- Cluster analysis is also known as clustering in Tableau is the process of dividing a data set into segments or clusters having relevant data values.
- Clustering helps us in doing a comparative analysis of data in. In clustering, similar or closely related values are clustered together into different clusters.
- While the values that are not closely related fall into another cluster.
- Clustering is done using specific clustering algorithms where similar values are kept together as a part of the group.
- In Tableau, we can have a cluster of up to seven color shades or codes at a time.
- The clustering algorithm used in Tableau is known as
*K-means clustering* . This algorithm divides a data set into K clusters or segments based on their similarity metrics.
- After this, it calculates the mean (mean of all the values in one cluster) for each cluster which gives the
*Centroid* (cluster center) of a cluster.
- Then by using the centroid value for each cluster present, the values are placed in such a way that the total sum of distances between the centroid and concerning members in a cluster is minimum or as small as possible.
- In this way, the K-means algorithm gives us closely packed clusters each made of closely related or similar values.
- For instance, if we have sales data for a product for different types of consumers or buyers.
- Now, we want to analyze the purchasing capacity of consumers. For this, we can create clusters where we can segregate consumers based on their purchasing capacities.
- With the help of such a cluster, we can come up with strategies to maximize sales depending upon the purchasing or spending capacities of each group.