Table 1 Summary of studies used... | Emerald Publishing

Table 1

Summary of studies used clustering as feature selection

Research	How clustering was used	Contribution	Data set
Guru et al. (2019) [14]	TCR method to lower dimensionality	SVM classifier performed better	Reuters dataset
Chormunge and Jena (2018) [15]	Eliminate irrelevant features using feature clustering and cross correlation	NB accuracy improved	12 data sets of microarrays and texts
Malji et al. (2017) [16]	Improve processing time of feature selection	NB accuracy improved and less processing time	Two textual data sets
Nguyen et al. (2016) [13]	Remove irrelevant features using hybrid filter and clustering approach		Two news and medicine data set
Sheydaei et al. (2015) [17]	Cluster high-frequency keywords based on class labels	Better performance of multiple classification algorithms	Publicly available texts
Yang et al. (2014) [18]	Use the deviation of features from centroids as feature selection approach	Multiple classifiers showed better performance	Reuters, newsgroup, and webkb

Research	How clustering was used	Contribution	Data set
Guru et al. (2019) [14]	TCR method to lower dimensionality	SVM classifier performed better	Reuters dataset
Chormunge and Jena (2018) [15]	Eliminate irrelevant features using feature clustering and cross correlation	NB accuracy improved	12 data sets of microarrays and texts
Malji et al. (2017) [16]	Improve processing time of feature selection	NB accuracy improved and less processing time	Two textual data sets
Nguyen et al. (2016) [13]	Remove irrelevant features using hybrid filter and clustering approach		Two news and medicine data set
Sheydaei et al. (2015) [17]	Cluster high-frequency keywords based on class labels	Better performance of multiple classification algorithms	Publicly available texts
Yang et al. (2014) [18]	Use the deviation of features from centroids as feature selection approach	Multiple classifiers showed better performance	Reuters, newsgroup, and webkb