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- W225390327 abstract "The analyst faces a perplexing problem when he is forced to choose an association measure and a clustering method for an analysis. The multitude of alternatives makes it difficult to say that a particular measure and a specific method are clearly superior selections for treating the problem at hand. Theoretical and conceptual considerations help to guide choices but are not quite sufficient because they give little insight into the performance characteristics of the alternatives when applied to real data. Nominally, every association measure or clustering method is different from every other one; but a small amount of experience with a variety of methods reveals a massive redundancy in the clustering results obtained. On any given problem, a large group of different methods will give substantially the same results while perhaps a few other methods give distinctively different results; these apparent overlaps and distinctions among methods may exhibit a radically different pattern on other problems. It often appears that most methods react to some central characteristics of the problem while a few methods detect special characteristics and produce distinctive results. Theoretical considerations, to the extent that they are known and understood, are insufficient to explain the manner in which the results of various clustering methods coincide or differ from one data set to another, yet some insight into this aspect of the various methods seems essential for making wise choices in the application of cluster analysis." @default.
- W225390327 created "2016-06-24" @default.
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- W225390327 date "1973-01-01" @default.
- W225390327 modified "2023-09-27" @default.
- W225390327 title "COMPARATIVE EVALUATION OF CLUSTER ANALYSIS METHODS" @default.
- W225390327 doi "https://doi.org/10.1016/b978-0-12-057650-0.50016-8" @default.
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