Get Self-tuned Descriptive Document Clustering Using A Predictive Network, Vol

yes you can do that, try it test it. in machine learning there is no need to justify, you can cross validate and test you model, if adding cluster variable means improved accuracy, use it by all means.

We focus on two kinds of clustering algorithms that have been widely used in document clustering: partitional clustering and agglomerative clustering. Finally, clustering results need to be presented with proper quality evaluation to users.

Clustering can be used to group these search results into a few clusters, each of which taking a specific element of the query. For example, a query of "movie" can restore Web pages combined into categories including reviews, trailers, stars, and theaters.

Descriptive clustering consists of automatically organizing data instances into clusters and generating a descriptive summary for each cluster.

Clustering models are different from predictive models in that the outcome of the process is not guided by a known result, that is, there is no target attribute. Predictive models predict values for a target attribute, and an error rate between the target and predicted values can be calculated to guide model building.

Clustering analysis identifies clusters embedded in the data. A cluster is a collection of data objects that are similar in some sense to one another.

Clustering is a technique widely used for exploring Descriptive Data Mining. A cluster is a collection of objects or rows similar to one another. A good data cluster ensures that the inter-cluster similarity is low and the intra-cluster similarity is high.