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# Different phases of projected clustering in data analytics

• Last Updated : 09 Dec, 2020

In this article , we are going to discuss about different phases of projected clustering in data analytics in detail.

Three Phases for Projected Clustering :

1. Initialization Phase
2. Iterative Phase
3. Refinement Phase

These are explained as following below.

1. Initialization Phase :
This phase comprises of two steps to select the superset.

• In the first step, it picks up a random sample data points whose size is proportional to the number of clusters that the user wish to produce which is given as,
`S= random sample size A.k,`

where A is a constant and k represents the number of clusters.

• The second step which uses the greedy method is accomplished to acquire a final set of points B.k,where B is a small constant.

This set is designated as M where hill climbing technique is put in during the next phase.

• Pick up a sample set of data point randomly.
• Pick up a set of data point which is probably the medoids of the cluster.

2. Iterative Phase :
From the initialization phase, we got a set of data points which should hold the medoids. This phase, we will find the best medoids from M. Randomly picks up the set of points M current, and restore the “bad” medoids from other point in M if required by which cluster quality is upgraded. The freshly formed meaningful medoid set is designated as M best.

For the medoids, following will be done as follows.

• Identify dimensions associated to the medoids.
• Allocate data points to the medoids.
• Gauge the clusters formed.
• Identify the poor medoid , and try the result of restoring poor medoid.
• The above procedure is replicate until we got a pleased result.

3. Refinement Phase -Handle Outliers :

• The end step of this algorithm is refinement phase. This phase comprises of better quality of the clusters formed.
• The clusters C1,C2,C3,….,Ck formed during the iterative phase are the feed in to this phase.
• The native data set is passed over one or more times to enhance the quality of the clusters.
• The dimension sets Di found during the iterative phase are dispose of and new dimension sets are calculated for each of the cluster set Ci.
• Once when the new dimensions are calculated for the clusters, then the points are reassigned to the medoids comparative to these new sets of dimensions.
• Outliers are determined in the last pass over the data.

Major Drawback :

• The algorithm requires the average number of dimensions per cluster as framework in input. The performance of projected clustering is highly sensitized to the value of its input framework.
• If the average number of dimensions is erroneously estimated ,the presentation of projected clustering significantly worsens.

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