Unsupervised Modified Clustering Technique Based on Fuzzy Set Theory to Categorize the Real-Life Data

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R Devi

Abstract

The fuzzy C-means clustering algorithm is appropriate for segmenting datasets and is commonly utilized in real-life settings. The growth of huge data has posed numerous obstacles for clustering approaches. Due to their high density and execution time, traditional clustering approaches cannot be applied for such vast amount of real-world data. To address these challenges, we offer an unsupervised clustering method for automatically categorizing large-scale datasets without requiring labels, with a focus on the real-life dataset. Experiments on real-world datasets show that our suggested unsupervised technique performs well and has high precision. The outcomes reveal that the proposed approach effectively segregate unstructured real-world database into distinct clusters.

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