Clustering Algorithms using Splitting Attribute Approach
DOI:
https://doi.org/10.61841/2gd9ej93Keywords:
Rough Set, Fuzzy dominance, MMR, MMeR, SDRAbstract
The computer field is growing rapidly in many areas like artificial intelligence, data mining, cloud computing, and more. Out of those, data mining keenly seeks the attraction of many people due to its applicability. Out of the steps in data mining, clustering is one of the most important steps that faces a lot of problems due to a lack of data. Factors like imprecision, uncertainty, missing value, and more affect the data directly or indirectly. In order to find a solution for the above-mentioned many algorithms are evolving day by day according to the need of the problems. Fuzzy set and rough set are the older techniques that are most often used by many researchers. In the modern era, algorithms like MMR, MMeR, MMeMeR, and SSDR are highly attracted by recent researchers, which gives better results compared to rough and fuzzy. In this paper, the detailed comparison of those algorithms with each other and a few applications related to attribute clustering is briefly explained.
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