A Comparison of K-Means and Mean Shift Algorithms
نویسندگان
چکیده
Clustering, also known as cluster analysis, is a learning problem that occurs without the intervention of human. This technique frequently used very efficiently in data analysis to observe and identify interesting, useful, or desirable patterns data. The clustering operates by dividing involved into similar objects based on their identified properties. process results formation groups, each formed group referred cluster. A single said consists from share similarities with other found same differ now exist clusters. Clustering an important many aspects because it determines presents intrinsic grouping attributes batch unlabeled raw method lacks textbook or, put another way, good criteria. due fact this unique customizable for user who requires variety reasons. There no best algorithm so dependent user's scenario needs. purpose paper compare contrast two different algorithms. algorithms under consideration are k- mean shift. These compared following criteria: time complexity, training, prediction performance, accuracy.
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ژورنال
عنوان ژورنال: International journal of theoretical and applied mathematics
سال: 2021
ISSN: ['2575-5080', '2575-5072']
DOI: https://doi.org/10.11648/j.ijtam.20210705.12