Search (46 results, page 1 of 3)

  • × theme_ss:"Data Mining"
  • × type_ss:"a"
  • × year_i:[2010 TO 2020}
  1. Wei, C.-P.; Lee, Y.-H.; Chiang, Y.-S.; Chen, C.-T.; Yang, C.C.C.: Exploiting temporal characteristics of features for effectively discovering event episodes from news corpora (2014) 0.01 
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    Abstract
    An organization performing environmental scanning generally monitors or tracks various events concerning its external environment. One of the major resources for environmental scanning is online news documents, which are readily accessible on news websites or infomediaries. However, the proliferation of the World Wide Web, which increases information sources and improves information circulation, has vastly expanded the amount of information to be scanned. Thus, it is essential to develop an effective event episode discovery mechanism to organize news documents pertaining to an event of interest. In this study, we propose two new metrics, Term Frequency × Inverse Document FrequencyTempo (TF×IDFTempo) and TF×Enhanced-IDFTempo, and develop a temporal-based event episode discovery (TEED) technique that uses the proposed metrics for feature selection and document representation. Using a traditional TF×IDF-based hierarchical agglomerative clustering technique as a performance benchmark, our empirical evaluation reveals that the proposed TEED technique outperforms its benchmark, as measured by cluster recall and cluster precision. In addition, the use of TF×Enhanced-IDFTempo significantly improves the effectiveness of event episode discovery when compared with the use of TF×IDFTempo.
    Source
    Journal of the Association for Information Science and Technology. 65(2014) no.3, S.621-634
  2. 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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    Abstract
    Numerous studies have explored the possibility of uncovering information from web search queries but few have examined the factors that affect web query data sources. We conducted a study that investigated this issue by comparing Google Trends and Baidu Index. Data from these two services are based on queries entered by users into Google and Baidu, two of the largest search engines in the world. We first compared the features and functions of the two services based on documents and extensive testing. We then carried out an empirical study that collected query volume data from the two sources. We found that data from both sources could be used to predict the quality of Chinese universities and companies. Despite the differences between the two services in terms of technology, such as differing methods of language processing, the search volume data from the two were highly correlated and combining the two data sources did not improve the predictive power of the data. However, there was a major difference between the two in terms of data availability. Baidu Index was able to provide more search volume data than Google Trends did. Our analysis showed that the disadvantage of Google Trends in this regard was due to Google's smaller user base in China. The implication of this finding goes beyond China. Google's user bases in many countries are smaller than that in China, so the search volume data related to those countries could result in the same issue as that related to China.
    Source
    Journal of the Association for Information Science and Technology. 66(2015) no.1, S.13-22
  3. Derek Doran, D.; Gokhale, S.S.: ¬A classification framework for web robots (2012) 0.01 
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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.
    Source
    Journal of the American Society for Information Science and Technology. 63(2012) no.12, S.2549-2554,
  4. Huvila, I.: Mining qualitative data on human information behaviour from the Web (2010) 0.01 
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    Abstract
    This paper discusses an approach of collecting qualitative data on human information behaviour that is based on mining web data using search engines. The approach is technically the same that has been used for some time in webometric research to make statistical inferences on web data, but the present paper shows how the same tools and data collecting methods can be used to gather data for qualitative data analysis on human information behaviour.
    Source
    Information und Wissen: global, sozial und frei? Proceedings des 12. Internationalen Symposiums für Informationswissenschaft (ISI 2011) ; Hildesheim, 9. - 11. März 2011. Hrsg.: J. Griesbaum, T. Mandl u. C. Womser-Hacker
  5. 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.
    Source
    Journal of the Association for Information Science and Technology. 68(2017) no.5, S.1323-1334
  6. Sarnikar, S.; Zhang, Z.; Zhao, J.L.: Query-performance prediction for effective query routing in domain-specific repositories (2014) 0.01 
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    Abstract
    The effective use of corporate memory is becoming increasingly important because every aspect of e-business requires access to information repositories. Unfortunately, less-than-satisfying effectiveness in state-of-the-art information-retrieval techniques is well known, even for some of the best search engines such as Google. In this study, the authors resolve this retrieval ineffectiveness problem by developing a new framework for predicting query performance, which is the first step toward better retrieval effectiveness. Specifically, they examine the relationship between query performance and query context. A query context consists of the query itself, the document collection, and the interaction between the two. The authors first analyze the characteristics of query context and develop various features for predicting query performance. Then, they propose a context-sensitive model for predicting query performance based on the characteristics of the query and the document collection. Finally, they validate this model with respect to five real-world collections of documents and demonstrate its utility in routing queries to the correct repository with high accuracy.
