Add to ORKGIn this paper we present a word encoding and clustering technique that groups web documents based on the importance of the words that appear in the documents. We use a two level self-organizing map architecture to generate clusters of words and documents. We propose that by capturing word importance information of words, similar documents can be then clustered to assist in web document retrieval. A web document retrieval system is presented to demonstrate how this approach could be integrated into web search. 1
In this paper an approach that is using evolving, incremental (on-line) clustering to automatically ...
Document clustering is a very hard task in automatic text processing since it requires extracting re...
Document clustering is a very hard task in automatic text processing since it requires extracting re...
This thesis presents new methods for classification and thematic grouping of billions of web pages, ...
The paper discusses SOM based clustering of web documents. The clustering method uses ontology for d...
In this paper, we propose a system that clusters web pages and presents them as a hierarchical struc...
We examine techniques that \discover " features in sets of pre{categorized documents, such that...
Document clustering has been applied in web information retrieval, which facilitates users’ qu...
This paper proposes a new and efficient methodology for clustering of html documents. The topic wise...
We propose a system that clusters web pages and presents them as a hierarchical structure instead of...
As the use of the web grows globally and exponentially, it becomes increasingly harder for users to ...
Conventional document retrieval systems (e.g., Alta Vista) return long lists of ranked documents in ...
With the increase in information on the World Wide Web it has become difficult to find the desired ...
The chapter provides a survey of some clustering methods relevant to the clustering document collect...
Document clustering is a very hard task in automatic text processing since it requires extracting re...
In this paper an approach that is using evolving, incremental (on-line) clustering to automatically ...
Document clustering is a very hard task in automatic text processing since it requires extracting re...
Document clustering is a very hard task in automatic text processing since it requires extracting re...
This thesis presents new methods for classification and thematic grouping of billions of web pages, ...
The paper discusses SOM based clustering of web documents. The clustering method uses ontology for d...
In this paper, we propose a system that clusters web pages and presents them as a hierarchical struc...
We examine techniques that \discover " features in sets of pre{categorized documents, such that...
Document clustering has been applied in web information retrieval, which facilitates users’ qu...
This paper proposes a new and efficient methodology for clustering of html documents. The topic wise...
We propose a system that clusters web pages and presents them as a hierarchical structure instead of...
As the use of the web grows globally and exponentially, it becomes increasingly harder for users to ...
Conventional document retrieval systems (e.g., Alta Vista) return long lists of ranked documents in ...
With the increase in information on the World Wide Web it has become difficult to find the desired ...
The chapter provides a survey of some clustering methods relevant to the clustering document collect...
Document clustering is a very hard task in automatic text processing since it requires extracting re...
In this paper an approach that is using evolving, incremental (on-line) clustering to automatically ...
Document clustering is a very hard task in automatic text processing since it requires extracting re...
Document clustering is a very hard task in automatic text processing since it requires extracting re...