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A Modified Cuckoo Search for High-Dimensional Data using Fast Adaptive K-Means Algorithm

D. Karthika, K. Kalaiselvi

Abstract


High-dimensional structures within various requirements of the contemporary environment define knowledge. On this paper, a concept of prototype subspace clustering of Fast-Adaptive K-mean (FAKM) for adaptive loss function is developed on the approach to delivering a flexible cluster pointer goal. FAKM was recently developed to finish the clustering and feature selection process. The FAKM algorithm, on the other hand, ignores the disintegration of its value. FAKM is a measure of the added value to real-world applications. The main goal of this article is to propose a novel clustering method for the Frobenius norm's optimum consequence. A proposed model is solved using the Modified Cuckoo Search (MCS) optimization method at this step. Because the MCS has certain unique characteristics, such as relaxed operation, constant conjunction reflectivity, and effective computational uprightness, it may be used to aggregate cluster points in the most efficient way possible. The new method was finally put to the test utilizing a variety of benchmark datasets from the University of California Irvine (UCI) repository, as well as sophisticated clustering algorithms. The result measures such as Accuracy (ACC), Normalized Mutual Information (NMI), and Error Rate (ER) are estimated using a variety of three datasets such as Breast, Glass, and WebKB.

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References


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