Search (114 results, page 1 of 6)

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  1. Chowdhury, G.G.: Template mining for information extraction from digital documents (1999) 0.22 
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    Date
    2. 4.2000 18:01:22
    Theme
    Data Mining
  2. Matson, L.D.; Bonski, D.J.: Do digital libraries need librarians? (1997) 0.13 
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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
    Theme
    Data Mining
  3. Tunbridge, N.: Semiology put to data mining (1999) 0.10 
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    Theme
    Data Mining
  4. Amir, A.; Feldman, R.; Kashi, R.: ¬A new and versatile method for association generation (1997) 0.10 
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    Source
    Information systems. 22(1997) nos.5/6, S.333-347
    Theme
    Data Mining
  5. Fayyad, U.; Piatetsky-Shapiro, G.; Smyth, P.: From data mining to knowledge discovery in databases (1996) 0.09 
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    Abstract
    Gives an overview of data mining and knowledge discovery in databases. Clarifies how they are related both to each other and to related fields. Mentions real world applications data mining techniques, challenges involved in real world applications of knowledge discovery, and current and future research directions
    Theme
    Data Mining
  6. Fong, A.C.M.: Mining a Web citation database for document clustering (2002) 0.09 
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    Theme
    Data Mining
  7. Blake, C.: Text mining (2011) 0.09 
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    Theme
    Data Mining
  8. Hofstede, A.H.M. ter; Proper, H.A.; Van der Weide, T.P.: Exploiting fact verbalisation in conceptual information modelling (1997) 0.09 
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    Source
    Information systems. 22(1997) nos.5/6, S.349-385
    Theme
    Data Mining
  9. Tonkin, E.L.; Tourte, G.J.L.: Working with text. tools, techniques and approaches for text mining (2016) 0.09 
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    Abstract
    What is text mining, and how can it be used? What relevance do these methods have to everyday work in information science and the digital humanities? How does one develop competences in text mining? Working with Text provides a series of cross-disciplinary perspectives on text mining and its applications. As text mining raises legal and ethical issues, the legal background of text mining and the responsibilities of the engineer are discussed in this book. Chapters provide an introduction to the use of the popular GATE text mining package with data drawn from social media, the use of text mining to support semantic search, the development of an authority system to support content tagging, and recent techniques in automatic language evaluation. Focused studies describe text mining on historical texts, automated indexing using constrained vocabularies, and the use of natural language processing to explore the climate science literature. Interviews are included that offer a glimpse into the real-life experience of working within commercial and academic text mining.
    LCSH
    Data mining
    RSWK
    Text Mining / Aufsatzsammlung
    Subject
    Text Mining / Aufsatzsammlung
    Data mining
    Theme
    Data Mining
  10. Kulathuramaiyer, N.; Maurer, H.: Implications of emerging data mining (2009) 0.08 
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    Abstract
    Data Mining describes a technology that discovers non-trivial hidden patterns in a large collection of data. Although this technology has a tremendous impact on our lives, the invaluable contributions of this invisible technology often go unnoticed. This paper discusses advances in data mining while focusing on the emerging data mining capability. Such data mining applications perform multidimensional mining on a wide variety of heterogeneous data sources, providing solutions to many unresolved problems. This paper also highlights the advantages and disadvantages arising from the ever-expanding scope of data mining. Data Mining augments human intelligence by equipping us with a wealth of knowledge and by empowering us to perform our daily tasks better. As the mining scope and capacity increases, users and organizations become more willing to compromise privacy. The huge data stores of the 'master miners' allow them to gain deep insights into individual lifestyles and their social and behavioural patterns. Data integration and analysis capability of combining business and financial trends together with the ability to deterministically track market changes will drastically affect our lives.
    Theme
    Data Mining
  11. Raghavan, V.V.; Deogun, J.S.; Sever, H.: Knowledge discovery and data mining : introduction (1998) 0.08 
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    Abstract
    Defines knowledge discovery and database mining. The challenge for knowledge discovery in databases (KDD) is to automatically process large quantities of raw data, identifying the most significant and meaningful patterns, and present these as as knowledge appropriate for achieving a user's goals. Data mining is the process of deriving useful knowledge from real world databases through the application of pattern extraction techniques. Explains the goals of, and motivation for, research work on data mining. Discusses the nature of database contents, along with problems within the field of data mining
    Footnote
    Contribution to a special issue devoted to knowledge discovery and data mining
    Theme
    Data Mining
  12. Zhou, L.; Chaovalit, P.: Ontology-supported polarity mining (2008) 0.08 
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    Abstract
    Polarity mining provides an in-depth analysis of semantic orientations of text information. Motivated by its success in the area of topic mining, we propose an ontology-supported polarity mining (OSPM) approach. The approach aims to enhance polarity mining with ontology by providing detailed topic-specific information. OSPM was evaluated in the movie review domain using both supervised and unsupervised techniques. Results revealed that OSPM outperformed the baseline method without ontology support. The findings of this study not only advance the state of polarity mining research but also shed light on future research directions.
