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- W2912297594 abstract "Given a population of individuals described by a set of attribute variables, clustering them into “similar” groups has many applications. The clustering problem, also known as unsupervised learning, is the problem of partitioning a population into clusters (or classes). The population is a set of n elements that can be clients, products, shops, agencies, etc., described by m attributes. These attributes can be quantitative (salary), categorical (type of profession) or binary (owner of a credit card). The goal is to construct a partition in which elements of a cluster are “similar” and elements of different clusters are “dissimilar” in terms of the m attributes. Here we define the clustering problem and discuss the ideas behind some of the major approaches, including a relatively new method, called RDA/AREVOMS, that is based on the theory of voting." @default.
- W2912297594 created "2019-02-21" @default.
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- W2912297594 date "1997-11-01" @default.
- W2912297594 modified "2023-09-25" @default.
- W2912297594 title "Clustering techniques" @default.
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- W2912297594 doi "https://doi.org/10.1016/s0167-739x(97)00017-4" @default.
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