How does K-means clustering work?
The k-means clustering algorithm attempts to split a given anonymous data set (a set containing no information as to class identity) into a fixed number (k) of clusters. Initially k number of so called centroids are chosen. These centroids are used to train a kNN classifier. ...
Related Posts:
- What is customer clustering? - Segmenting is the process of putting customers... (Read More)
- What is a cluster analysis in marketing? - Cluster analysis is a statistical method used... (Read More)
- How do you analyze cluster analysis? - Two-step clustering can handle scale and ordinal... (Read More)
- What is the purpose of cluster analysis? - The purpose of cluster analysis is to... (Read More)
- What is the goal of cluster analysis? - The goal of cluster analysis is to... (Read More)
- What is purpose of clustering? - Clustering is useful for exploring data. If... (Read More)
- What is clustering and its purpose? - Clustering is the task of dividing the... (Read More)
- What are the types of cluster? - The various types of clustering are:Connectivity-based Clustering... (Read More)
- What is the benefit of clustering data? - The main advantage of a clustered solution... (Read More)
- What is K means clustering explain with an example? - K Means Numerical Example. The basic step... (Read More)