I would start off by explaining how a cluster analysis is a type of technique for data analysis that is used to determine groupings or clusters in a set of data (Pu et al., 2022).
The example I would show would be if we took a bag of Skittles and sorted them by color. A cluster analysis would be similar to the Skittles being put into different piles based on their colors. With the data that we have, each of the Skittles will represent a point of data, and the color will represent the data characteristics. When using a k-
means cluster analysis, the goal is to sort the points of data in the ‘k’ groups, where the ‘k’ is a number that we decide on that will look similar to each other (Herman et al., 2022). If the results of the analysis look similar to the examples in the textbook, then this would represent that the data has been sorted out successfully into specific groups. All groups available will represent data points that are similar in characteristics. In order
to support the hypothesis, there will need to be a review regarding how well the data points will fit into their groups. If it is a good fit for the groups, then this would match the
hypothesis correctly showing that there is existence in the groups. So, if we were to sort the Skittles out and there are distinct piles of each of the colors, then our first guess
that the candies could be sorted out by the colors was a correct guess. Herman, E., Zsido, K.-E., & Fenyves, V. (2022). Cluster Analysis with K-Mean versus K-
Medoid in Financial Performance Evaluation. Applied Sciences (2076-3417), 12
(16), N.PAG. https://doi-org.lopes.idm.oclc.org/10.3390/app12167985 Pu, Q., Gan, J., Qiu, L., Duan, J., & Wang, H. (2022). An efficient hybrid approach based on PSO, ABC and k-means for cluster analysis. Multimedia Tools & Applications,
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(14), 19321–19339. https://doi-org.lopes.idm.oclc.org/10.1007/s11042-021-11016-6