    Source
    Journal of the Association for Information Science and Technology. 65(2014) no.8, S.1597-1614
  7. 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.
    Source
    Next generation search engines: advanced models for information retrieval. Eds.: C. Jouis, u.a
  8. Berry, M.W.; Esau, R.; Kiefer, B.: ¬The use of text mining techniques in electronic discovery for legal matters (2012) 0.00 
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    Abstract
    Electronic discovery (eDiscovery) is the process of collecting and analyzing electronic documents to determine their relevance to a legal matter. Office technology has advanced and eased the requirements necessary to create a document. As such, the volume of data has outgrown the manual processes previously used to make relevance judgments. Methods of text mining and information retrieval have been put to use in eDiscovery to help tame the volume of data; however, the results have been uneven. This chapter looks at the historical bias of the collection process. The authors examine how tools like classifiers, latent semantic analysis, and non-negative matrix factorization deal with nuances of the collection process.
    Source
    Next generation search engines: advanced models for information retrieval. Eds.: C. Jouis, u.a
  9. Chen, Y.-L.; Liu, Y.-H.; Ho, W.-L.: ¬A text mining approach to assist the general public in the retrieval of legal documents (2013) 0.00 
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    Abstract
    Applying text mining techniques to legal issues has been an emerging research topic in recent years. Although some previous studies focused on assisting professionals in the retrieval of related legal documents, they did not take into account the general public and their difficulty in describing legal problems in professional legal terms. Because this problem has not been addressed by previous research, this study aims to design a text-mining-based method that allows the general public to use everyday vocabulary to search for and retrieve criminal judgments. The experimental results indicate that our method can help the general public, who are not familiar with professional legal terms, to acquire relevant criminal judgments more accurately and effectively.
    Source
    Journal of the American Society for Information Science and Technology. 64(2013) no.2, S.280-290
  10. Miao, Q.; Li, Q.; Zeng, D.: Fine-grained opinion mining by integrating multiple review sources (2010) 0.00 
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    Abstract
    With the rapid development of Web 2.0, online reviews have become extremely valuable sources for mining customers' opinions. Fine-grained opinion mining has attracted more and more attention of both applied and theoretical research. In this article, the authors study how to automatically mine product features and opinions from multiple review sources. Specifically, they propose an integration strategy to solve the issue. Within the integration strategy, the authors mine domain knowledge from semistructured reviews and then exploit the domain knowledge to assist product feature extraction and sentiment orientation identification from unstructured reviews. Finally, feature-opinion tuples are generated. Experimental results on real-world datasets show that the proposed approach is effective.
    Source
    Journal of the American Society for Information Science and Technology. 61(2010) no.11, S.2288-2299
  11. Liu, X.; Yu, S.; Janssens, F.; Glänzel, W.; Moreau, Y.; Moor, B.de: Weighted hybrid clustering by combining text mining and bibliometrics on a large-scale journal database (2010) 0.00 
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    Abstract
    We propose a new hybrid clustering framework to incorporate text mining with bibliometrics in journal set analysis. The framework integrates two different approaches: clustering ensemble and kernel-fusion clustering. To improve the flexibility and the efficiency of processing large-scale data, we propose an information-based weighting scheme to leverage the effect of multiple data sources in hybrid clustering. Three different algorithms are extended by the proposed weighting scheme and they are employed on a large journal set retrieved from the Web of Science (WoS) database. The clustering performance of the proposed algorithms is systematically evaluated using multiple evaluation methods, and they were cross-compared with alternative methods. Experimental results demonstrate that the proposed weighted hybrid clustering strategy is superior to other methods in clustering performance and efficiency. The proposed approach also provides a more refined structural mapping of journal sets, which is useful for monitoring and detecting new trends in different scientific fields.
    Source
    Journal of the American Society for Information Science and Technology. 61(2010) no.6, S.1105-1119
  12. Zhang, Z.; Li, Q.; Zeng, D.; Ga, H.: Extracting evolutionary communities in community question answering (2014) 0.00 
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    Abstract
    With the rapid growth of Web 2.0, community question answering (CQA) has become a prevalent information seeking channel, in which users form interactive communities by posting questions and providing answers. Communities may evolve over time, because of changes in users' interests, activities, and new users joining the network. To better understand user interactions in CQA communities, it is necessary to analyze the community structures and track community evolution over time. Existing work in CQA focuses on question searching or content quality detection, and the important problems of community extraction and evolutionary pattern detection have not been studied. In this article, we propose a probabilistic community model (PCM) to extract overlapping community structures and capture their evolution patterns in CQA. The empirical results show that our algorithm appears to improve the community extraction quality. We show empirically, using the iPhone data set, that interesting community evolution patterns can be discovered, with each evolution pattern reflecting the variation of users' interests over time. Our analysis suggests that individual users could benefit to gain comprehensive information from tracking the transition of products. We also show that the communities provide a decision-making basis for business.
