Exponential Method for Determining Optimum Number of Clusters in Harmonic Monitoring Data

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Provided by: International Journal of Computer and Electrical Engineering (IJCEE)
Topic: Big Data
Format: PDF
Clustering is an important process for finding and describing a variety of patterns and anomalies in multivariate data through various machine learning techniques and statistical methods. Determination of the optimum number of clusters in data is the main difficulty when applying clustering algorithms. In this paper, an exponential method has been proposed to determine the optimum number of clusters in power quality monitoring data using an algorithm based on the Minimum Message Length (MML) technique.
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