K-means clustering
Clustering is the task of grouping observations in such a way that members of the same cluster are more similar to each other and members of different clusters are very different from each other.
Clustering is commonly used to explore a dataset to either identify the underlying patterns in it or to create a group of characteristics. In the case of social networks, they can be clustered to identify communities and to suggest missing connections between people. Here are a few examples:
- In anti-money laundering measures, suspicious activities and individuals can be identified using anomaly detection
- In biology, clustering is used to find groups of genes with similar expression patterns
- In marketing analytics, clustering is used to find segments of similar customers so that different marketing strategies can be applied to different customer segments accordingly
The k-means clustering algorithm is an iterative process of moving the centers of clusters or centroids to the mean position...
