"1210-0552" . "3"^^ . "3"^^ . . "CZ - \u010Cesk\u00E1 republika" . . . . . "RIV/68407700:21230/04:03107391!RIV/2005/MSM/212305/N" . "[5A49D9A2FFF3]" . "554137" . "14" . "Z(MSM 210000012)" . "Anachronistic Attributes in Temporal Data: A Case Study"@en . . . "Anachronistic Attributes in Temporal Data: A Case Study" . . "Neural Network World" . . . . . . "Anachronistic Attributes in Temporal Data: A Case Study"@en . "Anachronistic Attributes in Temporal Data: A Case Study" . "Nov\u00E1kov\u00E1, Lenka" . . "Kl\u00E9ma, Ji\u0159\u00ED" . "\u0160t\u011Bp\u00E1nkov\u00E1, Olga" . "Data mining; anachronistic attribute; data preprocessing; sequential data; temporal pattern; trend analysis; windowing"@en . . . "Nen\u00ED k dispozici"@cs . . "RIV/68407700:21230/04:03107391" . "421 ; 434" . . "The paper concerns mining data lacking the uniform structure. The data are collected from a number of objects during repeated measurements, all of which are tagged by a corresponding time. No attribute-valued machine learning algorithm can be applied directly on such data since the number of measurements is not fixed but it varies. The available data have to be transformed and preprocessed in such a way that a uniform type of information is obtained about all the considered objects. This can be achieved, e.g., by aggregation. But this process can introduce anachronistic variables, i.e., variables containing information which cannot be available at the moment when a prediction is needed. The paper suggests and tests a method how to preprocess the considered type of data without falling into a trap of introducing anachronistic attributes. The method is illustrated on a case study based on STULONG data." . . "21230" . "Nen\u00ED k dispozici"@cs . . "Nen\u00ED k dispozici"@cs . "5" . "14"^^ . . "The paper concerns mining data lacking the uniform structure. The data are collected from a number of objects during repeated measurements, all of which are tagged by a corresponding time. No attribute-valued machine learning algorithm can be applied directly on such data since the number of measurements is not fixed but it varies. The available data have to be transformed and preprocessed in such a way that a uniform type of information is obtained about all the considered objects. This can be achieved, e.g., by aggregation. But this process can introduce anachronistic variables, i.e., variables containing information which cannot be available at the moment when a prediction is needed. The paper suggests and tests a method how to preprocess the considered type of data without falling into a trap of introducing anachronistic attributes. The method is illustrated on a case study based on STULONG data."@en .