DoSO: A document self-organizer
|DoSO: A document self-organizer|
|Author(s)||Spanakis G., Siolas G., Stafylopatis A.|
|Published in||Journal of Intelligent Information Systems|
|Keyword(s)||Document clustering, Document representation, SOM, Wikipedia (Extra: Bag of words, Clustering results, Document Clustering, Document collection, Document Representation, Evaluation measures, Hierarchical approach, SOM, Wikipedia, Clustering algorithms, Knowledge representation, Labels, Self organizing maps, Websites, Information retrieval)|
|Article||BASE, CiteSeerX, Google Scholar|
|Web||Ask, Bing, Google (PDF), Yahoo!|
|Download and mirrors|
|Local copy||Not available|
|Remote mirror(s)||Not available|
|Export and share|
|BibTeX, CSV, RDF, JSON|
|Browse properties · List of journal articles|
In this paper, we propose a Document Self Organizer (DoSO), an extension of the classic Self Organizing Map (SOM) model, in order to deal more efficiently with a document clustering task. Starting from a document representation model, based on important "concepts" exploiting Wikipedia knowledge, that we have previously developed in order to overcome some of the shortcomings of the Bag-of-Words (BOW) model, we demonstrate how SOM's performance can be boosted by using themost important concepts of the document collection to explicitly initialize the neurons. We also show how a hierarchical approach can be utilized in the SOMmodel and how this can lead to amore comprehensive final clustering result with hierarchical descriptive labels attached to neurons and clusters. Experiments show that the proposed model (DoSO) yields promising results both in terms of extrinsic and SOM evaluation measures.
- This section requires expansion. Please, help!
Probably, this publication is cited by others, but there are no articles available for them in WikiPapers. Cited 1 time(s)