| Gerasimos Spanakis|
(Alternative names for this author)
|Co-authors||Andreas Stafylopatis, Georgios Siolas|
|Authorship||Publications (3), datasets (0), tools (0)|
|Citations||Total (0), average (0), median (0), max (0), min (0)|
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Gerasimos Spanakis is an author.
PublicationsOnly those publications related to wikis are shown here.
|Title||Keyword(s)||Published in||Language||DateThis property is a special property in this wiki.||Abstract||R||C|
|DoSO: A document self-organizer||Document clustering
|Journal of Intelligent Information Systems||English||2012||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.||0||0|
|Exploiting Wikipedia Knowledge for Conceptual Hierarchical Clustering of Documents||Comput. J.||English||2012||0||0|
|Conceptual hierarchical clustering of documents using Wikipedia knowledge||Lecture Notes in Electrical Engineering||English||2010||In this paper, we propose a novel method for conceptual hierarchical clustering of documents using knowledge extracted from Wikipedia. A robust and compact document representation is built in real-time using the Wikipedia API. The clustering process is hierarchical and creates cluster labels which are descriptive and important for the examined corpus. Experiments show that the proposed technique greatly improves over the baseline approach. © 2011 Springer Science+Business Media B.V.||0||0|