Cluster analysis
task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters)
Cluster analysis or clustering is a way of comparing data by splitting it into groups of similar data points. These groups are called clusters.
There are many algorithms to put data into clusters. Clustering algorithms can use different ways of measuring similarity between data points.[1] As a result, different clustering algorithms can get different clusters on the same data.
References
change- ↑ Estivill-Castro, Vladimir (June 2002). "Why so many clustering algorithms: a position paper". ACM SIGKDD Explorations Newsletter. 4 (1): 65–75. doi:10.1145/568574.568575.
Further reading
change- Ezugwu, Absalom E.; Ikotun, Abiodun M.; Oyelade, Olaide O.; Abualigah, Laith; Agushaka, Jeffery O.; Eke, Christopher I.; Akinyelu, Andronicus A. (1 April 2022). "A comprehensive survey of clustering algorithms: State-of-the-art machine learning applications, taxonomy, challenges, and future research prospects". Engineering Applications of Artificial Intelligence. 110: 104743. doi:10.1016/j.engappai.2022.104743.
