Дисертації з теми "Latent Semantic Indexing (LSI)"
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Zhu, Weizhong Allen Robert B. "Text clustering and active learning using a LSI subspace signature model and query expansion /." Philadelphia, Pa. : Drexel University, 2009. http://hdl.handle.net/1860/3077.
Повний текст джерелаLa, Fleur Magnus, and Fredrik Renström. "Conceptual Indexing using Latent Semantic Indexing : A Case Study." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-263029.
Повний текст джерелаSuwannajan, Pakinee. "Evaluating the performance of latent semantic indexing." Diss., Connect to online resource, 2005. http://wwwlib.umi.com/dissertations/fullcit/3178359.
Повний текст джерелаAraújo, Hugo Rafael Teixeira Soares. "Exploring biomedical literature using latent semantic indexing." Master's thesis, Universidade de Aveiro, 2012. http://hdl.handle.net/10773/11298.
Повний текст джерелаO rápido crescimento de dados disponível na Internet e o facto de se encontrar maioritariamente na forma de texto não estruturado, tem criado sucessivos desafios na recuperação e indexação desta informação. Para além da Internet, também inúmeras bases de dados documentais, de áreas específicas do conhecimento, são confrontadas com este problema. Com a quantidade de informação a crescer tão rapidamente, os métodos tradicionais para indexar e recuperar informação, tornam-se insuficientes face a requisitos cada vez mais exigentes por parte dos utilizadores. Estes problemas levam à necessidade de melhorar os sistemas de recuperação de informação, usando técnicas mais poderosas e eficientes. Um desses métodos designa-se por Latent Semantic Indexing (LSI) e, tem sido sugerido como uma boa solução para modelar e analisar texto não estruturado. O LSI permite revelar a estrutura semântica de um corpus, descobrindo relações entre documentos e termos, mostrando-se uma solução robusta para o melhoramento de sistemas de recuperação de informação, especialmente a identificação de documentos relevantes para a pesquisa de um utilizador. Além disso, o LSI pode ser útil em outras tarefas tais como indexação de documentos e anotação de termos. O principal objectivo deste projeto consistiu no estudo e exploração do LSI na anotação de termos e na estruturação dos resultados de um sistema de recuperação de informação. São apresentados resultados de desempenho destes algoritmos e são igualmente propostas algumas formas para visualizar estes resultados.
The rapid increase in the amount of data available on the Internet, and the fact that this is mostly in the form of unstructured text, has brought successive challenges in information indexing and retrieval. Besides the Internet, specific literature databases are also faced with these problems. With the amount of information growing so rapidly, traditional methods for indexing and retrieving information become insufficient for the increasingly stringent requirements from users. These issues lead to the need of improving information retrieval systems using more powerful and efficient techniques. One of those methods is the Latent Semantic Indexing (LSI), which has been suggested as a good solution for modeling and analyzing unstructured text. LSI allows discovering the semantic structure in a corpus, by finding the relations between documents and terms. It is a robust solution for improving information retrieval systems, especially in the identification of relevant documents for a user's query. Besides this, LSI can be useful in other tasks such as document indexing and annotation of terms. The main goal of this project consisted in studying and exploring the LSI process for terms annotations and for structuring the retrieved documents from an information retrieval system. The performance results of these algorithms are presented and, in addition, several new forms of visualizing these results are proposed.
Geiß, Johanna. "Latent Semantic Indexing and Information Retrieval a quest with BosSE /." [S.l. : s.n.], 2006. http://nbn-resolving.de/urn:nbn:de:bsz:16-opus-67536.
Повний текст джерелаGeiss, Johanna. "Latent semantic sentence clustering for multi-document summarization." Thesis, University of Cambridge, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.609761.
Повний текст джерелаBuys, Stephanus. "Log analysis aided by latent semantic mapping." Thesis, Rhodes University, 2013. http://hdl.handle.net/10962/d1002963.
Повний текст джерелаLaTeX with hyperref package
Adobe Acrobat 9.54 Paper Capture Plug-in
Polyakov, Serhiy. "Enhancing User Search Experience in Digital Libraries with Rotated Latent Semantic Indexing." Thesis, University of North Texas, 2015. https://digital.library.unt.edu/ark:/67531/metadc804881/.
