Search (22 results, page 1 of 2)

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  • × theme_ss:"Data Mining"
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  1. Chowdhury, G.G.: Template mining for information extraction from digital documents (1999) 0.02 
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    Date
    2. 4.2000 18:01:22
  2. Derek Doran, D.; Gokhale, S.S.: ¬A classification framework for web robots (2012) 0.02 
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    Abstract
    The behavior of modern web robots varies widely when they crawl for different purposes. In this article, we present a framework to classify these web robots from two orthogonal perspectives, namely, their functionality and the types of resources they consume. Applying the classification framework to a year-long access log from the UConn SoE web server, we present trends that point to significant differences in their crawling behavior.
  3. Lowe, D.B.; Dollinger, I.; Koster, T.; Herbert, B.E.: Text mining for type of research classification (2021) 0.02 
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    Abstract
    This project brought together undergraduate students in Computer Science with librarians to mine abstracts of articles from the Texas A&M University Libraries' institutional repository, OAKTrust, in order to probe the creation of new metadata to improve discovery and use. The mining operation task consisted simply of classifying the articles into two categories of research type: basic research ("for understanding," "curiosity-based," or "knowledge-based") and applied research ("use-based"). These categories are fundamental especially for funders but are also important to researchers. The mining-to-classification steps took several iterations, but ultimately, we achieved good results with the toolkit BERT (Bidirectional Encoder Representations from Transformers). The project and its workflows represent a preview of what may lie ahead in the future of crafting metadata using text mining techniques to enhance discoverability.
    Source
    Cataloging and classification quarterly. 59(2021) no.8, p.815-834
  4. Haravu, L.J.; Neelameghan, A.: Text mining and data mining in knowledge organization and discovery : the making of knowledge-based products (2003) 0.01 
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    Abstract
    Discusses the importance of knowledge organization in the context of the information overload caused by the vast quantities of data and information accessible on internal and external networks of an organization. Defines the characteristics of a knowledge-based product. Elaborates on the techniques and applications of text mining in developing knowledge products. Presents two approaches, as case studies, to the making of knowledge products: (1) steps and processes in the planning, designing and development of a composite multilingual multimedia CD product, with the potential international, inter-cultural end users in view, and (2) application of natural language processing software in text mining. Using a text mining software, it is possible to link concept terms from a processed text to a related thesaurus, glossary, schedules of a classification scheme, and facet structured subject representations. Concludes that the products of text mining and data mining could be made more useful if the features of a faceted scheme for subject classification are incorporated into text mining techniques and products.
    Content
    Beitrag eines Themenheftes "Knowledge organization and classification in international information retrieval"
    Source
    Cataloging and classification quarterly. 37(2003) nos.1/2, S.96-114
  5. Bell, D.A.; Guan, J.W.: Computational methods for rough classification and discovery (1998) 0.01 
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    Abstract
    Rough set theory is a mathematical tool to deal with vagueness and uncertainty. To apply the theory, it needs to be associated with efficient and effective computational methods. A relation can be used to represent a decison table for use in decision making. By using this kind of table, rough set theory can be applied successfully to rough classification and knowledge discovery. Presents computational methods for using rough sets to identify classes in datasets, finding dependencies in relations, and discovering rules which are hidden in databases. Illustrates the methods with a running example from a database of car test results
  6. Short, M.: Text mining and subject analysis for fiction; or, using machine learning and information extraction to assign subject headings to dime novels (2019) 0.01 
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    Abstract
    This article describes multiple experiments in text mining at Northern Illinois University that were undertaken to improve the efficiency and accuracy of cataloging. It focuses narrowly on subject analysis of dime novels, a format of inexpensive fiction that was popular in the United States between 1860 and 1915. NIU holds more than 55,000 dime novels in its collections, which it is in the process of comprehensively digitizing. Classification, keyword extraction, named-entity recognition, clustering, and topic modeling are discussed as means of assigning subject headings to improve their discoverability by researchers and to increase the productivity of digitization workflows.
