Abstract: This paper aim is to explore the centroid estimation analysis and distance measure variations from the previous methods of clustering and data mining techniques. This paper discusses the study based on these literatures so that methodological exploration may be possible. It is helpful in finding the advantages and disadvantages. Based on the gap identification new insights for the future development have been highlighted. This computation analysis also provides us the parametric exploration of the k-means clustering algorithm for the betterment in the efficiency of clustering.
Keywords: Software Metrics, Object Oriented Programming, Parameters, Quality Estimation
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DOI:
10.17148/IJARCCE.2018.789