Повний текст джерелаSpomer, Judith E. "Latent semantic analysis and classification modeling in applications for social movement theory /." Abstract Full Text (HTML) Full Text (PDF), 2008. http://eprints.ccsu.edu/archive/00000552/02/1996FT.htm.
Повний текст джерелаThesis advisor: Roger Bilisoly. "... in partial fulfillment of the requirements for the degree of Master of Science in Data Mining." Includes bibliographical references (leaves 122-127). Also available via the World Wide Web.
Hockey, Andrew. "Computational modelling of the language production system : semantic memory, conflict monitoring, and cognitive control processes /." [St. Lucia, Qld.], 2006. http://www.library.uq.edu.au/pdfserve.php?image=thesisabs/absthe20099.pdf.
Повний текст джерелаAlsallal, M. "A machine learning approach for plagiarism detection." Thesis, Coventry University, 2016. http://curve.coventry.ac.uk/open/items/7e903a56-4845-4852-b1a8-2849b1cdb08a/1.
Повний текст джерелаZaras, Dimitrios. "Evaluating Semantic Internalization Among Users of an Online Review Platform." Thesis, University of North Texas, 2015. https://digital.library.unt.edu/ark:/67531/metadc804823/.
Повний текст джерелаAlazzam, Iyad. "Using Information Retrieval to Improve Integration Testing." Diss., North Dakota State University, 2012. https://hdl.handle.net/10365/26508.
Повний текст джерелаChen, Xin. "Human-centered semantic retrieval in multimedia databases." Birmingham, Ala. : University of Alabama at Birmingham, 2008. https://www.mhsl.uab.edu/dt/2008p/chen.pdf.
Повний текст джерелаAdditional advisors: Barrett R. Bryant, Yuhua Song, Alan Sprague, Robert W. Thacker. Description based on contents viewed Oct. 8, 2008; title from PDF t.p. Includes bibliographical references (p. 172-183).
Langley, Joseph R. "SCRIBE a clustering approach to semantic information retrieval /." Master's thesis, Mississippi State : Mississippi State University, 2006. http://sun.library.msstate.edu/ETD-db/ETD-browse/browse.
Повний текст джерелаMeqdadi, Omar Mohammed. "UNDERSTANDING AND IDENTIFYING LARGE-SCALE ADAPTIVE CHANGES FROM VERSION HISTORIES." Kent State University / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=kent1374791564.
Повний текст джерелаNovák, Ján. "Automatická tvorba tezauru z wikipedie." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2011. http://www.nusl.cz/ntk/nusl-236964.
Повний текст джерелаVasireddy, Jhansi Lakshmi. "Applications of Linear Algebra to Information Retrieval." Digital Archive @ GSU, 2009. http://digitalarchive.gsu.edu/math_theses/71.
Повний текст джерелаHájek, Petr. "Možnosti využití netradičních kvantitativních metod při předpovídání finančních krizí." Doctoral thesis, Vysoká škola ekonomická v Praze, 2007. http://www.nusl.cz/ntk/nusl-2340.
Повний текст джерелаMacedo, Alessandra Alaniz. "Especificação, instanciação e experimentação de um arcabouço para criação automática de ligações hipertexto entre informações homogêneas." Universidade de São Paulo, 2004. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-05102004-113421/.
Повний текст джерелаWith the evolution of the Internet, distinct communication media have focused on the Web as a channel of information publishing. An immediate consequence is an abundance of sources of information and writing styles in the Web. This effect, combining with the inherent curiosity of human beings, has led Web users to look for more than a single article about a same subject. To gain access to separate on a same subject, readers need to search, read and analyze information provided by different sources of information. Besides consuming a great amount of time, that activity imposes a cognitive overhead to users. Several hypermedia researches have investigated mechanisms for supporting users during the process of identifying information on homogeneous repositories, available or not on the Web. In this thesis, homogeneous repositories are those containing information that describes a same subject. This thesis aims at investigating the specification and the construction of a framework intended to support the task of automatic creation of hypertext links between homogeneous repositories. The framework proposed, called CARe (Automatic Creation of Relationships), is composed of a set of classes, methods and relationships that gather information to be related, and also process that information for generating an index. Those indexes are related and used in the automatic creation of hypertext links among distinct excerpts of original information. The framework was defined based on a phase of domain analysis in which requirements were identified and software components were built. In that same phase several prototypes were developed in an iterative prototyping
Zougris, Konstantinos. "Sociological Applications of Topic Extraction Techniques: Two Case Studies." Thesis, University of North Texas, 2015. https://digital.library.unt.edu/ark:/67531/metadc804982/.
