Document clustering (also referred to as Text clustering) is closely related to the concept of data clustering. Document clustering is a more specific technique for unsupervised document organization, automatic topic extraction and fast information retrieval or filtering.
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, as is achieved by Enterprise Search engines such as Northern Light and Vivisimo, consumer search engines such as PolyMeta and Helioid, or open source software such as Carrot2.
Example:
FirstGov.gov, the official Web portal for the U.S. government, uses document clustering to automatically organize its search results into categories. For example, if a user submits “immigration”, next to their list of results they will see categories for “Immigration Reform”, “Citizenship and Immigration Services”, “Employment”, “Department of Homeland Security”, and more. Perform Probabilistic Latent Semantic Analysis (PLSA) can also be conducted to perform document clustering.
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.
The application of document clustering can be categorized to two types. The online application is usually constrained by the efficiency compared offline applications.
In general, there are two common algorithms. The first one is the hierarchical based algorithm, which includes single link, complete linkage, group average and ward's method. By aggregating or dividing, documents could be clustered into hierarchical structure, which is suitable for browsing. However, such an algorithm usually suffers from the efficiency problems. The other algorithm is developed with K-means algorithm and its variances. Usually, it shows a better efficiency, but it is less accurate than the hierarchical algorithm.
Other algorithms involve graph based clustering, ontology supported clustering and order sensitive clustering.
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