Machine Learning Cluster Analysis for Large Categorical Data Using R Programming
By: Rimal, Yagyanath
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Publisher: New Delhi STM Journals 2018Edition: Vol.6(2), May-Aug.Description: 23-34p.Subject(s): Computer EngineeringOnline resources: Click here
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Recent trends in programming languagesSummary: This review paper clearly discusses the compression between various types of cluster analysis of large categorical data sets. Although there is large gap between the choice of cluster analysis for large data in research design. Its primary purpose is to explain the simplest way of clustering analysis whose data structure were wide scattered using R software whose outputs were sufficiently explain with various intermediate output and graphical interpretation to reach the final conclusion. Therefore, this paper meets the choice of clustering when data sets with large dimensions and its strengths for data analysis of high-dimensional categorical data vectors of unequal length of alignment techniques to equalize its lengths using R programming.
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This review paper clearly discusses the compression between various types of cluster analysis of large categorical data sets. Although there is large gap between the choice of cluster analysis for large data in research design. Its primary purpose is to explain the simplest way of clustering analysis whose data structure were wide scattered using R software whose outputs were sufficiently explain with various intermediate output and graphical interpretation to reach the final conclusion. Therefore, this paper meets the choice of clustering when data sets with large dimensions and its strengths for data analysis of high-dimensional categorical data vectors of unequal length of alignment techniques to equalize its lengths using R programming.
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