Search (126 results, page 1 of 7)

  • × language_ss:"e"
  • × theme_ss:"Data Mining"
  1. Chen, Z.: Knowledge discovery and system-user partnership : on a production 'adversarial partnership' approach (1994) 0.02 
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    Abstract
    Examines the relationship between systems and users from the knowledge discovery in databases or data mining perspecitives. A comprehensive study on knowledge discovery in human computer symbiosis is needed. Proposes a database-user adversarial partnership, which is general enough to cover knowledge discovery and security of issues related to databases and their users. It can be further generalized into system-user adversarial paertnership. Discusses opportunities provided by knowledge discovery techniques and potential social implications
    Type
    a
  2. Kong, S.; Ye, F.; Feng, L.; Zhao, Z.: Towards the prediction problems of bursting hashtags on Twitter (2015) 0.02 
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    Abstract
    Hundreds of thousands of hashtags are generated every day on Twitter. Only a few will burst and become trending topics. In this article, we provide the definition of a bursting hashtag and conduct a systematic study of a series of challenging prediction problems that span the entire life cycles of bursting hashtags. Around the problem of "how to build a system to predict bursting hashtags," we explore different types of features and present machine learning solutions. On real data sets from Twitter, experiments are conducted to evaluate the effectiveness of the proposed solutions and the contributions of features.
    Type
    a
  3. 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
    Type
    a
  4. Sarnikar, S.; Zhang, Z.; Zhao, J.L.: Query-performance prediction for effective query routing in domain-specific repositories (2014) 0.02 
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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.
    Type
    a
  5. KDD : techniques and applications (1998) 0.01 
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    Footnote
    A special issue of selected papers from the Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'97), held Singapore, 22-23 Feb 1997
  6. 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.
    Type
    a
  7. Li, D.; Tang, J.; Ding, Y.; Shuai, X.; Chambers, T.; Sun, G.; Luo, Z.; Zhang, J.: Topic-level opinion influence model (TOIM) : an investigation using tencent microblogging (2015) 0.01 
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    Abstract
    Text mining has been widely used in multiple types of user-generated data to infer user opinion, but its application to microblogging is difficult because text messages are short and noisy, providing limited information about user opinion. Given that microblogging users communicate with each other to form a social network, we hypothesize that user opinion is influenced by its neighbors in the network. In this paper, we infer user opinion on a topic by combining two factors: the user's historical opinion about relevant topics and opinion influence from his/her neighbors. We thus build a topic-level opinion influence model (TOIM) by integrating both topic factor and opinion influence factor into a unified probabilistic model. We evaluate our model in one of the largest microblogging sites in China, Tencent Weibo, and the experiments show that TOIM outperforms baseline methods in opinion inference accuracy. Moreover, incorporating indirect influence further improves inference recall and f1-measure. Finally, we demonstrate some useful applications of TOIM in analyzing users' behaviors in Tencent Weibo.
    Type
    a
  8. Suakkaphong, N.; Zhang, Z.; Chen, H.: Disease named entity recognition using semisupervised learning and conditional random fields (2011) 0.01 
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    Abstract
    Information extraction is an important text-mining task that aims at extracting prespecified types of information from large text collections and making them available in structured representations such as databases. In the biomedical domain, information extraction can be applied to help biologists make the most use of their digital-literature archives. Currently, there are large amounts of biomedical literature that contain rich information about biomedical substances. Extracting such knowledge requires a good named entity recognition technique. In this article, we combine conditional random fields (CRFs), a state-of-the-art sequence-labeling algorithm, with two semisupervised learning techniques, bootstrapping and feature sampling, to recognize disease names from biomedical literature. Two data-processing strategies for each technique also were analyzed: one sequentially processing unlabeled data partitions and another one processing unlabeled data partitions in a round-robin fashion. The experimental results showed the advantage of semisupervised learning techniques given limited labeled training data. Specifically, CRFs with bootstrapping implemented in sequential fashion outperformed strictly supervised CRFs for disease name recognition. The project was supported by NIH/NLM Grant R33 LM07299-01, 2002-2005.
