A Density Peak Clustering Algorithm Based on Information Bottleneck
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초록
Although density peak clustering can often easily yield excellent results, there is still room for improvementwhen dealing with complex, high-dimensional datasets. One of the main limitations of this algorithm is itsreliance on geometric distance as the sole similarity measurement. To address this limitation, we drawinspiration from the information bottleneck theory, and propose a novel density peak clustering algorithm thatincorporates this theory as a similarity measure. Specifically, our algorithm utilizes the joint probabilitydistribution between data objects and feature information, and employs the loss of mutual information as themeasurement standard. This approach not only eliminates the potential for subjective error in selectingsimilarity method, but also enhances performance on datasets with multiple centers and high dimensionality. To evaluate the effectiveness of our algorithm, we conducted experiments using ten carefully selected datasetsand compared the results with three other algorithms. The experimental results demonstrate that our informationbottleneck-based density peaks clustering (IBDPC) algorithm consistently achieves high levels of accuracy,highlighting its potential as a valuable tool for data clustering tasks.
Abstract
Although density peak clustering can often easily yield excellent results, there is still room for improvementwhen dealing with complex, high-dimensional datasets. One of the main limitations of this algorithm is itsreliance on geometric distance as the sole similarity measurement. To address this limitation, we drawinspiration from the information bottleneck theory, and propose a novel density peak clustering algorithm thatincorporates this theory as a similarity measure. Specifically, our algorithm utilizes the joint probabilitydistribution between data objects and feature information, and employs the loss of mutual information as themeasurement standard. This approach not only eliminates the potential for subjective error in selectingsimilarity method, but also enhances performance on datasets with multiple centers and high dimensionality. To evaluate the effectiveness of our algorithm, we conducted experiments using ten carefully selected datasetsand compared the results with three other algorithms. The experimental results demonstrate that our informationbottleneck-based density peaks clustering (IBDPC) algorithm consistently achieves high levels of accuracy,highlighting its potential as a valuable tool for data clustering tasks.
참고문헌 (14)
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