Open AccessAn approach to finding a global optimum in constrained clustering tasks involving the assessments of several experts
Author(s) -
Alexander Zuenko,
AUTHOR_ID,
O. V. Fridman,
Ольга Николаевна Зуенко,
AUTHOR_ID,
AUTHOR_ID
Publication year - 2021
Publication title -
trudy kolʹskogo naučnogo centra ran
Language(s) - English
Resource type - Journals
ISSN - 2307-5252
DOI - 10.37614/2307-5252.2021.5.12.007
Subject(s) - constrained clustering , cluster analysis , heuristics , computer science , constraint (computer aided design) , constraint satisfaction problem , a priori and a posteriori , mathematical optimization , partition (number theory) , constraint satisfaction dual problem , local consistency , fuzzy clustering , data mining , mathematics , machine learning , artificial intelligence , cure data clustering algorithm , philosophy , geometry , epistemology , combinatorics , probabilistic logic
An approach to solving the constrained clustering problem has been developed, based on the aggregation of data obtained as a result of evaluating the characteristics of clustered objects by several independent experts, and the analysis of alternative variants of clustering by constraint programming methods using original heuristics. Objects clusterized are represented as multisets, which makes it possible to use appropriate methods of aggregation of expert opinions. It is proposed to solve the constrained clustering problem as a constraint satisfaction problem. The main attention is paid to the issue of reducing the number and simplifying the constraints of the constraint satisfaction problem at the stage of its formalization. Within the framework of the approach, we have created: a) a method for estimating the optimal value of the objective function by hierarchical clustering of multisets, taking into account a priori constraints of the subject domain, and b) a method for generating additional constraints on the desired solution in the form of “smart tables”, based on the obtained estimate. The approach allows us to find the best partition in the problems of the class under consideration, which are characterized by a high dimension.