Document clustering

Document clustering (or text clustering) is the application of cluster analysis to textual documents. It has applications in automatic document organization, topic extraction and fast information retrieval or filtering.Document clustering involves the use of descriptors and descriptor extraction. Descriptors are sets of words that describe the contents within the cluster. Document clustering is generally considered to be a centralized process. Examples of document clustering include web document clustering for search users.A web search engine often returns thousands of pages in response to a broad query, making it difficult for users to browse or to identify relevant information. Clustering methods can be used to automatically group the retrieved documents into a list of meaningful categories.In practice, document clustering often takes the following steps:Clustering algorithms in computational text analysis groups documents into grouping a set of text what are called subsets or clusters where the algorithm's goal is to create internally coherent clusters that are distinct from one another. Classification on the other hand, is a form of supervised learning where the features of the documents are used to predict the 'type' of documents.