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Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some specific sense defined by the analyst) to each other than to those in other groups (clusters). It is a main task of exploratory data analysis, and a common technique for statistical ...
Cluster criticism is a method of rhetorical criticism in which a critic examines the structural relations and associative meanings between certain main ideas, concepts, or subjects present in a text. Method [ edit ]
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.
Prewriting is the first stage of the writing process, typically followed by drafting, revision, editing and publishing. [1][2][3] Prewriting can consist of a combination of outlining, diagramming, storyboarding, and clustering (for a technique similar to clustering, see mindmapping).
Cluster development (or cluster initiative or economic clustering) is the economic development of business clusters. The cluster concept has rapidly attracted attention from governments, consultants , and academics since it was first proposed in 1990 by Michael Porter .
Clustering high-dimensional data is the cluster analysis of data with anywhere from a few dozen to many thousands of dimensions.Such high-dimensional spaces of data are often encountered in areas such as medicine, where DNA microarray technology can produce many measurements at once, and the clustering of text documents, where, if a word-frequency vector is used, the number of dimensions ...
Biclustering, block clustering, [1] [2] Co-clustering or two-mode clustering [3] [4] [5] is a data mining technique which allows simultaneous clustering of the rows and columns of a matrix. The term was first introduced by Boris Mirkin [ 6 ] to name a technique introduced many years earlier, [ 6 ] in 1972, by John A. Hartigan .
Introduction. In natural language processing, Brown clustering [3] or IBM clustering [4] is a form of hierarchical clustering of words based on the contexts in which they occur, proposed by Peter Brown, William A. Brown, Vincent Della Pietra, Peter de Souza, Jennifer Lai, and Robert Mercer of IBM in the context of language modeling. [1]