    Type
    a
  9. Zhang, Z.; Li, Q.; Zeng, D.; Ga, H.: Extracting evolutionary communities in community question answering (2014) 0.01 
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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.
    Type
    a
  10. Amir, A.; Feldman, R.; Kashi, R.: ¬A new and versatile method for association generation (1997) 0.01 
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    Abstract
    Current algorithms for finding associations among the attributes describing data in a database have a number of shortcomings. Presents a novel method for association generation, that answers all desiderata. The method is different from all existing algorithms and especially suitable to textual databases with binary attributes. Uses subword trees for quick indexing into the required database statistics. Tests the algorithm on the Reuters-22173 database with satisfactory results
    Source
    Information systems. 22(1997) nos.5/6, S.333-347
    Type
    a
  11. Matson, L.D.; Bonski, D.J.: Do digital libraries need librarians? (1997) 0.01 
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    Abstract
    Defines digital libraries and discusses the effects of new technology on librarians. Examines the different viewpoints of librarians and information technologists on digital libraries. Describes the development of a digital library at the National Drug Intelligence Center, USA, which was carried out in collaboration with information technology experts. The system is based on Web enabled search technology to find information, data visualization and data mining to visualize it and use of SGML as an information standard to store it
    Date
    22.11.1998 18:57:22
    Type
    a
  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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    Abstract
    Focuses on the information modelling side of conceptual modelling. Deals with the exploitation of fact verbalisations after finishing the actual information system. Verbalisations are used as input for the design of the so-called information model. Exploits these verbalisation in 4 directions: considers their use for a conceptual query language, the verbalisation of instances, the description of the contents of a database and for the verbalisation of queries in a computer supported query environment. Provides an example session with an envisioned tool for end user query formulations that exploits the verbalisation
    Source
    Information systems. 22(1997) nos.5/6, S.349-385
    Type
    a
  13. Heyer, G.; Quasthoff, U.; Wittig, T.: Text Mining : Wissensrohstoff Text. Konzepte, Algorithmen, Ergebnisse (2006) 0.01 
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    Abstract
    Ein großer Teil des Weltwissens befindet sich in Form digitaler Texte im Internet oder in Intranets. Heutige Suchmaschinen nutzen diesen Wissensrohstoff nur rudimentär: Sie können semantische Zusammen-hänge nur bedingt erkennen. Alle warten auf das semantische Web, in dem die Ersteller von Text selbst die Semantik einfügen. Das wird aber noch lange dauern. Es gibt jedoch eine Technologie, die es bereits heute ermöglicht semantische Zusammenhänge in Rohtexten zu analysieren und aufzubereiten. Das Forschungsgebiet "Text Mining" ermöglicht es mit Hilfe statistischer und musterbasierter Verfahren, Wissen aus Texten zu extrahieren, zu verarbeiten und zu nutzen. Hier wird die Basis für die Suchmaschinen der Zukunft gelegt. Das erste deutsche Lehrbuch zu einer bahnbrechenden Technologie: Text Mining: Wissensrohstoff Text Konzepte, Algorithmen, Ergebnisse Ein großer Teil des Weltwissens befindet sich in Form digitaler Texte im Internet oder in Intranets. Heutige Suchmaschinen nutzen diesen Wissensrohstoff nur rudimentär: Sie können semantische Zusammen-hänge nur bedingt erkennen. Alle warten auf das semantische Web, in dem die Ersteller von Text selbst die Semantik einfügen. Das wird aber noch lange dauern. Es gibt jedoch eine Technologie, die es bereits heute ermöglicht semantische Zusammenhänge in Rohtexten zu analysieren und aufzubereiten. Das For-schungsgebiet "Text Mining" ermöglicht es mit Hilfe statistischer und musterbasierter Verfahren, Wissen aus Texten zu extrahieren, zu verarbeiten und zu nutzen. Hier wird die Basis für die Suchmaschinen der Zukunft gelegt. Was fällt Ihnen bei dem Wort "Stich" ein? Die einen denken an Tennis, die anderen an Skat. Die verschiedenen Zusammenhänge können durch Text Mining automatisch ermittelt und in Form von Wortnetzen dargestellt werden. Welche Begriffe