A novel approach for statistical analysis of business intelligent system performance in cloud computing using graph clustering algorithm and hierarchical clustering algorithm based on accuracy and root mean square error (RMSE) value

The purpose of this research was to determine whether or not a graph clustering algorithm could be used to accurately anticipate the Novel approach business intelligence system in cloud computing, as opposed to a hierarchical clustering method. Using a sample size of N=4, a graph clustering method a...

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Bibliographic Details
Main Authors: Mahesh, Gudisa, Parthipan, V.
Format: Conference Proceeding
Language:English
Subjects:
Algorithms
Cloud computing
Cluster analysis
Clustering
Datasets
Information management
Root-mean-square errors
Statistical analysis
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Summary:The purpose of this research was to determine whether or not a graph clustering algorithm could be used to accurately anticipate the Novel approach business intelligence system in cloud computing, as opposed to a hierarchical clustering method. Using a sample size of N=4, a graph clustering method and a hierarchical clustering technique were used to classify a suspicious dataset. Using G power and a pretest power of 80% at p0.05, we determine the required number of subjects in each group. With regards to clustering algorithms, Prewitt scores 96% in the graph clustering category and 86% in the hierarchical clustering category on average. When compared to the traditional hierarchical clustering method, the suggested graph clustering algorithm seems to provide more reliable predictions for the datasets.
ISSN:0094-243X
1551-7616
DOI:10.1063/5.0197499