Development of an Intelligent Hybrid Clustering Algorithm Based on Depth Data and Grid Mapping for Automatic Parameter Estimation
Keywords:
Clustering; K-Means; Hybrid Algorithm; Data Depth; Grid-Mapping; Automatic Parameter EstimationAbstract
The K-Means algorithm is one of the most popular clustering methods; however, it has notable weaknesses, particularly its reliance on an arbitrary selection of the number of clusters (K) and its sensitivity to the initialization of centroid positions. Traditional K-Means often exhibits unstable performance and is vulnerable to noise, while many cluster validity indices (CVIs) used to determine the optimal K also suffer from limitations related to computational complexity and efficiency. Although advanced approaches—such as grid-based clustering and Data Depth–based estimation methods—have been developed to address these issues, many of them still require manual intervention to determine key parameters, which becomes an obstacle in creating a fully autonomous clustering system.
To overcome these challenges, this study proposes the development of an Intelligent Hybrid Clustering Algorithm that integrates Grid-Mapping with the concept of Data Depth for automatic parameter estimation. The Grid-Mapping approach is employed due to its proven speed, stability, and robustness against noise by transforming data into a grid-based representation. Meanwhile, the Data Depth concept is utilized as a foundation for efficiently and accurately estimating the optimal number of clusters (K) without the need for repeated full clustering processes. The main innovation of this research lies in creating an automated mechanism for determining crucial parameters, thus eliminating the need for manual user input. With the integration of these two approaches, the proposed algorithm is expected to offer a clustering solution that is not only accurate and efficient but also more intelligent and adaptive, capable of operating autonomously across various data scenarios.
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