Повний текст джерелаAlhindawi, Nouh Talal. "Supporting Source Code Comprehension During Software Evolution and Maintenance." Kent State University / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=kent1374790792.
Повний текст джерелаPohlídal, Antonín. "Inteligentní emailová schránka." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2012. http://www.nusl.cz/ntk/nusl-236458.
Повний текст джерелаKontostathis, April. "A term co-occurrence based framework for understanding LSI [i.e. latent semantic indexing] : theory and practice /." Diss., 2003. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3117161.
Повний текст джерелаShiung, Ruei-shiang, and 熊瑞祥. "On Latent semantic Indexing." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/42703613124815531926.
Повний текст джерела國立中正大學
應用數學研究所
94
In this article we study the well-known information retrieval(IR) system : the Latent Semantic Indexing(LSI) model. This method projects document vectors into some specific subspace of the range of the term-document matrix. We provide two new methods, called QR method and Gram-Schmidt method, for projecting document vectors into different subspaces and compare them with the LSI model. Furthermore, we show the numerical experiment in using the LSI model, the vector space model, and the QR method on Medline collection and Cranfield collection.
Zhang, Xueshan. "Novelty Detection by Latent Semantic Indexing." Thesis, 2013. http://hdl.handle.net/10012/7560.
Повний текст джерелаLin, Chia-min, and 林家民. "Clustering Multilingual Documents: A Latent Semantic Indexing Based Approach." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/69494075344886983368.
Повний текст джерела國立中山大學
資訊管理學系研究所
94
Document clustering automatically organizes a document collection into distinct groups of similar documents on the basis of their contents. Most of existing document clustering techniques deal with monolingual documents (i.e., documents written in one language). However, with the trend of globalization and advances in Internet technology, an organization or individual often generates/acquires and subsequently archives documents in different languages, thus creating the need for multilingual document clustering (MLDC). Motivated by its significance and need, this study designs a Latent Semantic Indexing (LSI) based MLDC technique. Our empirical evaluation results show that the proposed LSI-based multilingual document clustering technique achieves satisfactory clustering effectiveness, measured by both cluster recall and cluster precision.
Zhuang, Ke-Ren, and 莊可任. "Automatic Presentation Slide Generation based on Latent Semantic Indexing." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/97034104190672499230.
Повний текст джерела國立屏東商業技術學院
資訊管理系(所)
100
We proposed using Latent Semantic Analysis (LSA) to generate document summary, and further form PowerPoint slides to help researchers to organize their briefings. Comparing with automatic summarization, automatic slide generation requires: the alignment of the slide’s contents with their original chapters, and covering important issues of the document. To fulfill these two requirements, we proposed to get summary from each chapter/section, and to use LSA for topics extraction to cover document’s issues. In addition, Owing that the purpose of document summary is to extract sentences instead of terms, therefore, we suggested using sentence-by-paragraph matrix to substitute for the original term-by-sentence matrix. We evaluate the following parameters: TF (term frequency) or √TF in the frequency matrix; sentence restructuring (fix incorrect sentence segmentation and removing hyphens at the end of line) or non-restructuring. The compared summarizer includes NTU and LSA, where NTU is a non-topic extraction method; LSA is a topic extraction method. We first compared the differences between section-oriented summarization and whole document summarization. The results showed that both NTU and LSA perform better (higher F score) on section-oriented summarization than whole document summarization. It therefore verified our first idea that section- oriented summarization is more suitable for slide generation. We next compared the performances of TF and√TF. The results showed that NTU performs slightly better (but not significant) on √TF than on TF, but LSA performs poorer on √TF than TF. Therefore, TF is well enough and its speed is faster than √TF. Thirdly, we examined the effect of sentence restructuring. The results showed that both NTU and LSA can have improvements when applying sentence restructuring. The result coincides the information theory that garbage in, garbage out. When the input sentences are a mess, then the output will also be incorrect. Finally, we compared NTU, LSA-ts (term-by-sentence), LSA-sp(sentence-by-paragraph) using the whole dataset. The results showed that our proposed method LSA-sp perform best, far better than the other two. It thus demonstrated the validity of our proposed method.