    Source
    Journal of the Association for Information Science and Technology. 65(2014) no.6, S.1170-1186
  13. Drees, B.: Text und data mining : Herausforderungen und Möglichkeiten für Bibliotheken (2016) 0.00 
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    Content
    Vgl.: http://journals.ub.uni-heidelberg.de/index.php/bibliothek/article/view/33691/pdf.
    Source
    Perspektive Bibliothek. 5(2016) H.1, S.49-73
  14. Nohr, H.: Big Data im Lichte der EU-Datenschutz-Grundverordnung (2017) 0.00 
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    Content
    Vgl.: JurPC Web-Dok. 111/2017 - DOI 10.7328/jurpcb2017328111.
  15. Hallonsten, O.; Holmberg, D.: Analyzing structural stratification in the Swedish higher education system : data contextualization with policy-history analysis (2013) 0.00 
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    Date
    22. 3.2013 19:43:01
    Source
    Journal of the American Society for Information Science and Technology. 64(2013) no.3, S.574-586
  16. Fonseca, F.; Marcinkowski, M.; Davis, C.: Cyber-human systems of thought and understanding (2019) 0.00 
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    Date
    7. 3.2019 16:32:22
    Source
    Journal of the Association for Information Science and Technology. 70(2019) no.4, S.402-411
  17. Kraker, P.; Kittel, C,; Enkhbayar, A.: Open Knowledge Maps : creating a visual interface to the world's scientific knowledge based on natural language processing (2016) 0.00 
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    Abstract
    The goal of Open Knowledge Maps is to create a visual interface to the world's scientific knowledge. The base for this visual interface consists of so-called knowledge maps, which enable the exploration of existing knowledge and the discovery of new knowledge. Our open source knowledge mapping software applies a mixture of summarization techniques and similarity measures on article metadata, which are iteratively chained together. After processing, the representation is saved in a database for use in a web visualization. In the future, we want to create a space for collective knowledge mapping that brings together individuals and communities involved in exploration and discovery. We want to enable people to guide each other in their discovery by collaboratively annotating and modifying the automatically created maps.
  18. Wongthontham, P.; Abu-Salih, B.: Ontology-based approach for semantic data extraction from social big data : state-of-the-art and research directions (2018) 0.00 
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    Theme
    Semantisches Umfeld in Indexierung u. Retrieval
  19. Blake, C.: Text mining (2011) 0.00 
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    Source
    Annual review of information science and technology. 45(2011) no.1, S.121-155
  20. O'Brien, H.L.; Lebow, M.: Mixed-methods approach to measuring user experience in online news interactions (2013) 0.00 
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    Abstract
    When it comes to evaluating online information experiences, what metrics matter? We conducted a study in which 30 people browsed and selected content within an online news website. Data collected included psychometric scales (User Engagement, Cognitive Absorption, System Usability Scales), self-reported interest in news content, and performance metrics (i.e., reading time, browsing time, total time, number of pages visited, and use of recommended links); a subset of the participants had their physiological responses recorded during the interaction (i.e., heart rate, electrodermal activity, electrocmytogram). Findings demonstrated the concurrent validity of the psychometric scales and interest ratings and revealed that increased time on tasks, number of pages visited, and use of recommended links were not necessarily indicative of greater self-reported engagement, cognitive absorption, or perceived usability. Positive ratings of news content were associated with lower physiological activity. The implications of this research are twofold. First, we propose that user experience is a useful framework for studying online information interactions and will result in a broader conceptualization of information interaction and its evaluation. Second, we advocate a mixed-methods approach to measurement that employs a suite of metrics capable of capturing the pragmatic (e.g., usability) and hedonic (e.g., fun, engagement) aspects of information interactions. We underscore the importance of using multiple measures in information research, because our results emphasize that performance and physiological data must be interpreted in the context of users' subjective experiences.
    Source
    Journal of the American Society for Information Science and Technology. 64(2013) no.8, S.1543-1556

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