Clustering of XML documents
Identifieur interne : 001964 ( Main/Exploration ); précédent : 001963; suivant : 001965Clustering of XML documents
Auteurs : Damien Guillaume [États-Unis] ; Fionn Murtagh [Royaume-Uni]Source :
- Computer Physics Communications [ 0010-4655 ] ; 2000.
English descriptors
- Teeft :
- Algorithm, Astronomical markup language, Astronomical object, Astronomical object names, Astronomical objects, Average number, Balanced clusters, Best keyword, Best score, Cluster, Collaboration graph, Cost function, Current_solution, Damien guillaume, Database, Document, Elsevier science, Euclidean distance, Extensible markup language, Goto step, Graph partitioning, Graph partitioning algorithm, Graph partitioning problem, Grid, Guillaume, Html, Html documents, Ident, Indexing vocabulary, Information retrieval, Initial solution, Java applet, Jean heyvaerts, Keyword, Keyword links, Keywords, Knowledge discovery, Links, Local minima problem, Many articles, Many documents, Markup, Markup language, Murtagh, Murtagh computer physics communications, Node, Noising, Noising algorithm, Noising method, Noising rate, Original data, Other clusters, Other documents, Partitioning, Partitioning algorithm, Regular grid, Same cluster, Same keyword, Same number, Same time, Server, Similar documents, Total number, User, User clicks, User interface, Xlink.
Abstract
Abstract: Self-organization or clustering of data objects can be a powerful aid towards knowledge discovery in distributed databases. The web presents opportunities for such clustering of documents and other data objects. This potential will be even more pronounced when XML becomes widely used over the next few years. Based on clustering of XML links, we explore a visualization approach for discovering knowledge on the web.
Url:
DOI: 10.1016/S0010-4655(99)00511-1
Affiliations:
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Le document en format XML
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The web presents opportunities for such clustering of documents and other data objects. This potential will be even more pronounced when XML becomes widely used over the next few years. Based on clustering of XML links, we explore a visualization approach for discovering knowledge on the web.</div></front></TEI><affiliations><list><country><li>Royaume-Uni</li><li>États-Unis</li></country><region><li>Illinois</li></region></list><tree><country name="États-Unis"><region name="Illinois"><name sortKey="Guillaume, Damien" sort="Guillaume, Damien" uniqKey="Guillaume D" first="Damien" last="Guillaume">Damien Guillaume</name></region></country><country name="Royaume-Uni"><noRegion><name sortKey="Murtagh, Fionn" sort="Murtagh, Fionn" uniqKey="Murtagh F" first="Fionn" last="Murtagh">Fionn Murtagh</name></noRegion></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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