000			01811naa a2200241 a 4500
003			AR-LpUFIB
005			20250311170448.0
008			230201s2015 xx r 000 0 eng d
024	8		_aDIF-M7604 _b7825 _zDIF006950
040			_aAR-LpUFIB _bspa _cAR-LpUFIB
100	1		_aLanzarini, Laura Cristina
245	1	0	_aSOM+PSO : _ba novel method to obtain classification rules
300			_a1 archivo (1,5 MB)
500			_aFormato de archivo PDF. -- Este documento es producción intelectual de la Facultad de Informática - UNLP (Colección BIPA/Biblioteca)
520			_aCurrently, most processes have a volume of historical information that makes its manual processing difficult. Data mining, one of the most significant stages in the Knowledge Discovery in Databases (KDD) process, has a set of techniques capable of modeling and summarizing these historical data, making it easier to understand them and helping the decision making process in future situations. This article presents a new data mining adaptive technique called SOM+PSO that can build, from the available information, a reduced set of simple classification rules from which the most significant relations between the features recorded can be derived. These rules operate both on numeric and nominal attributes, and they are built by combining a variation of a population metaheuristic and a competitive neural network. The method proposed was compared with the PART method and measured over 19 databases (mostly from the UCI repository), and satisfactory results were obtained.
534			_aJournal of Computer Science & Technology, 15(1), pp. 15-22.
650		4	_aMINERÍA DE DATOS
650		4	_aBASES DE DATOS
653			_areglas de clasificación
700	1		_aVilla Monte, Augusto
700	1		_aRonchetti, Franco
942			_cCP
999			_c56726 _d56726