TAXONOMIC EVIDENCE OF CLASSIFICATION APPLYING INTELLIGENT DATA MINING. GALACTIC AND GLOBULAR CLUSTERS
Gregorio Perichinsky, Arturo Carlos Servetto, Elizabeth M. Jimenez, Félix Anibal Vallejos, R. B. Orellana · 2007
Taxonomy aims to group in families, using so-called structure analysis of operational taxonomic units (OTUs or taxons or taxa). Clusters that constitute families with a new approach, is the purpose of this paper that belong to a series of papers, of this kind, of the authors. In this case of use the objects are stars instead of other celestial bodies, as asteroids or minor planets. But the data of the observed field in galactic and globular clusters, the taxonomic distances are distorted, because of their projection in the celestial sphere, for that reason, using trigonometrical functions the taxonomic distances should be transformed. The original algorithm, that must be modified, is conformed by: Structural analysis, that shows the relationships, in terms of degrees of similarity, through the computation of the Matrix of Similarity, applying the technique of integration dynamic of independent domains, of the semantics of the Dynamic Relational Database Model. The main contribution is to introduce the concept of spectrum of the OTUs, based in the states of their characters. The concept of families' spectra emerges, if the principles of superposition and interference, and the Invariants determined by the maximum of the Bienayme-Tchebycheff relation, are applied to the spectra of the OTUs. Through Intelligent Data Mining, we focused our interest on the Quinlan algorithms, applied in classification problems with the Gain of Entropy, we contrast the Computational Taxonomy, obtaining a new criterion and the robustness of the method.