    Source
    Cataloging and classification quarterly. 57(2019) no.5, S.315-336
  7. Matson, L.D.; Bonski, D.J.: Do digital libraries need librarians? (1997) 0.01 
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    Date
    22.11.1998 18:57:22
  8. Amir, A.; Feldman, R.; Kashi, R.: ¬A new and versatile method for association generation (1997) 0.01 
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    Source
    Information systems. 22(1997) nos.5/6, S.333-347
  9. Ayadi, H.; Torjmen-Khemakhem, M.; Daoud, M.; Huang, J.X.; Jemaa, M.B.: Mining correlations between medically dependent features and image retrieval models for query classification (2017) 0.01 
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    Abstract
    The abundance of medical resources has encouraged the development of systems that allow for efficient searches of information in large medical image data sets. State-of-the-art image retrieval models are classified into three categories: content-based (visual) models, textual models, and combined models. Content-based models use visual features to answer image queries, textual image retrieval models use word matching to answer textual queries, and combined image retrieval models, use both textual and visual features to answer queries. Nevertheless, most of previous works in this field have used the same image retrieval model independently of the query type. In this article, we define a list of generic and specific medical query features and exploit them in an association rule mining technique to discover correlations between query features and image retrieval models. Based on these rules, we propose to use an associative classifier (NaiveClass) to find the best suitable retrieval model given a new textual query. We also propose a second associative classifier (SmartClass) to select the most appropriate default class for the query. Experiments are performed on Medical ImageCLEF queries from 2008 to 2012 to evaluate the impact of the proposed query features on the classification performance. The results show that combining our proposed specific and generic query features is effective in query classification.
  10. Deogun, J.S.: Feature selection and effective classifiers (1998) 0.01 
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    Abstract
    Develops and analyzes 4 algorithms for feature selection in the context of rough set methodology. Develops the notion of accuracy of classification that can be used for upper or lower classification methods and defines the feature selection problem. Presents a discussion of upper classifiers and develops 4 features selection heuristics and discusses the family of stepwise backward selection algorithms. Analyzes the worst case time complexity in all algorithms presented. Discusses details of the experiments and results of using a family of stepwise backward selection learning data sets and a duodenal ulcer data set. Includes the experimental setup and results of comparison of lower classifiers and upper classiers on the duodenal ulcer data set. Discusses exteded decision tables
  11. Biskri, I.; Rompré, L.: Using association rules for query reformulation (2012) 0.01 
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    Abstract
    In this paper the authors will present research on the combination of two methods of data mining: text classification and maximal association rules. Text classification has been the focus of interest of many researchers for a long time. However, the results take the form of lists of words (classes) that people often do not know what to do with. The use of maximal association rules induced a number of advantages: (1) the detection of dependencies and correlations between the relevant units of information (words) of different classes, (2) the extraction of hidden knowledge, often relevant, from a large volume of data. The authors will show how this combination can improve the process of information retrieval.
  12. Hofstede, A.H.M. ter; Proper, H.A.; Van der Weide, T.P.: Exploiting fact verbalisation in conceptual information modelling (1997) 0.01 
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    Source
    Information systems. 22(1997) nos.5/6, S.349-385
  13. Fayyad, U.M.; Djorgovski, S.G.; Weir, N.: From digitized images to online catalogs : data ming a sky server (1996) 0.01 
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    Abstract
    Offers a data mining approach based on machine learning classification methods to the problem of automated cataloguing of online databases of digital images resulting from sky surveys. The SKICAT system automates the reduction and analysis of 3 terabytes of images expected to contain about 2 billion sky objects. It offers a solution to problems associated with the analysis of large data sets in science
  14. Nicholson, S.: Bibliomining for automated collection development in a digital library setting : using data mining to discover Web-based scholarly research works (2003) 0.01 
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    Abstract
    This research creates an intelligent agent for automated collection development in a digital library setting. It uses a predictive model based an facets of each Web page to select scholarly works. The criteria came from the academic library selection literature, and a Delphi study was used to refine the list to 41 criteria. A Perl program was designed to analyze a Web page for each criterion and applied to a large collection of scholarly and nonscholarly Web pages. Bibliomining, or data mining for libraries, was then used to create different classification models. Four techniques were used: logistic regression, nonparametric discriminant analysis, classification trees, and neural networks. Accuracy and return were used to judge the effectiveness of each model an test datasets. In addition, a set of problematic pages that were difficult to classify because of their similarity to scholarly research was gathered and classified using the models. The resulting models could be used in the selection process to automatically create a digital library of Webbased scholarly research works. In addition, the technique can be extended to create a digital library of any type of structured electronic information.
