Validating cluster structures in data mining tasks
Cluster is a group of objects that belongs to the same class.
In other words, similar objects are grouped in one cluster and dissimilar objects are grouped in another cluster.
In this, we start with all of the objects in the same cluster.
In the continuous iteration, a cluster is split up into smaller clusters.
We can classify hierarchical methods on the basis of how the hierarchical decomposition is formed.
There are two approaches here − This approach is also known as the bottom-up approach.
That is, clusters of objects are formed so that objects within a cluster have high similarity in comparison to one another, but are very dissimilar to objects in other clusters.
A database may contain data objects that do not comply with the general behavior or model of the data. Most data mining methods discard outliers as noise or exceptions.
It keeps on merging the objects or groups that are close to one another.Clustering is the process of making a group of abstract objects into classes of similar objects.Points to Remember Suppose we are given a database of ‘n’ objects and the partitioning method constructs ‘k’ partition of data. It means that it will classify the data into k groups, which satisfy the following requirements − This method creates a hierarchical decomposition of the given set of data objects.Predictive mining tasks perform inference on the current data in order to make predictions. For example, in the Electronics store, classes of items for sale include computers and printers, and concepts of customers include big Spenders and budget Spenders.Data characterization is a summarization of the general characteristics or features of a target class of data.