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Wyszukujesz frazę "sequential patterns" wg kryterium: Temat


Wyświetlanie 1-3 z 3
Tytuł:
An experimental evaluation of two approaches to mining context based sequential patterns
Autorzy:
Stefanowski, J.
Ziembiński, R.
Powiązania:
https://bibliotekanauki.pl/articles/970837.pdf
Data publikacji:
2009
Wydawca:
Polska Akademia Nauk. Instytut Badań Systemowych PAN
Tematy:
knowledge discovery
sequential patterns mining
context patterns
similarity of patterns
Opis:
The paper discusses the results of experiments with a new context extension of a sequential pattern mining problem. In this extension, two kinds of context attributes are introduced for describing the source of a sequence and for each element inside this sequence. Such context based sequential patterns may be discovered by a new algorithm, called Context Mapping Improved, specific for handling attributes with similarity functions. For numerical attributes an alternative approach could include their pre-discretization, transforming discrete values into artificial items and, then, using an adaptation of an algorithm for mining sequential patterns from nominal items. The aim of this paper is to experimentally compare these two approaches to mine artificially generated sequence databases with numerical context attributes where several reference patterns are hidden. The results of experiments show that the Context Mapping Improved algorithm has led to better re-discovery of reference patterns. Moreover, a new measure for comparing two sets of context based patterns is introduced.
Źródło:
Control and Cybernetics; 2009, 38, 1; 27-45
0324-8569
Pojawia się w:
Control and Cybernetics
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Mining Pharmacy Database Using Evolutionary Genetic Algorithm
Autorzy:
Ykhlef, M.
ElGibreen, H.
Powiązania:
https://bibliotekanauki.pl/articles/226717.pdf
Data publikacji:
2010
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
data mining
evolutionary algorithms
genetic algorithm
pharmacy database
sequential patterns
Opis:
Medication management is an important process in pharmacy field. Prescribing errors occur upstream in the process, and their effects can be perpetuated in subsequent steps. Prescription errors are an important issue for which conflicts with another prescribed medicine could cause severe harm for a patient. In addition, due to the shortage of pharmacists and to contain the cost of healthcare delivery, time is also an important issue. Former knowledge of prescriptions can reduce the errors, and discovery of such knowledge requires data mining techniques, such as Sequential Pattern. Moreover, Evolutionary Algorithms, such as Genetic Algorithm (GA), can find good rules in short time, thus it can be used to discover the Sequential Patterns in Pharmacy Database. In this paper GA is used to assess patient prescriptions based on former knowledge of series of prescriptions in order to extract sequenced patterns and predict unusual activities to reduce errors in timely manner.
Źródło:
International Journal of Electronics and Telecommunications; 2010, 56, 4; 427-432
2300-1933
Pojawia się w:
International Journal of Electronics and Telecommunications
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
A new approach for discovering top-k sequential patterns based on the variety of items
Autorzy:
Sakurai, S.
Nishizawa, M.
Powiązania:
https://bibliotekanauki.pl/articles/91708.pdf
Data publikacji:
2015
Wydawca:
Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
Tematy:
sequential data
sequential patterns
synthetic sequential data
numerical experiment
top-k
dane sekwencyjne
wzorce sekwencyjne
syntetyczne dane sekwencyjne
eksperyment numeryczny
Opis:
This paper proposes a method that discovers various sequential patterns from sequential data. The sequential data is a set of sequences. Each sequence is a row of item sets. Many previous methods discover frequent sequential patterns from the data. However, the patterns tend to be similar to each other because they are composed of limited items. The patterns do not always correspond to the interests of analysts. Therefore, this paper tackles on the issue discovering various sequential patterns. The proposed method decides redundant sequential patterns by evaluating the variety of items and deletes them based on three kinds of delete processes. It can discover various sequential patterns within the upper bound for the number of sequential patterns given by the analysts. This paper applies the method to the synthetic sequential data which is characterized by number of items, their kind, and length of sequence. The effect of the method is verified through numerical experiments.
Źródło:
Journal of Artificial Intelligence and Soft Computing Research; 2015, 5, 2; 141-153
2083-2567
2449-6499
Pojawia się w:
Journal of Artificial Intelligence and Soft Computing Research
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-3 z 3

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