Bit Sequences and Biclustering of Text Documents

Leung, Carson K.; Carmichael, Christopher L.; Hao, Boyu

doi:10.1109/icdmw.2007.38

Cited by 35 publications

(3 citation statements)

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“…Encontra-se, entretanto, o uso de resíduo quadrático médio [41,62], índice Davies-Bouldin [63], similaridade média intra e inter grupos [34] e inspeção visual [62]. Para análise do desempenho do algoritmo, encontra-se artigos nos quais os autores destacam análises de tempo de execução [60,64]. Além disso, o uso de índices de validação externa é bastante frequente na área de mineração de textos.…”

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“…Nesses casos, conjuntos de dados textuais de referência podem ser usados para facilitar comparações. Os conjuntos de dados usados nos artigos já mencionados nesta seção são: 20-Newsgroups [11,68,7,63,59,34,60,57,67,55,56,65,41], Reuters [9,68,34,60,57,67,69,33,64], CLASSIC3 [9,59,7,63,60,67], CLUTO toolkit [9,59,34,66,60,55], CSTR [9,59,34,65,33], WebACE [65,11,59,34,60], CLASSIC4 [65,56,9,59,55], MovieLens …”

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Biclustering and coclustering: concepts, algorithms and viability for text mining

Diaz

Peres

2019

RITA

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Biclustering and coclustering are data mining tasks capable of extracting relevant information from data by applying similarity criteria simultaneously to rows and columns of data matrices. Algorithms used to accomplish these tasks simultaneously cluster objects and attributes, enabling the discovery of biclusters or coclusters. Although similar, the natures and aims of these tasks are different, and coclustering can be seen as a generalization of biclustering. An accurate study on algorithms related to biclustering and coclustering is essential to achieve effectiveness when solving real-world problems. Determining the values appropriate for the parameters of these algorithms is even more difficult when complex real-world data are analyzed. For example, when biclustering or coclustering is applied to textual data (i.e., in text mining), a representation through a vector space model is required. Such representation usually generates vector spaces with a high number of dimensions and high sparsity, which influences the performance of many algorithms. This tutorial aims to didactically present concepts related to the biclustering and coclustering tasks and how two basic algorithms address these concepts. In addition, experiments are presented in data contexts with a high number of dimensions and high sparsity, represented by both a synthetic dataset and a corpus of real-world news. In general and comparative terms, the results obtained show the algorithm used for coclustering (i.e., NBVD) as the most appropriate for the experiments' context. Although the biclustering algorithm (i.e., Cheng and Church) was responsible for producing less relevant results in textual data than NBVD, its application in data with a high number of dimensions and high sparsity provided a suitable study environment to understand its operation.Resumo: Biagrupamento e coagrupamento são tarefas de análise de dados que permitem a extração de informação relevante aplicando critérios de similaridade simultaneamente às linhas e às colunas de matrizes de dados. Algoritmos usados na resolução destas tarefas agrupam simultaneamente os objetos e os atributos, possibilitando a criação de bigrupos ou cogrupos. Embora semelhantes, essas tarefas possuem naturezas e objetivos diferentes, sendo que coagrupamento pode ser visto como uma generalização de biagrupamento. Um estudo detalhado sobre algoritmos associados à cada tarefa é importante para alcançar eficácia na resolução de problemas do mundo real. Escolher os valores adequados para os parâmetros desses algoritmos é mais difícil quando dados complexos do mundo real são analisados. Por exemplo, para aplicar biagrupamento ou coagrupamento em dados textuais (i.e., em mineração de textos) é necessário usar uma representação de textos em um modelo de espaço vetorial. Esse tipo de representação comumente leva à geração de espaços vetoriais caracterizados pela alta dimensionalidade e esparsidade, e afeta o desempenho de muitos dos algoritmos. Este tutorial tem por objetivo apresentar, de forma didática, os con...

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Section: Leituras Adicionaisunclassified

Biclustering and coclustering: concepts, algorithms and viability for text mining

Diaz

Peres

2019

RITA

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“…A subseção 2.5.2 introduz o conceito de biclusterização -biclustering ou coclustering (MADEIRA; OLIVEIRA, 2004;CHO, 2008), que se refere a uma classe distinta de algoritmos de agrupamento que executam simultaneamente o agrupamento de linhas e colunas. Algoritmos de biclusterização têm sido utilizados em vários domínios (MADEIRA;OLIVEIRA, 2004;FRANÇA, 2010;MIMAROGLU;UEHARA, 2007;SYMEONIDIS et al, 2008). No contexto de sistemas de recomendação, tais algoritmos são empregados para simultaneamente agrupar usuários e itens que possuam características similares.…”

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Agrupamento de dados baseado em predições de modelos de regressão: desenvolvimentos e aplicações em sistemas de recomendação

Pereira¹

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Recommender Systems (RS) are powerful and popular tools for e-commerce. To build its recommendations, RS make use of multiple data sources, capture the characteristics of items, users and their transactions, and take advantage of prediction models. Given the large amount of data involved in the predictions made by RS, is unlikely that all predictions can be well represented by a single global model. Another important aspect to note is the problem known as cold-start that, despite that recent advances in the RS area, it is still a relevant issue that deserves further attention. The problem arises due to the lack of prior information about new users and new items. This thesis presents a hybrid recommendation approach that addresses the (pure) cold start problem, where no collaborative information (ratings) is available for new users. The approach is based on an existing algorithm, named SCOAL (Simultaneous Co-Clustering and Learning). In its original version, based on multiple linear prediction models, the SCOAL algorithm has shown to be efficient and versatile. In addition, it can be used in a wide range of problems of classification and / or regression. The SCOAL algorithm showed impressive results with the use of linear prediction models, but there is still room for improvements with nonlinear models. From this perspective, this thesis presents a variant of the SCOAL based on Extreme Learning Machines. Besides improving the accuracy, another important issue related to the development of RS is system scalability. In this sense, a parallel version of the SCOAL, based on OpenMP, was developed, aimed at minimizing the computational cost involved as prediction models are learned. Experiments using real-world datasets has shown that all proposed developments make SCOAL algorithm even more attractive for a variety of practical applications.

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Hybrid Biclustering Algorithms for Data Mining

Orzechowski

Boryczko

2016

Applications of Evolutionary Computation

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Bit Sequences and Biclustering of Text Documents

Cited by 35 publications

References 10 publications

Biclustering and coclustering: concepts, algorithms and viability for text mining

Biclustering and coclustering: concepts, algorithms and viability for text mining

Agrupamento de dados baseado em predições de modelos de regressão: desenvolvimentos e aplicações em sistemas de recomendação

Hybrid Biclustering Algorithms for Data Mining

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