2020
DOI: 10.1016/j.asoc.2019.105871
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Machine learning integrated credibilistic semi supervised clustering for categorical data

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Cited by 10 publications
(6 citation statements)
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“…In this section, the validation of SKC method is analyzed against various existing techniques such as AFC-NSPSO [14], CrKMd [16] and other popular techniques like Support Vector Machine (SVM) and Naive Bayes (NB). The existing AFC-NSPSO and CrKMd conducted the experiments only on mushroom dataset.…”
Section: Performance Analysis Of Proposed Methods By Means Of Accuracymentioning
confidence: 99%
See 3 more Smart Citations
“…In this section, the validation of SKC method is analyzed against various existing techniques such as AFC-NSPSO [14], CrKMd [16] and other popular techniques like Support Vector Machine (SVM) and Naive Bayes (NB). The existing AFC-NSPSO and CrKMd conducted the experiments only on mushroom dataset.…”
Section: Performance Analysis Of Proposed Methods By Means Of Accuracymentioning
confidence: 99%
“…For connect dataset, the existing techniques namely SVM, NB, AFC-NSPSO and CrKMd achieved only 76% to 79% of accuracy, where proposed SKC achieved 81.47% of accuracy. The existing AFC-NSPSO [14] and CrKMd [16] didn't consider the removal of outliers before clustering process, where SKC removed the outliers that leads high performance on accuracy. This is due to the distance measures used in the SKC method for clustering the data.…”
Section: Performance Analysis Of Proposed Methods By Means Of Accuracymentioning
confidence: 99%
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“…We aim at connecting similar vertices such that the resulting connected components of the graph will be cliques. The problem was given notable attention due to its applications in various fields such as data mining [14,33,23,15], machine learning [27,1,32,9], computational biology [31], and many others.…”
Section: Chapter One Introductionmentioning
confidence: 99%