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    Wyświetlanie 1-7 z 7
    Tytuł:
    A document clustering method based on ant algorithms
    Autorzy:
    Machnik, Ł.
    Powiązania:
    https://bibliotekanauki.pl/articles/1943269.pdf
    Data publikacji:
    2007
    Wydawca:
    Politechnika Gdańska
    Tematy:
    ant algorithms
    ant systems
    document clustering
    document grouping
    Opis:
    Ant Algorithms, particularly the Ant Colony Optimization (ACO) metaheuristic, are universal, flexible and scalable because they are based on multi-agent cooperation. The increased demand for effective methods of managing large collections of documents is a sufficient stimulus to place the research on new applications of ant-based systems in the area of text document processing. The author presents an implementation of such a technique in the area of document clustering. Details of the ACO document clustering method and results of experiments are presented.
    Źródło:
    TASK Quarterly. Scientific Bulletin of Academic Computer Centre in Gdansk; 2007, 11, 1-2; 87-102
    1428-6394
    Pojawia się w:
    TASK Quarterly. Scientific Bulletin of Academic Computer Centre in Gdansk
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Multiplicative Algorithm for Correntropy-Based Nonnegative Matrix Factorization
    Autorzy:
    Hosseini-Asl, E.
    Zurada, J. M.
    Powiązania:
    https://bibliotekanauki.pl/articles/108758.pdf
    Data publikacji:
    2013
    Wydawca:
    Społeczna Akademia Nauk w Łodzi
    Tematy:
    Nonnegative Matrix Factorization (NMF)
    Correntropy
    Multiplicative Algorithm
    Document Clustering
    Opis:
    Nonnegative matrix factorization (NMF) is a popular dimension reduction technique used for clustering by extracting latent features from highdimensional data and is widely used for text mining. Several optimization algorithms have been developed for NMF with different cost functions. In this paper we evaluate the correntropy similarity cost function. Correntropy is a nonlinear localized similarity measure which measures the similarity between two random variables using entropy-based criterion, and is especially robust to outliers. Some algorithms based on gradient descent have been used for correntropy cost function, but their convergence is highly dependent on proper initialization and step size and other parameter selection. The proposed general multiplicative factorization algorithm uses the gradient descent algorithm with adaptive step size to maximize the correntropy similarity between the data matrix and its factorization. After devising the algorithm, its performance has been evaluated for document clustering. Results were compared with constrained gradient descent method using steepest descent and L-BFGS methods. The simulations show that the performance of steepest descent and LBFGS convergence are highly dependent on gradient descent step size which depends on σ parameter of correntropy cost function. However, the multiplicative algorithm is shown to be less sensitive to σ parameterand yields better clustering results than other algorithms. The results demonstrate that clustering performance measured by entropy and purity improve the clustering. The multiplicative correntropy-based algorithm also shows less variation in accuracy of document clusters for variable number of clusters. The convergence of each algorithm is also investigated, and the experiments have shown that the multiplicative algorithm converges faster than L-BFGS and steepest descent method.
    Źródło:
    Journal of Applied Computer Science Methods; 2013, 5 No. 2; 89-104
    1689-9636
    Pojawia się w:
    Journal of Applied Computer Science Methods
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Extending k-means with the description comes first approach
    Autorzy:
    Stefanowski, J.
    Weiss, D.
    Powiązania:
    https://bibliotekanauki.pl/articles/970926.pdf
    Data publikacji:
    2007
    Wydawca:
    Polska Akademia Nauk. Instytut Badań Systemowych PAN
    Tematy:
    document clustering
    cluster labels
    k-means algorithm
    information retrieval
    Opis:
    This paper describes a technique for clustering large collections of short and medium length text documents such as press articles, news stories and the like. The technique called description comes first (DCF) consists of identification of related document clusters, selection of salient phrases relevant to these clusters and reallocation of documents matching the selected phrases to form final document groups. The advantages of this technique include more comprehensive cluster labels and clearer (more transparent) relationship between cluster labels and their content. We demonstrate the DCF by taking a standard k-means algorithm as a baseline and weaving DCF elements into it; the outcome is the descriptive k-means (DKM) algorithm. The paper goes through technical background explaining how to implement DKM efficiently and ends with the description of an experiment measuring clustering quality on a benchmark document collection 20-newsgroups. Short fragments of this paper appeared at the poster session of the RIAO 2007 conference, Pittsburgh, PA, USA (electronic proceedings only).
    Źródło:
    Control and Cybernetics; 2007, 36, 4; 1009-1035
    0324-8569
    Pojawia się w:
    Control and Cybernetics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Document Clustering : Concepts, Metrics and Algorithms
    Autorzy:
    Tarczynski, T.
    Powiązania:
    https://bibliotekanauki.pl/articles/226231.pdf
    Data publikacji:
    2011
    Wydawca:
    Polska Akademia Nauk. Czytelnia Czasopism PAN
    Tematy:
    document clustering
    text mining
    k-means
    hierarchical clustersting
    vector space model
    Opis:
    Document clustering, which is also refered to as text clustering, is a technique of unsupervised document organisation. Text clustering is used to group documents into subsets that consist of texts that are similar to each orher. These subsets are called clusters. Document clustering algorithms are widely used in web searching engines to produce results relevant to a query. An example of practical use of those techniques are Yahoo! hierarchies of documents [1]. Another application of document clustering is browsing which is defined as searching session without well specific goal. The browsing techniques heavily relies on document clustering. In this article we examine the most important concepts related to document clustering. Besides the algorithms we present comprehensive discussion about representation of documents, calculation of similarity between documents and evaluation of clusters quality.
