Existing indexes, such as Dunn's index, have limitations in handling noisy data.
Purpose of the Study:
- To identify deficiencies in Dunn's index for cluster validity.
- To propose and evaluate generalized versions of Dunn's index.
- To compare the effectiveness of different cluster validation strategies.
Main Methods:
- Reviewed hard c-means and single linkage clustering algorithms.
- Evaluated Hubert's statistics, Davies-Bouldin index, and Dunn's index.
- Developed and tested several generalizations of Dunn's index.
- Conducted numerical simulations with varying cluster characteristics.
Main Results:
- Dunn's index is overly sensitive to noisy clusters and outliers.
- The minimum interset distance is an unreliable measure for volumetric clouds.
- Intercluster separation is more critical for cluster validation than cluster diameter.
- Five generalized Dunn's indexes demonstrated superior performance in simulations.
Conclusions:
- Dunn's index, despite flaws, offers a valuable framework for validating cloud-like clusters.
- Generalized Dunn's indexes provide more robust and reliable cluster validation.
- The findings guide the selection of appropriate cluster validation metrics for specific data structures.
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