stehen am häufigsten links und rechts vom Wort "Festplatte"? Welche Wortformen und Eigennamen treten seit 2001 neu in der deutschen Sprache auf? Text Mining beantwortet diese und viele weitere Fragen. Tauchen Sie mit diesem Lehrbuch ein in eine neue, faszinierende Wissenschaftsdisziplin und entdecken Sie neue, bisher unbekannte Zusammenhänge und Sichtweisen. Sehen Sie, wie aus dem Wissensrohstoff Text Wissen wird! Dieses Lehrbuch richtet sich sowohl an Studierende als auch an Praktiker mit einem fachlichen Schwerpunkt in der Informatik, Wirtschaftsinformatik und/oder Linguistik, die sich über die Grundlagen, Verfahren und Anwendungen des Text Mining informieren möchten und Anregungen für die Implementierung eigener Anwendungen suchen. Es basiert auf Arbeiten, die während der letzten Jahre an der Abteilung Automatische Sprachverarbeitung am Institut für Informatik der Universität Leipzig unter Leitung von Prof. Dr. Heyer entstanden sind. Eine Fülle praktischer Beispiele von Text Mining-Konzepten und -Algorithmen verhelfen dem Leser zu einem umfassenden, aber auch detaillierten Verständnis der Grundlagen und Anwendungen des Text Mining. Folgende Themen werden behandelt: Wissen und Text Grundlagen der Bedeutungsanalyse Textdatenbanken Sprachstatistik Clustering Musteranalyse Hybride Verfahren Beispielanwendungen Anhänge: Statistik und linguistische Grundlagen 360 Seiten, 54 Abb., 58 Tabellen und 95 Glossarbegriffe Mit kostenlosen e-learning-Kurs "Schnelleinstieg: Sprachstatistik" Zusätzlich zum Buch gibt es in Kürze einen Online-Zertifikats-Kurs mit Mentor- und Tutorunterstützung.
  14. Fonseca, F.; Marcinkowski, M.; Davis, C.: Cyber-human systems of thought and understanding (2019) 0.01 
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    Abstract
    The present challenge faced by scientists working with Big Data comes in the overwhelming volume and level of detail provided by current data sets. Exceeding traditional empirical approaches, Big Data opens a new perspective on scientific work in which data comes to play a role in the development of the scientific problematic to be developed. Addressing this reconfiguration of our relationship with data through readings of Wittgenstein, Macherey, and Popper, we propose a picture of science that encourages scientists to engage with the data in a direct way, using the data itself as an instrument for scientific investigation. Using GIS as a theme, we develop the concept of cyber-human systems of thought and understanding to bridge the divide between representative (theoretical) thinking and (non-theoretical) data-driven science. At the foundation of these systems, we invoke the concept of the "semantic pixel" to establish a logical and virtual space linking data and the work of scientists. It is with this discussion of the relationship between analysts in their pursuit of knowledge and the rise of Big Data that this present discussion of the philosophical foundations of Big Data addresses the central questions raised by social informatics research.
    Date
    7. 3.2019 16:32:22
    Type
    a
  15. 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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    Abstract
    20th century massification of higher education and research in academia is said to have produced structurally stratified higher education systems in many countries. Most manifestly, the research mission of universities appears to be divisive. Authors have claimed that the Swedish system, while formally unified, has developed into a binary state, and statistics seem to support this conclusion. This article makes use of a comprehensive statistical data source on Swedish higher education institutions to illustrate stratification, and uses literature on Swedish research policy history to contextualize the statistics. Highlighting the opportunities as well as constraints of the data, the article argues that there is great merit in combining statistics with a qualitative analysis when studying the structural characteristics of national higher education systems. Not least the article shows that it is an over-simplification to describe the Swedish system as binary; the stratification is more complex. On basis of the analysis, the article also argues that while global trends certainly influence national developments, higher education systems have country-specific features that may enrich the understanding of how systems evolve and therefore should be analyzed as part of a broader study of the increasingly globalized academic system.