    Theme
    Data Mining
  13. Vaughan, L.; Chen, Y.: Data mining from web search queries : a comparison of Google trends and Baidu index (2015) 0.08 
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    Source
    Journal of the Association for Information Science and Technology. 66(2015) no.1, S.13-22
    Theme
    Data Mining
  14. Fayyad, U.M.: Data mining and knowledge dicovery : making sense out of data (1996) 0.08 
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    Abstract
    Defines knowledge discovery and data mining (KDD) as the overall process of extracting high level knowledge from low level data. Outlines the KDD process. Explains how KDD is related to the fields of: statistics, pattern recognition, machine learning, artificial intelligence, databases and data warehouses
    Theme
    Data Mining
  15. Chen, S.Y.; Liu, X.: ¬The contribution of data mining to information science : making sense of it all (2005) 0.08 
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    Theme
    Data Mining
  16. Varathan, K.D.; Giachanou, A.; Crestani, F.: Comparative opinion mining : a review (2017) 0.07 
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    Abstract
    Opinion mining refers to the use of natural language processing, text analysis, and computational linguistics to identify and extract subjective information in textual material. Opinion mining, also known as sentiment analysis, has received a lot of attention in recent times, as it provides a number of tools to analyze public opinion on a number of different topics. Comparative opinion mining is a subfield of opinion mining which deals with identifying and extracting information that is expressed in a comparative form (e.g., "paper X is better than the Y"). Comparative opinion mining plays a very important role when one tries to evaluate something because it provides a reference point for the comparison. This paper provides a review of the area of comparative opinion mining. It is the first review that cover specifically this topic as all previous reviews dealt mostly with general opinion mining. This survey covers comparative opinion mining from two different angles. One from the perspective of techniques and the other from the perspective of comparative opinion elements. It also incorporates preprocessing tools as well as data set that were used by past researchers that can be useful to future researchers in the field of comparative opinion mining.
    Theme
    Data Mining
  17. Howlett, D.: Digging deep for treasure (1998) 0.07 
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    Theme
    Data Mining
  18. Benoit, G.: Data mining (2002) 0.07 
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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.
    Theme
    Data Mining
  19. Perugini, S.; Ramakrishnan, N.: Mining Web functional dependencies for flexible information access (2007) 0.07 
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    Abstract
    We present an approach to enhancing information access through Web structure mining in contrast to traditional approaches involving usage mining. Specifically, we mine the hardwired hierarchical hyperlink structure of Web sites to identify patterns of term-term co-occurrences we call Web functional dependencies (FDs). Intuitively, a Web FD x -> y declares that all paths through a site involving a hyperlink labeled x also contain a hyperlink labeled y. The complete set of FDs satisfied by a site help characterize (flexible and expressive) interaction paradigms supported by a site, where a paradigm is the set of explorable sequences therein. We describe algorithms for mining FDs and results from mining several hierarchical Web sites and present several interface designs that can exploit such FDs to provide compelling user experiences.
    Footnote
    Beitrag eines Themenschwerpunktes "Mining Web resources for enhancing information retrieval"
    Theme
    Data Mining
  20. Srinivasan, P.: Text mining in biomedicine : challenges and opportunities (2006) 0.07 
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    Abstract
    Text mining is about making serendipity more likely. Serendipity, the chance discovery of interesting ideas, has been responsible for many discoveries in science. Text mining systems strive to explore large text collections, separate the potentially meaningfull connections from a vast and mostly noisy background of random associations. In this paper we provide a summary of our text mining approach and also illustrate briefly some of the experiments we have conducted with this approach. In particular we use a profile-based text mining method. We have used these profiles to explore the global distribution of disease research, replicate discoveries made by others and propose new hypotheses. Text mining holds much potential that has yet to be tapped.
    Theme
    Data Mining

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