Show the implementation of Clustering Algorithm.

Show the implementation of Clustering Algorithm.

The Microsoft Clustering algorithm is a segmentation algorithm provided by Analysis Services. The algorithm uses iterative techniques to group cases in a dataset into clusters that contain similar characteristics. These groupings are useful for exploring data, identifying anomalies in the data, and creating predictions.

Clustering models identify relationships in a dataset that you might not logically derive through casual observation. For example, you can logically discern that people who commute to their jobs by bicycle do not typically live a long distance from where they work. The algorithm, however, can find other characteristics about bicycle commuters that are not as obvious. In the following diagram, cluster A represents data about people who tend to drive to work, while cluster B represents data about people who tend to ride bicycles to work.

The clustering algorithm differs from other data mining algorithms, such as the Microsoft Decision Trees algorithm, in that you do not have to designate a predictable column to be able to build a clustering model. The clustering algorithm trains the model strictly from the relationships that exist in the data and from the clusters that the algorithm identifies.

the Microsoft Clustering algorithm provides two methods for creating clusters and assigning data points to the clusters. The first, the K-means algorithm, is a hard clustering method. This means that a data point can belong to only one cluster, and that a single probability is calculated for the membership of each data point in that cluster. The second method, the Expectation Maximization (EM) method, is a soft clustering method. This means that a data point always belongs to multiple clusters, and that a probability is calculated for each combination of data point and cluster.