    Źródło:
    International Journal of Electronics and Telecommunications; 2011, 57, 3; 271-277
    2300-1933
    Pojawia się w:
    International Journal of Electronics and Telecommunications
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Extraction of Polish noun senses from large corpora by means of clustering
    Autorzy:
    Broda, B.
    Piasecki, M.
    Szpakowicz, S.
    Powiązania:
    https://bibliotekanauki.pl/articles/969804.pdf
    Data publikacji:
    2010
    Wydawca:
    Polska Akademia Nauk. Instytut Badań Systemowych PAN
    Tematy:
    corpus linguistics
    semantic similarity
    Polish nouns
    word clustering
    Clustering by Committee
    co-occurrence retrieval models
    rank weight function
    Polish WordNet
    WordNet-based synonymy test
    document clustering
    keywords extraction
    Opis:
    We investigate two methods of identifying noun senses, based on clustering of lemmas and of documents. We have adapted to Polish the well-known algorithm of Clustering by Committee, and tested it on very large Polish corpora. The evaluation by means of a WordNet-based synonymy test used Polish wordnet (plWordNet 1.0). Various clustering algorithms were analysed for the needs of extraction of document clusters as indicators of the senses of words which occur in them. The two approaches to wordsense identification have been compared, and conclusions drawn.
    Źródło:
    Control and Cybernetics; 2010, 39, 2; 401-420
    0324-8569
    Pojawia się w:
    Control and Cybernetics
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Benchmarking high performance architectures with natural language processing algorithms
    Benchmarking architektur wysokiej wydajności algorytmami przetwarzania języka naturalnego
    Autorzy:
    Kuta, M.
    Kitowski, J.
    Powiązania:
    https://bibliotekanauki.pl/articles/305469.pdf
    Data publikacji:
    2011
    Wydawca:
    Akademia Górniczo-Hutnicza im. Stanisława Staszica w Krakowie. Wydawnictwo AGH
    Tematy:
    benchmarking
    tagowanie częściami mowy
    klasteryzacja dokumentów
    przetwarzanie języka naturalnego
    architektury wysokiej wydajności
    part-of-speech tagging
    document clustering
    natural language processing
    high performance architectures
    Opis:
    Natural Language Processing algorithms are resource demanding, especially when tuning to inflective language like Polish is needed. The paper presents time and memory requirements of part of speech tagging and clustering algorithms applied to two corpora of the Polish language. The algorithms are benchmarked on three high performance platforms of different architectures. Additionally sequential versions and OpenMP implementations of clustering algorithms were compared.
    Algorytmy przetwarzania języka naturalnego mają duże zapotrzebowanie na zasoby komputerowe, szczególnie gdy wymagane jest dostosowanie algorytmu do języka fleksyjnego jakim jest np. język polski. Artykuł przedstawia wymagania czasowe i pamięciowe algorytmów tagowania częściami mowy oraz algorytmów klasteryzacji zastosowanych do dwóch korpusów języka polskiego. Dokonano benchmarkingu algorytmów na trzech platformach wysokiej wydajności reprezentujących różne architektury. Dodatkowo porównano wersję sekwencyjną oraz implementacje OpenMP algorytmów klasteryzacji.
    Źródło:
    Computer Science; 2011, 12; 19-31
    1508-2806
    2300-7036
    Pojawia się w:
    Computer Science
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
    Tytuł:
    Analysis of methods and means of text mining
    Autorzy:
    Rybchak, Z.
    Basystiuk, O.
    Powiązania:
    https://bibliotekanauki.pl/articles/411072.pdf
    Data publikacji:
    2017
    Wydawca:
    Polska Akademia Nauk. Oddział w Lublinie PAN
    Tematy:
    text mining
    text analytics
    data analysis
    high-quality information
    text categorization
    text clustering
    document summarization
    sentiment analysis
    sieć językowa
    analiza tekstu
    analiza danych
    wysoka jakość informacji
    klasyfikacja tekstowa
    kategoryzacja tekstowa
    grupowanie tekstu
    streszczenie dokumentów tekstowych
    technika sentiment analysis
    Opis:
    In Big Data era when data volume doubled every year analyzing of all this data become really complicated task, so in this case text mining systems, techniques and tools become main instrument of analyzing tones and tones of information, selecting that information that suit the best for your needs and just help save your time for more interesting thing. The main aims of this article are explain basic principles of this field and overview some interesting technologies that nowadays are widely used in text mining.
    Źródło:
    ECONTECHMOD : An International Quarterly Journal on Economics of Technology and Modelling Processes; 2017, 6, 2; 73-78
    2084-5715
    Pojawia się w:
    ECONTECHMOD : An International Quarterly Journal on Economics of Technology and Modelling Processes
    Dostawca treści:
    Biblioteka Nauki
    Artykuł
      Wyświetlanie 1-7 z 7

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