Enhancing Data Streaming Clustering Algorithms for AutoML in Cloud Environments: A Novel Design Approach
Received: 23 August 2024 | Revised: 9 November 2024 | Accepted: 20 November 2024 | Online: 4 December 2024
Corresponding author: Madhuri H. Parekh
Abstract
The objective of this revision is to enhance existing AutoCloud clustering technology, which demonstrates optimal performance when dealing with clusters of specific dimensions and arrangements. AutoCloud uses the TEDA framework to break down the clustering challenge into two smaller problems, called micro cluster and macro cluster. AutoCloud is an innovative method that eliminates the requirement for any pre-existing understanding of datasets, where clusters can develop and combine when new information and explanations are presented. This study proposes an experimental configuration to generate microclusters and data clouds without imposing a certain topology on static datasets. MLAutoCloud uses a modified distance-based technique, utilizing the big data framework and incorporating the adjusted random index value with the TEDA framework for streaming data. The MLAutoCloud technique yielded optimal cluster numbers and achieved excellent data collection results, as seen in the test results on different datasets. Estimating thickness despite changes in the underlying assumptions is a process that could modify the variables used to provide data. The MLAutoCloud method is an effective way to generate a cloud clustering algorithm in the data streaming section.
Keywords:
AutoCloud, clustering, data streams, big data, machine learning, spark, eccentricity, outliers, anomalyDownloads
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