Informacja

Drogi użytkowniku, aplikacja do prawidłowego działania wymaga obsługi JavaScript. Proszę włącz obsługę JavaScript w Twojej przeglądarce.

English (EN)·Polski (PL)·Yкраїнський (UK)
Przejdź do strony domowej biblioteki Centralna Biblioteka Wojskowa Link otwiera się w nowym oknie Logo biblioteki Prolib Integro - strona głównaCentralna Biblioteka Wojskowa

Wyszukujesz frazę "graph mining" wg kryterium: Temat

  Lista filtrów
Wyrównaj
    Wyświetlanie 1-4 z 4
    Tytuł:
    Inferring graph grammars by detecting overlap in frequent subgraphs
    Autorzy:
    Kukluk, J. P.
    Holder, L. B.
    Cook, D. J.
    Powiązania:
    https://bibliotekanauki.pl/articles/907941.pdf
    Data publikacji:
    2008
    Wydawca:
    Uniwersytet Zielonogórski. Oficyna Wydawnicza
    Tematy:
    indukcja gramatyczna
    gramatyka grafowa
    pozyskiwanie danych
    grammar induction
    graph grammars
    graph mining
    multi-relational data mining
    Opis:
    In this paper we study the inference of node and edge replacement graph grammars. We search for frequent subgraphs and then check for an overlap among the instances of the subgraphs in the input graph. If the subgraphs overlap by one node, we propose a node replacement graph grammar production. If the subgraphs overlap by two nodes or two nodes and an edge, we propose an edge replacement graph grammar production. We can also infer a hierarchy of productions by compressing portions of a graph described by a production and then inferring new productions on the compressed graph. We validate the approach in experiments where we generate graphs from known grammars and measure how well the approach infers the original grammar from the generated graph. We show graph grammars found in biological molecules, biological networks, and analyze learning curves of the algorithm.
    Źródło:
    International Journal of Applied Mathematics and Computer Science; 2008, 18, 2; 241-250
    1641-876X
    2083-8492
    Pojawia się w:
    International Journal of Applied Mathematics and Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    B FGMAC : breadth - first frequent subgraph mining with ARC consistency
    Autorzy:
    Douar, B.
    Latiri, C.
    Liquiere, M.
    Slimani, Y.
    Powiązania:
    https://bibliotekanauki.pl/articles/91669.pdf
    Data publikacji:
    2011
    Wydawca:
    Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
    Tematy:
    graph mining
    arc consistency
    AC-projection
    projection operator
    AC-reduced
    subgraph mining
    polynomial complexity projection
    Opis:
    The paper presents a new projection operator for graphs named AC-projection, which exhibits nice theoretical complexity properties unlike to the graph isomorphism operator typically used in graph mining. We study the size of the search space as well as some practical properties of the projection operator. We also introduce a novel breadth-first algorithm for frequent AC-reduced subgraphs mining. Then, we prove experimentally that we can achieve an important performance gain (polynomial complexity projection) without or with non-significant loss of discovered patterns in terms of quality.
    Źródło:
    Journal of Artificial Intelligence and Soft Computing Research; 2011, 1, 4; 269-281
    2083-2567
    2449-6499
    Pojawia się w:
    Journal of Artificial Intelligence and Soft Computing Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    On graph mining with deep learning: introducing model r for link weight prediction
    Autorzy:
    Hou, Yuchen
    Holder, Lawrence B.
    Powiązania:
    https://bibliotekanauki.pl/articles/91884.pdf
    Data publikacji:
    2019
    Wydawca:
    Społeczna Akademia Nauk w Łodzi. Polskie Towarzystwo Sieci Neuronowych
    Tematy:
    deep learning
    neural networks
    machine learning
    graph mining
    link weight prediction
    predictive models
    node embeddings
    Opis:
    Deep learning has been successful in various domains including image recognition, speech recognition and natural language processing. However, the research on its application in graph mining is still in an early stage. Here we present Model R, a neural network model created to provide a deep learning approach to the link weight prediction problem. This model uses a node embedding technique that extracts node embeddings (knowledge of nodes) from the known links’ weights (relations between nodes) and uses this knowledge to predict the unknown links’ weights. We demonstrate the power of Model R through experiments and compare it with the stochastic block model and its derivatives. Model R shows that deep learning can be successfully applied to link weight prediction and it outperforms stochastic block model and its derivatives by up to 73% in terms of prediction accuracy. We analyze the node embeddings to confirm that closeness in embedding space correlates with stronger relationships as measured by the link weight. We anticipate this new approach will provide effective solutions to more graph mining tasks
    Źródło:
    Journal of Artificial Intelligence and Soft Computing Research; 2019, 9, 1; 21-40
    2083-2567
    2449-6499
    Pojawia się w:
    Journal of Artificial Intelligence and Soft Computing Research
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    An efficient approach for view selection for data warehouse using tree mining and evolutionary computation
    Autorzy:
    Thakare, A.
    Deshpande, P.
    Powiązania:
    https://bibliotekanauki.pl/articles/305413.pdf
    Data publikacji:
    2018
    Wydawca:
    Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
    Tematy:
    database management systems
    data warehousing and data mining
    query optimization
    graph mining
    algorithms for parallel computing
    evolutionary computations
    genetic algorithms
    Opis:
    The selection of a proper set of views to materialize plays an important role in database performance. There are many methods of view selection that use different techniques and frameworks to select an efficient set of views for materialization. In this paper, we present a new efficient scalable method for view selection under the given storage constraints using a tree mining approach and evolutionary optimization. The tree mining algorithm is designed to determine the exact frequency of (sub)queries in the historical SQL dataset. The Query Cost model achieves the objective of maximizing the performance benefits from the final view set that is derived from the frequent view set given by the tree mining algorithm. The performance benefit of a query is defined as a function of query frequency, query creation cost, and query maintenance cost. The experimental results show that the proposed method is successful in recommending a solution that is fairly close to an optimal solution.
    Źródło:
    Computer Science; 2018, 19 (4); 431-455
    1508-2806
    2300-7036
    Pojawia się w:
    Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-4 z 4

      Ta witryna wykorzystuje pliki cookies do przechowywania informacji na Twoim komputerze. Pliki cookies stosujemy w celu świadczenia usług na najwyższym poziomie, w tym w sposób dostosowany do indywidualnych potrzeb. Korzystanie z witryny bez zmiany ustawień dotyczących cookies oznacza, że będą one zamieszczane w Twoim komputerze. W każdym momencie możesz dokonać zmiany ustawień dotyczących cookies