Wang, Juo-Wen, and 汪若文. "Automatic Classification of Text Documents by Using Latent Semantic Indexing." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/09421240911724157604.
Повний текст джерела國立交通大學
管理學院碩士在職專班資訊管理組
92
Search and browse are both important tasks in information retrieval. Search provides a way to find information rapidly, but relying on words makes it hard to deal with the problems of synonym and polysemy. Besides, users sometimes cannot provide suitable query and cannot find the information they really need. To provide good information services, the service of browse through good classification mechanism as well as information search are very important. There are two steps in classifying documents. The first is to present documents in suitable mathematical forms. The second is to classify documents automatically by using suitable classification algorithms. Classification is a task of conceptualization. Presenting documents in conventional vector space model cannot avoid relying on words explicitly. Latent semantic indexing (LSI) is developed to find the semantic concept of document, which may be suitable for the classification of documents. This thesis is intended to study the feasibility and effect of the classification of text documents by using LSI as the presentation of documents, and using both centroid vector and k-NN as the classification algorithms. The results are compared to those of the vector space model. This study deals with the problem of one-category classification. The results show that automatic classification of text documents by using LSI along with suitable classification algorithms is feasible. But the accuracy of classification by using LSI is not as good as by using vector space model. The effect of applying LSI on multi-category classification and the effect of combining LSI with other classification algorithms need further studies.
Zeng, Wei-Rong, and 曾韋榮. "Combining Latent Semantic Indexing with Information granulation for Data Mining." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/pr286u.
Повний текст джерела國立臺北科技大學
商業自動化與管理研究所
94
With the rapid information growth, the development of data mining aims at discovering useful patterns from the huge amount of data. Enterprise data usually have features of multi-dimension, sparsity and imbalance. These features result in significant impacts on the functions of data mining. Therefore, data preprocessing has become an essential task in data mining, which can reduce the data size and remove noises and outliers. By using Singular Value Decomposition, Latent Semantic Indexing (LSI) can effectively process multidimensional and sparse data. The data possessing features of multi-dimension and sparsity can be preprocessed by using LSI to reduce the data dimension and records. In the case of processing imbalance data, Information Granulation (IG) can transform data of majority class that share similar property into information granule in order to raise the ratio of minority as well as to resolve problem of imbalance data. Therefore, LSI and IG can be taken as the first stage of data preprocessing in data mining process. This thesis combines LSI with IG for data mining in order to achieve the goal of reducing the size and dimensions of data, and resolve the problem caused by imbalance data. According to the results in this thesis, it points out that implementing LSI to data can effectively reduce the dimensions of data, implementing LSI+IG to data can effectively reduce the dimensions and the size of data, and implementing IG+LSI to data can effectively reduce the sub-attributes (generated in IG process) and the size of data. Moreover, all these three methods of data reduction can reduce the computational time of analysis. In the case of processing imbalance data, the computational results indicate that LSI alone is not suitable for preprocessing imbalance data. By implementing LSI+IG or IG+LSI to preprocessing imbalance data, the accuracy of minority class is improved. This thesis concludes that the results of classification can be most improved provided that IG+LSI is adopted.
Lin, Guan-Hong, and 林冠宏. "Protein Function Prediction from Protein Interaction Networks by Latent Semantic Indexing." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/77778843235131898005.
