Search (2180 results, page 1 of 109)

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Content

Vgl.: http://www.google.de/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&ved=0CEAQFjAA&url=http%3A%2F%2Fciteseerx.ist.psu.edu%2Fviewdoc%2Fdownload%3Fdoi%3D10.1.1.91.4940%26rep%3Drep1%26type%3Dpdf&ei=dOXrUMeIDYHDtQahsIGACg&usg=AFQjCNHFWVh6gNPvnOrOS9R3rkrXCNVD-A&sig2=5I2F5evRfMnsttSgFF9g7Q&bvm=bv.1357316858,d.Yms.

Date

8. 1.2013 10:22:32

Source

Proceedings of the 4th IEEE International Conference on Data Mining (ICDM 2004), 1-4 November 2004, Brighton, UK

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Date

2. 4.2000 18:01:22

Theme

Data Mining

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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

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Theme

Data Mining

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Date

1. 8.1996 22:08:06

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Source

Information systems. 22(1997) nos.5/6, S.333-347

Theme

Data Mining

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Abstract

Describes and evaluates methods for automatically isolating and extracting biliographic references from the full texts of patents, designed to facilitate the work of patent examiners who currently perform this task manually. These references include citations both to patents and to other bibliographic sources. Notes that patents are unusual as citing documents in that the citations occur maily in the body of the text, rather than as footnotes or in separate sections. Describes the natural language processing technique of template mining used to extract data directly from the text where either the data or the text surrounding the data form recognizable patterns. When text matches a template, the system extracts data according to instructions associated with that template. Examines the sub languages of citations and the development of templates for the extraction of citations to patent. Reports results of running 2 reference extraction systems against a sample of 100 European Patent Office patent documents, with recall and prescision data for patent and non patent citations, and concludes with suggestions for future improvements

Source

Journal of information science. 22(1996) no.6, S.423-436

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Abstract

Describes a new concept of linguistic atoms with 3 digital characteristics: expected value Ex, entropy En, and deviation D. The mathematical description has effectively integrated the fuzziness and randomness of linguistic terms in a unified way. Develops a method of knowledge representation in KDD, which bridges the gap between quantitative and qualitative knowledge. Mapping between quantities and qualities becomes much easier and interchangeable. In order to discover generalised knowledge from a database, uses virtual linguistic terms and cloud transfer for the auto-generation of concept hierarchies to attributes. Predicitve data mining with the cloud model is given for implementation. Illustrates the advantages of this linguistic model in KDD

Footnote

Contribution to 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

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Abstract

Homepages usually describe important semantic information about conceptual or physical entities; hence, they are the main targets for searching and browsing. To facilitate semantic-based information retrieval (IR) at a Web site, homepages can be identified and classified under some predefined concepts and these concepts are then used in query or browsing criteria, e.g., finding professor homepages containing information retrieval. In some Web sites, relationships may also exist among homepages. These relationship instances (also known as homepage relationships) enrich our knowledge about these Web sites and allow more expressive semantic-based IR. In this article, we investigate the features to be used in mining homepage relationships. We systematically develop different classes of inter-homepage features, namely, navigation, relative-location, and common-item features. We also propose deriving for each homepage a set of support pages to obtain richer and more complete content about the entity described by the homepage. The homepage together with its support pages are known to be a Web unit. By extracting inter-homepage features from Web units, our experiments on the WebKB dataset show that better homepage relationship mining accuracies can be achieved.

Date

22. 7.2006 16:18:25

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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

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Theme

Data Mining

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Theme

Data Mining

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Abstract

As with print collections, the evaluation and selection of CD-ROMs should be based on established guidelines. Such attributes as computer network compatibility and platform are exclusively applicable to CD-ROM. Presents a knowledge based system to prioritize and select CD-ROMs for a library collection, operating on consistency driven pairwise comparisons. The computer system indicates the most inconsistent judgements and allows librarians to reconsider their position. After consistency analysis is completed, the software computes the weights of all criteria used in the evaluation process. The system includes a subsystem for evaluating CD-ROM titles. Offers a CD-ROM evaluation form. Discusses cost considerations; the use of pairwise comparisons in knowledge based systems with reference to data mining; the CD-ROM selection process; and consistency analysis of experts' judgements

Date

6. 3.1997 16:22:15

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Source

Information systems. 22(1997) nos.5/6, S.349-385

Theme

Data Mining

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Footnote

Contribution to 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

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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

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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

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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

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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

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Abstract

We present CopeOpi, an opinion-analysis system, which extracts from the Web opinions about specific targets, summarizes the polarity and strength of these opinions, and tracks opinion variations over time. Objects that yield similar opinion tendencies over a certain time period may be correlated due to the latent causal events. CopeOpi discovers relationships among objects based on their opinion-tracking plots and collocations. Event bursts are detected from the tracking plots, and the strength of opinion relationships is determined by the coverage of these plots. To evaluate opinion mining, we use the NTCIR corpus annotated with opinion information at sentence and document levels. CopeOpi achieves sentence- and document-level f-measures of 62% and 74%. For relationship discovery, we collected 1.3M economics-related documents from 93 Web sources over 22 months, and analyzed collocation-based, opinion-based, and hybrid models. We consider as correlated company pairs that demonstrate similar stock-price variations, and selected these as the gold standard for evaluation. Results show that opinion-based and collocation-based models complement each other, and that integrated models perform the best. The top 25, 50, and 100 pairs discovered achieve precision rates of 1, 0.92, and 0.79, respectively.