Importance of CURE Clustering Algorithm over K-Means Clustering Algorithm for Large Data-set

The method of finding natural groupings within multidimensional data based on some similarity metrics is known as data clustering. There are many clustering algorithms proposed over time, and many of them have gained considerable popularity due to the ease of use and efficiency of such algorithms. A...

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Veröffentlicht in:2023 Third International Conference on Secure Cyber Computing and Communication (ICSCCC) S. 421 - 426
Hauptverfasser: Bharadwaj, Prakhar, Gupta, Ragesh, Gurjar, Ravi, Singh, Anurag
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 26.05.2023
Schlagworte:
Clustering
Clustering algorithms
data analysis
Filtering algorithms
Geometry
large data set
Measurement
Partitioning algorithms
Shape
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Zusammenfassung:The method of finding natural groupings within multidimensional data based on some similarity metrics is known as data clustering. There are many clustering algorithms proposed over time, and many of them have gained considerable popularity due to the ease of use and efficiency of such algorithms. Although most of the algorithms perform well in many use cases, some are limited to specific types of data distributions. This paper aims to demonstrate the importance of Cure algorithm over the K-Means Algorithm with the help of a specified performance metric for large data sets.
DOI:10.1109/ICSCCC58608.2023.10177015