Author/Authors
IŞIK, Meltem Şişli Endüstri Meslek Lisesi, Turkey , ÇAMURCU, Ali Yılmaz Marmara Üniversitesi - Teknik Eğitim Fakültesi - Elektronik ve Bilgisayar Eğitimi Bölümü, Turkey
Title Of Article
DOCUMENT CLUSTERING USING K-MEANS AND HYPERSPHERICAL FUZZY C-MEANS ALGORITHMS
شماره ركورد
43783
Abstract
Web pages have became a big data repository, with rapid grow in Internet. For these reason, interest to data mining in the field of searching in web pages and analyzing user profile is increased. Document mining is preferred to get necessary knowledge from documents on web pages. In this study, k-means and hyperspherical fuzzy c-means algorithms were applied to web documents and clustering performances were investigated comparatively using three data sets which have web documents. Our results show that clustering feature of hyperspherical fuzzy c-means algorithm is better than k-means algorithm.
From Page
1
NaturalLanguageKeyword
Data mining , Document mining , clustering , K , means , Hyperspherical Fuzzy c , means
JournalTitle
International journal of advances in engineering and pure sciences
To Page
18
JournalTitle
International journal of advances in engineering and pure sciences
Link To Document