Description

Clustering is a division of data into groups of similar objects. Each group, called cluster, consists of objects that are similar between themselves and dissimilar to objects of other groups. In other words, the goal of a good document clustering scheme is to minimize intra-cluster distances between documents, while maximizing inter-cluster distances (using an appropriate distance measure between documents). A distance measure (or, dually, similarity measure) thus lies at the heart of document clustering. Clustering is the most common form of unsupervised learning and this is the major difference between clustering and classification. No super-vision means that there is no human expert who has assigned documents to classes. In clustering, it is the distribution and makeup of the data that will determine cluster membership. Clustering is sometimes erroneously referred to as automatic classification; however, this is inaccurate, since the clusters found are not known prior to processing whereas in case of classification the classes are pre-defined. In clustering, it is the distribution and the nature of data that will determine cluster membership, in opposition to the classification where the classifier learns the association between objects and classes from a so called training set, i.e. a set of data correctly labeled by hand, and then replicates the learnt behavior on unlabeled data.