K-means Clustering with Tableau – Call Detail Records Example

In this blog, we will discuss about clustering of customer activities for 24 hours by using K-means clustering feature in Tableau 10. This type of clustering helps you create statistically-based segments that provide insights about similarities in different groups and performance of the groups when compared to each other. You can use clustering on any type of visualization ranging from scatter plots to text tables and even maps. In our previous blog post – "Call Detail Record Analysis – K-means Clustering with R", we have discussed about CDR analysis using unsupervised K-means clustering algorithm. A daily activity file from Dandelion API is used as a data source, where the file contains CDR records generated by the Telecom Italia cellular network over the city of Milano.