A fuzzy clustering approach for finding similar documents using a novel similarity measure
Küçük Resim Yok
Tarih
2007
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
PERGAMON-ELSEVIER SCIENCE LTD
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Searching for similar documents has a crucial role in document management. This paper aims for developing a fast and high quality method of searching similar documents based on fuzzy clustering in large document collections. In order to perform these requirements, a two layers structure is proposed. Formerly, finding the similarity in documents is based on the strategy that uses word-by-word comparison. The proposed method in this study uses two layers structure and lets the documents pass through it to find the similarities. In this system, predefined fuzzy clusters are used to extract feature vectors of related documents for finding similar documents of them. Similarity measure is estimated based on these vectors. To do this, a distance based similarity measure is proposed. It has been seen in empirical results that the proposed system uses new similarity measure and has better performance compared with conventional similarity measurement systems. (c) 2006 Elsevier Ltd. All rights reserved.
Açıklama
Anahtar Kelimeler
text mining, document similarity, fuzzy clustering, fuzzy similarity measure, distance based similarity
Kaynak
EXPERT SYSTEMS WITH APPLICATIONS
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
33
Sayı
3
