Optimization Inspired by the Organization of Interiors Applied to Cluster Analysis





Evolutive Computing, Metaheuristics, Combinatorial Optimization, Pattern Recognition


Cluster analysis consists of a procedure capable of establishing, based on a similarity measure, the classification of a collection of objects into disjoint subsets of elements, so that the items included in a group or subset are more similar to each other than objects added to a distinct group. For represent an operation capable of being interpreted as a combinatorial optimization problem, which has as a criterion function to minimize the squared error associated with the established subdivision of items, it observe that the application of approximation algorithms to its resolution has been frequently performed. In this circumstance, this study proposes the  use of a metaheuristic inspired by the composition and decoration of internal environments to cluster analysis, and compares the results obtained by this method with the responses achieved by five other classification techniques. Particulary, a non-parametric statistical evaluation, which compared the subdivisions determined by the method suggested with the subsets established by the K-means clustering algorithm and by meta-heuristics inspired by biological behaviors and physical phenomena, indicated that the proposed strategy it obtned groupings equivalent or more congruent than those determined by the other partitioning methods.


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How to Cite

Araújo Neto, A. S. (2023). Optimization Inspired by the Organization of Interiors Applied to Cluster Analysis. Revista De Informática Teórica E Aplicada, 30(2), 59–74. https://doi.org/10.22456/2175-2745.125288



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