Повний текст джерела國立中央大學
資訊工程研究所
93
Determining protein function is one of the most important tasks in the post-genomic era. Large-scale biological experiment results such as protein interaction networks can be obtained now, and these data often involve the information about protein functions. In this thesis, we present an approach based on Latent Semantic Indexing (LSI) to extract this information from protein interaction networks. LSI is an information retrieval technique that can solve the synonymy and polysemy problems. Because biologists believe that there are a lot of false positives and false negatives in protein interaction networks, we use the properties of LSI to filter out the wrong and confused information retrieved from these networks. Our results show that our approach can find out the functional related proteins in cells.
Chang, Jyh-Cheng, and 張志成. "Computer-Assisted Construction of Knowledge Map Based on Latent Semantic Indexing." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/69111521837321476885.
Повний текст джерела中原大學
電子工程研究所
95
The most important issue of constructing a concept map is not coming up with the list of concepts to involve, but linking the concepts into meaningful propositions to create a connected structure that reflects the person’s understanding of a domain. This research present a system which, during the process of concept mapping, takes the partially constructed map or an independent keyword as input to mine the Web, and suggests to the user a list of weighted concepts that are relevant to the map under construction. The system first uses the Latent Semantic Indexing (LSI) algorithm to analyze the contents on the web and transforms the contents into a set of relevant terms. Next, the relevant terms are evaluated with a sigmoid function to transform into a list of weighted concepts. The system also designs a knowledge structure, Knowledge Map, which extends from the concept map with a hierarchical structure on the computer to store the learners' concepts. The system with knowledge map is called KMap. The KMap system can also record the learners' learning processes and play the knowledge map construction processes again. At the end of this study, a prototype system is implemented and used to demonstrate the suggestion process. The system uses the popular web content, Wikipedia, as analyzing content. After analyzing the content, the system proposes three kinds of learning model, Free-style, Guided and Expert learning model, to help learner constructing the knowledge map.
Majavu, Wabo. "Classification of web resident sensor resources using latent semantic indexing and ontologies." Thesis, 2010. http://hdl.handle.net/10539/7920.
Повний текст джерелаHsiu, Min, and 何旻修. "The Research of Using Agglomerative Fuzzy K-Means Clustering in Latent Semantic Indexing." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/35507679547671787463.
Повний текст джерела國立中央大學
資訊管理研究所
98
Due to high cost of computing latent semantic indexing has not been popular, and full computing of large datasets is still too expensive is concerned by some scholars recently, and some strategies is improved based on clustering, that allow users to query keywords with little effort on comparing with similar clusters and reduce computational cost. However, those studies using the strategies based on clustering can only be compared with a fixed number of comparisons, and the query results are limited. The Agglomerative Fuzzy K-Means Clustering algorithm is proposed to carry out clusters on the large datasets. Each cluster is analyzed with execute singular value decomposition and low-rank approximations respectively to identify each document mapping in a low dimensional vector space coordinates. When keywords are input to query through the fuzzy clustering, latent semantic indexing with similar cluster can be carried out dynamically. Finally the documents with relevant keywords can be found. The experimental results show that the study does increase the quality of information retrieval comparing with traditional methods clustered in advance effectively, and in the dynamic cluster selection related with keywords almost chooses the best clustering number. F-measure value reached 83% in a single keyword query and recall rate is as high as 85%, the mean times F-measure value is also 72% in two key words query. Proved by Agglomerative Fuzzy K-Means Clustering algorithm for clustering, most of the web pages of information can be filtered with irrelevant cluster, and latent semantic index of the huge calculation costs can be reduced.
Lin, Chun-Yu, and 林俊宇. "Language Identification of Language-Mixed Speech Using Latent Semantic Indexing and Language Model." Thesis, 2002. http://ndltd.ncl.edu.tw/handle/5cyrat.
