Divisive-agglomerative algorithm and complexity of automatic classification problems

TL;DR

This paper presents a divisive-agglomerative algorithm for automatic classification, addressing the problem of partitioning data based on pattern matrices or similarity measures, and analyzes its complexity.

Contribution

It introduces a novel divisive-agglomerative algorithm for automatic classification and examines its computational complexity.

Findings

Algorithm effectively partitions data based on similarity matrices.

Complexity analysis provides insights into the algorithm's efficiency.

Demonstrates applicability to various classification problems.

Abstract

An algorithm of solution of the Automatic Classification (AC for brevity) problem is set forth in the paper. In the AC problem, it is required to find one or several artitions, starting with the given pattern matrix or dissimilarity, similarity matrix.