Search (1176 results, page 1 of 59)

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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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Abstract

Presents the concept and the techniques identified by the term data mining. Explains the principles and phases of developing a data mining process, and the main types of data mining tools

Footnote

Übers. des Titels: Perspectives on the retrieval and exploitation of electronic information: data mining

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

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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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

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

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

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Abstract

The term "facet" was introduced into the field of library classification systems by Ranganathan in the 1930's [Ranganathan, 1962]. A facet is a viewpoint or aspect. In contrast to traditional classification systems, faceted systems are modular in that a domain is analyzed in terms of baseline facets which are then synthesized. In this paper, the term "facet" is used in a broader meaning. Facets can describe different aspects on the same level of abstraction or the same aspect on different levels of abstraction. The notion of facets is related to database views, multicontexts and conceptual scaling in formal concept analysis [Ganter and Wille, 1999], polymorphism in object-oriented design, aspect-oriented programming, views and contexts in description logic and semantic networks. This paper presents a definition of facets in terms of faceted knowledge representation that incorporates the traditional narrower notion of facets and potentially facilitates translation between different knowledge representation formalisms. A goal of this approach is a modular, machine-aided knowledge base design mechanism. A possible application is faceted thesaurus construction for information retrieval and data mining. Reasoning complexity depends on the size of the modules (facets). A more general analysis of complexity will be left for future research.

Date

22. 1.2016 17:30:31

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Theme

Data Mining

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Abstract

Examines basic issues of data mining using the theory of rough sets, which is a recent proposal for generalizing classical set theory. The Pawlak rough set model is based on the concept of an equivalence relation. A generalized rough set model need not be based on equivalence relation axioms. The Pawlak rough set model has been used for deriving deterministic as well as probabilistic rules froma complete database. Demonstrates that a generalised rough set model can be used for generating rules from incomplete databases. These rules are based on plausability functions proposed by Shafer. Discusses the importance of rule extraction from incomplete databases in data mining

Footnote

Contribution to a special issue devoted to knowledge discovery and data mining

Theme

Data Mining

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Abstract

State of the art review of the recently developed concepts of data mining (defined as the automated process of evaluating data and finding relationships) and knowledge discovery (defined as the automated process of extracting information, especially unpredicted relationships or previously unknown patterns among the data) with particular reference to numerical data. Includes: the knowledge acquisition process; data mining; evaluation methods; and knowledge discovery. Concludes that existing work in the field are confusing because the terminology is inconsistent and poorly defined. Although methods are available for analyzing and cleaning databases, better coordinated efforts should be directed toward providing users with improved means of structuring search mechanisms to explore the data for relationships

Theme

Data Mining

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Abstract

Presents a heuristic, attribute-based, noise-tolerant data mining program, HCV (Version 2.0), absed on the newly-developed extension matrix approach. Gives a simple example of attribute-based induction to show the difference between the rules in variable-valued logic produced by HCV, the decision tree generated by C4.5 and the decision tree's decompiled rules by C4.5 rules. Outlines the extension matrix approach for data mining. Describes the HCV algorithm in detail. Outlines techniques developed and implemented in the HCV program for noise handling and discretization of continuous domains respectively. Follows these with a performance comparison of HCV with famous ID3-like algorithms including C4.5 and C4.5 rules on a collection of standard databases including the famous MONK's problems

Footnote

Contribution to a special issue devoted to knowledge discovery and data mining

Theme

Data Mining