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AKILLI SİSTEMLER VE UYGULAMALARI DERGİSİ
JOURNAL OF INTELLIGENT SYSTEMS WITH APPLICATIONS
J. Intell. Syst. Appl.
E-ISSN: 2667-6893
Creative Commons License This work is licensed under a Creative Commons Attribution 4.0 International License.

Metaheuristics Based Clustering Algorithms on Document Clustering

Metin Belgesi Kümelemede Metasezgisel Yöntemlere Dayalı Kümeleme Algoritmaları

How to cite: Onan A. Metaheuristics based clustering algorithms on document clustering. Akıllı Sistemler ve Uygulamaları Dergisi (Journal of Intelligent Systems with Applications) 2019; 2(1): 39-45. DOI: 10.54856/jiswa.201905059

Full Text: PDF, in Turkish.

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Title: Metaheuristics Based Clustering Algorithms on Document Clustering

Abstract: Cluster analysis is an important exploratory data analysis technique which divides data into groups based on their similarity. Document clustering is the process of employing clustering algorithms on textual data so that text documents can be retrieved, organized, navigated and summarized in an efficient way. Document clustering can be utilized in the organization, summarization and classification of text documents. Metaheuristic algorithms have been successfully utilized to deal with complex optimization problems, including cluster analysis. In this paper, we analyze the clustering quality of five metaheuristic clustering algorithms (namely, particle swarm optimization, genetic algorithm, cuckoo search, firefly algorithm and yarasa algorithm) on fifteen text collections in term of F-measure. In the empirical analysis, two conventional clustering algorithms (K-means and bi-secting k-means) are also considered. The experimental analysis indicates that swarm-based clustering algorithms outperform conventional clustering algorithms on text document clustering.

Keywords: Document clustering; swarm intelligence; metaheuristic algorithms


Başlık: Metin Belgesi Kümelemede Metasezgisel Yöntemlere Dayalı Kümeleme Algoritmaları

Özet: Kümeleme analizi, verileri benzerliklerine göre gruplarına ayıran önemli bir veri analizi tekniğidir. Belge kümeleme, kümeleme algoritmalarının metin belgeleri üzerinde uygulanması ile belgelerin etkin bir biçimde geri getiriminin, organizasyonunun, erişiminin ve özetlenmesinin olanaklı hale gelmesini sağlar. Belge kümeleme, metin belgelerinin organizasyonu, özetlenmesi ve sınıflandırılmasında kullanılabilir. Metasezgisel algoritmalar, aralarında kümeleme analizinin de yer aldığı birçok karmaşık eniyileme probleminin çözümünde uygulanmaktadır. Bu çalışmada, beş metasezgisel kümeleme algoritmasının (parçacık sürüsü eniyilemesi, genetik algoritma, guguk kuşu algoritması, ateşböceği algoritması ve yarasa algoritması) on beş metin veri seti üzerinde F-ölçütü aracılığı ile değerlendirilmiştir. Deneysel analizlerde, iki geleneksel kümeleme algoritması (K-ortalama ve ikiye ayırma Kortalama) da dikkate alınmıştır. Deneysel analiz sonuçları, sürü zekasına dayalı kümeleme algoritmalarının daha yüksek başarım elde ettiğini göstermektedir.

Anahtar kelimeler: Belge kümeleme; sürü zekası; metasezgisel algoritmalar


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