    Date
    22. 3.2013 19:43:01
    Type
    a
  16. 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
    Type
    a
  17. Information visualization in data mining and knowledge discovery (2002) 0.00 
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    Date
    23. 3.2008 19:10:22
    Footnote
    Rez. in: JASIST 54(2003) no.9, S.905-906 (C.A. Badurek): "Visual approaches for knowledge discovery in very large databases are a prime research need for information scientists focused an extracting meaningful information from the ever growing stores of data from a variety of domains, including business, the geosciences, and satellite and medical imagery. This work presents a summary of research efforts in the fields of data mining, knowledge discovery, and data visualization with the goal of aiding the integration of research approaches and techniques from these major fields. The editors, leading computer scientists from academia and industry, present a collection of 32 papers from contributors who are incorporating visualization and data mining techniques through academic research as well application development in industry and government agencies. Information Visualization focuses upon techniques to enhance the natural abilities of humans to visually understand data, in particular, large-scale data sets. It is primarily concerned with developing interactive graphical representations to enable users to more intuitively make sense of multidimensional data as part of the data exploration process. It includes research from computer science, psychology, human-computer interaction, statistics, and information science. Knowledge Discovery in Databases (KDD) most often refers to the process of mining databases for previously unknown patterns and trends in data. Data mining refers to the particular computational methods or algorithms used in this process. The data mining research field is most related to computational advances in database theory, artificial intelligence and machine learning. This work compiles research summaries from these main research areas in order to provide "a reference work containing the collection of thoughts and ideas of noted researchers from the fields of data mining and data visualization" (p. 8). It addresses these areas in three main sections: the first an data visualization, the second an KDD and model visualization, and the last an using visualization in the knowledge discovery process. The seven chapters of Part One focus upon methodologies and successful techniques from the field of Data Visualization. Hoffman and Grinstein (Chapter 2) give a particularly good overview of the field of data visualization and its potential application to data mining. An introduction to the terminology of data visualization, relation to perceptual and cognitive science, and discussion of the major visualization display techniques are presented. Discussion and illustration explain the usefulness and proper context of such data visualization techniques as scatter plots, 2D and 3D isosurfaces, glyphs, parallel coordinates, and radial coordinate visualizations. Remaining chapters present the need for standardization of visualization methods, discussion of user requirements in the development of tools, and examples of using information visualization in addressing research problems.
    In 13 chapters, Part Two provides an introduction to KDD, an overview of data mining techniques, and examples of the usefulness of data model visualizations. The importance of visualization throughout the KDD process is stressed in many of the chapters. In particular, the need for measures of visualization effectiveness, benchmarking for identifying best practices, and the use of standardized sample data sets is convincingly presented. Many of the important data mining approaches are discussed in this complementary context. Cluster and outlier detection, classification techniques, and rule discovery algorithms are presented as the basic techniques common to the KDD process. The potential effectiveness of using visualization in the data modeling process are illustrated in chapters focused an using visualization for helping users understand the KDD process, ask questions and form hypotheses about their data, and evaluate the accuracy and veracity of their results. The 11 chapters of Part Three provide an overview of the KDD process and successful approaches to integrating KDD, data mining, and visualization in complementary domains. Rhodes (Chapter 21) begins this section with an excellent overview of the relation between the KDD process and data mining techniques. He states that the "primary goals of data mining are to describe the existing data and to predict the behavior or characteristics of future data of the same type" (p. 281). These goals are met by data mining tasks such as classification, regression, clustering, summarization, dependency modeling, and change or deviation detection. Subsequent chapters demonstrate how visualization can aid users in the interactive process of knowledge discovery by graphically representing the results from these iterative tasks. Finally, examples of the usefulness of integrating visualization and data mining tools in the domain of business, imagery and text mining, and massive data sets are provided. This text concludes with a thorough and useful 17-page index and lengthy yet integrating 17-page summary of the academic and industrial backgrounds of the contributing authors. A 16-page set of color inserts provide a better representation of the visualizations discussed, and a URL provided suggests that readers may view all the book's figures in color on-line, although as of this submission date it only provides access to a summary of the book and its contents. The overall contribution of this work is its focus an bridging two distinct areas of research, making it a valuable addition to the Morgan Kaufmann Series in Database Management Systems. The editors of this text have met their main goal of providing the first textbook integrating knowledge discovery, data mining, and visualization. Although it contributes greatly to our under- standing of the development and current state of the field, a major weakness of this text is that there is no concluding chapter to discuss the contributions of the sum of these contributed papers or give direction to possible future areas of research. "Integration of expertise between two different disciplines is a difficult process of communication and reeducation. Integrating data mining and visualization is particularly complex because each of these fields in itself must draw an a wide range of research experience" (p. 300). Although this work contributes to the crossdisciplinary communication needed to advance visualization in KDD, a more formal call for an interdisciplinary research agenda in a concluding chapter would have provided a more satisfying conclusion to a very good introductory text.