Повний текст джерела國立成功大學
資訊工程學系碩博士班
90
With the trend of globalizing information exchangeability and communication, human machine interface with multi-lingual processing ability to distinguish between languages and provide inter-connective services become increasingly important. In the multi-lingual spoken language and dialog applications, the problem of multiple language or mixed language input is crucial for speech recognition. Recent researches into automatic language identification (LID) and recognition have been addressed to keep up with the growing demand from the application side. These approaches had more emphasis on the task of determining the language in which a single utterance was spoken and can be categorized from a framework viewpoint towards building the language dependent or independent recognizer, such as Gaussian mixture modeling, single language phone or parallel phone recognition followed by language modeling, etc. In this paper, a flexible and efficient front-end architecture for language identification was proposed for speech segmentation and detection with mixed LID in a single utterance. More specially, this study focuses on: 1) adopting the Bayesian information criteria (BIC) with language-dependent acoustic features to divide input utterance into several acoustically-associated segments, 2) proposing a feature-discriminative and language dependent GMM using Latent Semantic Indexing approach to measure the strength of language for each segment, 3) integrating a VQ-based bi-gram language model into an MAP-based decision mechanism for language identification and 4) finally, applying a linear filtering and dynamic programming approaches for the precise language boundary estimation and smoothing. In order to evaluate our proposed approach, 5304 Mandarin-English mixed speech corpus (3 male speakers), 500 single language utterances with the duration of 3~5 seconds (Database 1), and 250 single language utterances with the duration of 15 seconds (Database 2) are collected. 80% corpus are used as the training database, 20% corpus are used as the testing database. Experimental results showed that the proposed mixed language decision mechanism achieved 74% accuracy and F value for the language boundary detection was 0.62. The LID rate for Database 1 and Database 2 were 0.79 and 0.90, respectively. Our proposed architecture outperforms than other well-established approaches. This study aims for multi-lingual speech recognition.
Lukon, Shelly Candita. "A machine-aided approach to intelligent index generation using natural language processing and latent semantic anaylsis to determine the contexts and relationships among words in a corpus /." 2006. http://etd1.library.duq.edu/theses/available/etd-11022006-145614/.
Повний текст джерелаCHEN, SHIH-HSUAN, and 陳世軒. "Integrating Latent Semantic Indexing and Clustering Algorithms to Develop a Long-Term Care 2.0 App based on Spark." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/wnw89d.
Повний текст джерела國立虎尾科技大學
資訊工程系碩士班
107
The main reason for the low usage rate of long-term care 2.0 is that most people do not understand the long-term care 2.0 system, and how to improve the awareness and usage of long-term care is the main purpose of this study. This study proposes to Integrating LSI, K-means and K-NN into Semantic Cloud Framework (ILKKSCF) to solve the above problems. The main of the system is to use the Latent Semantic Indexing (LSI), K-means and K-NN in Machine Learning and combine the Semantic Web, and to analyze Big Data based on cloud computing. Collect articles related to long-term care and divide them into words through Jieba. Matrix Market Format is used to transform words into matrix vectors, and TF-IDF is used to calculate the weights of words. Then LSI module is established to find out the hidden association between words, and then K-means algorithm is used to cluster the words. This research constructs a Long-Term Care Application Platform (LCAP), collects the user's problems about Long-Term 2.0 through LCAP, inputs them to the built LSI module, and classifies the problems by through K-NN algorithm. Finally, find out the matching articles through the Cosine Similarity to reply to the user. In addition, the user information collected by LCAP and the long-term care sites in Open Data are integrated into the long-term care sites and services recommended by Semantic Web. Due to the huge amount of data accumulated over the years, Spark is used to integrate Machine Learning and Semantic Web into cloud computing to improve the speed, and the K-value settings of Spark in LSI, K-means and cloud performance tests under different data volumes are compared. In this study, the accuracy and satisfaction of the survey are evaluated systematically. According to the results, LSI and K-means can meet the needs of the system at K=300, and the total satisfaction score of 5 is 4.15, which verifies the feasibility of ILKKSCF.
Rodrigues, Alexandre José Monteiro. "Recomendação de conteúdos : aplicação de agrupamento distribuído a conteúdos de TV." Master's thesis, 2010. http://hdl.handle.net/10216/63414.
Повний текст джерелаRodrigues, Alexandre José Monteiro. "Recomendação de conteúdos : aplicação de agrupamento distribuído a conteúdos de TV." Dissertação, 2010. http://hdl.handle.net/10216/63414.
Повний текст джерела