[Retracted] Classification and Prediction of Software Incidents Using Machine Learning Techniques

Table 5

Summary of K-mean clustering.

k-Means
======
Number of iterations: 4
Within cluster sum of squared errors: 186.97500542403395
Initial starting points (random):
Cluster 0: “7/20/2017 23:10”	“App-configuration failed”	7009	None
Cluster 1: “7/18/2017 23:09”	“Need updation”	6008	None
Cluster 2: “7/20/2017 23:34”	“Ending window installer transaction”	37	−7
Missing values globally replaced with mean/mode
Final cluster centroids:
Clusters #
Attribute	Full data	Cluster 0	Cluster 1	Cluster 2
	(100)	(21.0)	(41.0)	(38.0)
Date and time	7/17/2017 12:07	7/21/2017 11:15	7/17/2017 12:07	7/20/2017 23:34
Source	Software protection service failed	App configuration failed	Software protection service failed	Rtop service failed
Event ID	2204.01	6617.4286	1477.0244	549.3947
Task category	None	None	None	−212
Time taken to build the model (full training data): 0.02 seconds