  15. Ma, Z.; Sun, A.; Cong, G.: On predicting the popularity of newly emerging hashtags in Twitter (2013) 0.01 
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    Abstract
    Because of Twitter's popularity and the viral nature of information dissemination on Twitter, predicting which Twitter topics will become popular in the near future becomes a task of considerable economic importance. Many Twitter topics are annotated by hashtags. In this article, we propose methods to predict the popularity of new hashtags on Twitter by formulating the problem as a classification task. We use five standard classification models (i.e., Naïve bayes, k-nearest neighbors, decision trees, support vector machines, and logistic regression) for prediction. The main challenge is the identification of effective features for describing new hashtags. We extract 7 content features from a hashtag string and the collection of tweets containing the hashtag and 11 contextual features from the social graph formed by users who have adopted the hashtag. We conducted experiments on a Twitter data set consisting of 31 million tweets from 2 million Singapore-based users. The experimental results show that the standard classifiers using the extracted features significantly outperform the baseline methods that do not use these features. Among the five classifiers, the logistic regression model performs the best in terms of the Micro-F1 measure. We also observe that contextual features are more effective than content features.
  16. Hallonsten, O.; Holmberg, D.: Analyzing structural stratification in the Swedish higher education system : data contextualization with policy-history analysis (2013) 0.01 
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    Date
    22. 3.2013 19:43:01
  17. Vaughan, L.; Chen, Y.: Data mining from web search queries : a comparison of Google trends and Baidu index (2015) 0.01 
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    Source
    Journal of the Association for Information Science and Technology. 66(2015) no.1, S.13-22
  18. Fonseca, F.; Marcinkowski, M.; Davis, C.: Cyber-human systems of thought and understanding (2019) 0.01 
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    Date
    7. 3.2019 16:32:22
  19. Benoit, G.: Data mining (2002) 0.01 
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    Abstract
    Data mining (DM) is a multistaged process of extracting previously unanticipated knowledge from large databases, and applying the results to decision making. Data mining tools detect patterns from the data and infer associations and rules from them. The extracted information may then be applied to prediction or classification models by identifying relations within the data records or between databases. Those patterns and rules can then guide decision making and forecast the effects of those decisions. However, this definition may be applied equally to "knowledge discovery in databases" (KDD). Indeed, in the recent literature of DM and KDD, a source of confusion has emerged, making it difficult to determine the exact parameters of both. KDD is sometimes viewed as the broader discipline, of which data mining is merely a component-specifically pattern extraction, evaluation, and cleansing methods (Raghavan, Deogun, & Sever, 1998, p. 397). Thurasingham (1999, p. 2) remarked that "knowledge discovery," "pattern discovery," "data dredging," "information extraction," and "knowledge mining" are all employed as synonyms for DM. Trybula, in his ARIST chapter an text mining, observed that the "existing work [in KDD] is confusing because the terminology is inconsistent and poorly defined.
  20. Pons-Porrata, A.; Berlanga-Llavori, R.; Ruiz-Shulcloper, J.: Topic discovery based on text mining techniques (2007) 0.01 
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    Abstract
    In this paper, we present a topic discovery system aimed to reveal the implicit knowledge present in news streams. This knowledge is expressed as a hierarchy of topic/subtopics, where each topic contains the set of documents that are related to it and a summary extracted from these documents. Summaries so built are useful to browse and select topics of interest from the generated hierarchies. Our proposal consists of a new incremental hierarchical clustering algorithm, which combines both partitional and agglomerative approaches, taking the main benefits from them. Finally, a new summarization method based on Testor Theory has been proposed to build the topic summaries. Experimental results in the TDT2 collection demonstrate its usefulness and effectiveness not only as a topic detection system, but also as a classification and summarization tool.

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