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Recursive-mode K-means clustering for self-organization of Dynamic Imaging data

The aim of this study was to develop a novel algorithmic scheme for self-organizing data, adopting an recursive-mode k-means clustering approach. The proposed methodology attempts to refine and improve the clustering result by sequentially updating centers on the basis of their present and previous positions, exploiting both prior expert knowledge and posterior data information from the statistical distribution of the examined population. The performance of the implemented algorithm is evaluated on Dynamic Imaging data from cervical tissue, examining numerous sample curves representing the temporal response of tissue areas under the aceto-whitening effect. In comparison to the performance of classical k-means approach, the preliminary results of this study indicate the outperformance of the proposed iterative scheme. The primary benefit is attributed to the center improvement strategy against the center replacement methodology enforced in the classical approach. The performance and conceptual integration of knowledge justify the proposed update strategy as an efficient data grouping and classification tool, revealing a proposing potential to tissue evaluation and disease characterization applications.