. "anachronism; episode rules; inductive logic programming; temporal pattern; trend analysis; windowing"@en . . . . . "21230" . "IEEE Transactions on Systems, Man, and Cybernetics: Part C" . "394466" . . . "000251840500002" . "Sekven\u010Dn\u00ED data jsou v\u00FDznamn\u00FDm zdrojem potenci\u00E1ln\u011B nov\u00FDch l\u00E9ka\u0159sk\u00FDch znalost\u00ED. \u010Cl\u00E1nek aplikuje a srovn\u00E1v\u00E1 t\u0159i r\u016Fzn\u00E9 p\u0159\u00EDstupy sekven\u010Dn\u00EDho dolov\u00E1n\u00ED dat v p\u0159\u00EDpadov\u00E9 studii v\u00FDvoje rizikov\u00FDch faktor\u016F ateroskler\u00F3zy."@cs . . "\u0160t\u011Bp\u00E1nkov\u00E1, Olga" . "Dolov\u00E1n\u00ED sekven\u010Dn\u00EDch dat: srovn\u00E1vac\u00ED p\u0159\u00EDpadov\u00E1 studie v\u00FDvoje rizikov\u00FDch faktor\u016F ateroskler\u00F3zy"@cs . . . . "38" . "US - Spojen\u00E9 st\u00E1ty americk\u00E9" . "13"^^ . . "1" . "\u017Delezn\u00FD, Filip" . "Karel, Filip" . "Dolov\u00E1n\u00ED sekven\u010Dn\u00EDch dat: srovn\u00E1vac\u00ED p\u0159\u00EDpadov\u00E1 studie v\u00FDvoje rizikov\u00FDch faktor\u016F ateroskler\u00F3zy"@cs . "Nov\u00E1kov\u00E1, Lenka" . "Sequential Data Mining: A Comparative Case Study in Development of Atherosclerosis Risk Factors" . "5"^^ . . "1094-6977" . . "RIV/68407700:21230/08:03136549!RIV09-MSM-21230___" . . "P(1ET101210513), Z(MSM6840770012)" . . . . . "Sequential Data Mining: A Comparative Case Study in Development of Atherosclerosis Risk Factors"@en . "RIV/68407700:21230/08:03136549" . "Sequential Data Mining: A Comparative Case Study in Development of Atherosclerosis Risk Factors" . "5"^^ . "Sequential data represent an important source of potentially new medical knowledge. However, this type of data is rarely provided in a format suitable for immediate application of conventional mining algorithms. This paper summarizes and compares three different sequential mining approaches, based respectively on windowing, episode rules and inductive logic programming. Windowing is one of the essential methods of data preprocessing, episode rules represent general sequential mining while inductive logic programming extracts first order features whose structure is determined by background knowledge. The three approaches are demonstrated and evaluated in terms of a case study STULONG. It is a longitudinal preventive study of atherosclerosis where the data consist of series of longterm observations recording the development of risk factors and associated conditions." . "[406CB7AB22CF]" . . . "Kl\u00E9ma, Ji\u0159\u00ED" . . . "Sequential Data Mining: A Comparative Case Study in Development of Atherosclerosis Risk Factors"@en . "Sequential data represent an important source of potentially new medical knowledge. However, this type of data is rarely provided in a format suitable for immediate application of conventional mining algorithms. This paper summarizes and compares three different sequential mining approaches, based respectively on windowing, episode rules and inductive logic programming. Windowing is one of the essential methods of data preprocessing, episode rules represent general sequential mining while inductive logic programming extracts first order features whose structure is determined by background knowledge. The three approaches are demonstrated and evaluated in terms of a case study STULONG. It is a longitudinal preventive study of atherosclerosis where the data consist of series of longterm observations recording the development of risk factors and associated conditions."@en . .