<![CDATA[Clustering and classification technologies are pivotal in data analysis, helping to uncover hidden patterns in complex datasets. Despite their broad applications across fields such as pattern recognition, market segmentation, anomaly detection, and weather prediction, these techniques face significant limitations. Clustering methods like K-Means assume known cluster numbers and data distributions, while classification approaches such as K-Nearest Neighbors (KNN) rely heavily on the quality of labeled data. These challenges are particularly pronounced in the context of dynamic weather data, which exhibits high variability and complexity. This research addresses these limitations by integrating probabilistic multi-class labeling with an adaptive K-Means clustering approach. Probabilistic labeling allows data points to belong to multiple classes, reflecting the nuanced nature of overlapping weather conditions. Adaptive K-Means dynamically determines the optimal number of clusters, overcoming traditional constraints. By combining these methods with KNN classification, the proposed approach enhances the accuracy of weather classification. KNN leverages cluster centroids and class probabilities to provide more precise predictions. This approach provides a robust foundation for further research and optimization of adaptive methods applicable to other complex data types. Ultimately, the proposed model contributes significantly to advancing data analysis methods, particularly for dynamic and multi-class datasets like weather data.]]>
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