Strict Standard Normal Cloud Sets C-Means Clustering: A New Approach for Competitive Intelligence Classification of Hydrogen Energy
DOI:
https://doi.org/10.54097/4jm3dh63Keywords:
Competitive Intelligence Classification, C-Means Clustering, Hydrogen Energy Technology, Strict Standard Normal Cloud Sets, Randomness and FuzzinessAbstract
This research first defines strict standard normal cloud sets—a mathematical model integrating randomness and fuzziness to quantify and address dataset uncertainty—and develops the corresponding cloud generation algorithm. This model converts quantitative feature data into cloud sets (characterized by parameters Ex, En, He, β), effectively capturing the uncertain distribution of features. In the classification of hydrogen energy technology competitive intelligence, this model is applied: first extract key features, then use the proposed model to generate cloud sets for each feature. We further derive the iterative formulas of strict standard normal cloud sets C-means clustering, including formulas for updating cluster center cloud parameters and calculating membership degrees. Additionally, we use the feature cloud sets to construct the initial membership degree matrix, and select initial clustering centers based on the membership degree values. Comparative experiments show that this method outperforms traditional fuzzy C-means in handling text data related to intelligence, with higher classification accuracy. It provides a rigorous technical framework for the precise classification of hydrogen energy technology competitive intelligence, supporting decision-making in industrial dynamics analysis.
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