    With contributors almost exclusively from the computer science field, the intended audience of this work is heavily slanted towards a computer science perspective. However, it is highly readable and provides introductory material that would be useful to information scientists from a variety of domains. Yet, much interesting work in information visualization from other fields could have been included giving the work more of an interdisciplinary perspective to complement their goals of integrating work in this area. Unfortunately, many of the application chapters are these, shallow, and lack complementary illustrations of visualization techniques or user interfaces used. However, they do provide insight into the many applications being developed in this rapidly expanding field. The authors have successfully put together a highly useful reference text for the data mining and information visualization communities. Those interested in a good introduction and overview of complementary research areas in these fields will be satisfied with this collection of papers. The focus upon integrating data visualization with data mining complements texts in each of these fields, such as Advances in Knowledge Discovery and Data Mining (Fayyad et al., MIT Press) and Readings in Information Visualization: Using Vision to Think (Card et. al., Morgan Kauffman). This unique work is a good starting point for future interaction between researchers in the fields of data visualization and data mining and makes a good accompaniment for a course focused an integrating these areas or to the main reference texts in these fields."
  18. Fong, A.C.M.: Mining a Web citation database for document clustering (2002) 0.00 
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  19. Ebrahimi, M.; ShafieiBavani, E.; Wong, R.; Chen, F.: Twitter user geolocation by filtering of highly mentioned users (2018) 0.00 
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    Abstract
    Geolocated social media data provide a powerful source of information about places and regional human behavior. Because only a small amount of social media data have been geolocation-annotated, inference techniques play a substantial role to increase the volume of annotated data. Conventional research in this area has been based on the text content of posts from a given user or the social network of the user, with some recent crossovers between the text- and network-based approaches. This paper proposes a novel approach to categorize highly-mentioned users (celebrities) into Local and Global types, and consequently use Local celebrities as location indicators. A label propagation algorithm is then used over the refined social network for geolocation inference. Finally, we propose a hybrid approach by merging a text-based method as a back-off strategy into our network-based approach. Empirical experiments over three standard Twitter benchmark data sets demonstrate that our approach outperforms state-of-the-art user geolocation methods.
    Type
    a
  20. Ekbia, H.; Mattioli, M.; Kouper, I.; Arave, G.; Ghazinejad, A.; Bowman, T.; Suri, V.R.; Tsou, A.; Weingart, S.; Sugimoto, C.R.: Big data, bigger dilemmas : a critical review (2015) 0.00 
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    Abstract
    The recent interest in Big Data has generated a broad range of new academic, corporate, and policy practices along with an evolving debate among its proponents, detractors, and skeptics. While the practices draw on a common set of tools, techniques, and technologies, most contributions to the debate come either from a particular disciplinary perspective or with a focus on a domain-specific issue. A close examination of these contributions reveals a set of common problematics that arise in various guises and in different places. It also demonstrates the need for a critical synthesis of the conceptual and practical dilemmas surrounding Big Data. The purpose of this article is to provide such a synthesis by drawing on relevant writings in the sciences, humanities, policy, and trade literature. In bringing these diverse literatures together, we aim to shed light on the common underlying issues that concern and affect all of these areas. By contextualizing the phenomenon of Big Data within larger socioeconomic developments, we also seek to provide a broader understanding of its drivers, barriers, and challenges. This approach allows us to identify attributes of Big Data that require more attention-autonomy, opacity, generativity, disparity, and futurity-leading to questions and ideas for moving beyond dilemmas.
    Type
    a

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