Algorithms for Clustering Data

Algorithms for Clustering Data

English | 1988 | ISBN: 013022278X | 304 Pages | DJVU | 6.9 MB

The practice of classifying objects according to perceived similarities is the basis for much of science. Organizing data into sensible groupings is one of the most fundamental modes of understanding and learning. Cluster analysis is the formal study of algorithms and methods for grouping, or classifying, objects. An object is described either by a set of measurements or by relationships between the object and other objects. Cluster analysis does not use category labels that tag objects with prior identifiers. The absence of category labels distinguishes cluster analysis from discriminant analysis (and pattern recognition and decision analysis). The objective of cluster analysis is simply to find a convenient and valid organization of the data, not to establish rules for separating future data into categories. Clustering algorithms are geared toward finding structure in the data.

Cluster analysis is an important technique in the rapidly growing field known as exploratory data analysis and is being applied in a variety of engineering and scientific disciplines such as biology, psychology, medicine, marketing, computer vision, and remote sensing. Cluster analysis organizes data by abstracting underlying structure either as a grouping of individuals or as a hierarchy of groups. The representation can then be investigated to see if the data group according to preconceived ideas or to suggest new experiments. Cluster analysis is a tool for xploring the structure of the data that does not require the assumptions common to most statistical methods. It is called "unsupervised learning'' in the literature of pattern
recognition and artificial intelligence.


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