A New Algorithm for Identifying and Quantifying of Latent Classes
УДК 519.259
Abstract
Processing large amounts of data can be greatly simplified if this data is divided into approximately homogeneous groups. Splitting into such groups is the task of cluster analysis. However, the question of constructing an objective, natural partition into clusters remains open. The paper considers a modern approach to the search for such an objective cluster structure by highlighting the indicator of a common essential part from the set of characteristics that define objects (we call them the forming ones). When this indicator is fixed, the remains of the forming characteristics become independent or close to such. The resulting independent residuals are interpreted as a kind of information noise, and the latent cluster variable, the common fixed part that provides such a transformation, can be a reason for the objective integration of objects into clusters. A new algorithm for the formation of a cluster partition based on the proximity or coincidence of the values of a latent cluster variable with the simultaneous quantification of its values is proposed. The algorithm is based on the targeted search of partitions, the transition from the start one to the partition, more close to the objective. The algorithm proposed in the paper can be easily modified to the case of non-numeric categorized characteristics.
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Copyright (c) 2020 Сергей Вадимович Дронов, Антон Юрьевич Шеларь
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