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    Tytuł:
    Probabilistic Sequence Mining : Evaluation and Extension of ProMFS Algorithm for Real-Time Problems
    Autorzy:
    Hryniów, K.
    Dzieliński, A.
    Powiązania:
    https://bibliotekanauki.pl/articles/226422.pdf
    Data publikacji:
    2012
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    ProMFS
    sequential pattern mining
    probabilistic mining
    real-time
    Opis:
    Sequential pattern mining is an extensively studied method for data mining. One of new and less documented approaches is estimation of statistical characteristics of sequence for creating model sequences, that can be used to speed up the process of sequence mining. This paper proposes extensive modifications to one of such algorithms, ProMFS (probabilistic algorithm for mining frequent sequences), which notably increases algorithm's processing speed by a significant reduction of its computational complexity. A new version of algorithm is evaluated for real-life and artificial data sets and proven to be useful in real-time applications and problems.
    Źródło:
    International Journal of Electronics and Telecommunications; 2012, 58, 4; 323-326
    2300-1933
    Pojawia się w:
    International Journal of Electronics and Telecommunications
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Accuracy of generalized context patterns in the context based sequential patterns mining
    Autorzy:
    Ziembiński, R. Z.
    Powiązania:
    https://bibliotekanauki.pl/articles/206061.pdf
    Data publikacji:
    2011
    Wydawca:
    Polska Akademia Nauk. Instytut Badań Systemowych PAN
    Tematy:
    knowledge discovery
    context based sequential pattern mining
    sequential context pattern clustering
    pattern accuracy
    Opis:
    A context pattern is a frequent subsequence mined from the context database containing set of sequences. This kind of sequential patterns and all elements inside them are described by additional sets of context attributes e.g. continuous ones. The contexts describe circumstances of transactions and sources of sequential data. These patterns can be mined by an algorithm for the context based sequential pattern mining. However, this can create large sets of patterns because all contexts related to patterns are taken from the database. The goal of the generalization method is to reduce the context pattern set by introducing a more compact and descriptive kind of patterns. This is achieved by finding clusters of similar context patterns in the mined set and transforming them to a smaller set of generalized context patterns. This process has to retain as much as possible information from the mined context patterns. This paper introduces a definition of the generalized context pattern and the related algorithm. Results from the generalization may differ as depending on the algorithm design and settings. Hence, generalized patterns may reflect frequent information from the context database differently. Thus, an accuracy measure is also proposed to evaluate the generalized patterns. This measure is used in the experiments presented. The generalized context patterns are compared to patterns mined by the basic sequential patterns mining with prediscretization of context values.
    Źródło:
    Control and Cybernetics; 2011, 40, 3; 585-603
    0324-8569
    Pojawia się w:
    Control and Cybernetics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-2 z 2

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