Determining Number of Clusters Using Firefly Algorithm with Cluster Merging for Text Clustering

  • Athraa Jasim MohammedEmail author
  • Yuhanis Yusof
  • Husniza Husni
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9429)


Text mining, in particular the clustering is mostly used by search engines to increase the recall and precision of a search query. The content of online websites (text, blogs, chats, news, etc.) are dynamically updated, nevertheless relevant information on the changes made are not present. Such a scenario requires a dynamic text clustering method that operates without initial knowledge on a data collection. In this paper, a dynamic text clustering that utilizes Firefly algorithm is introduced. The proposed, aFAmerge, clustering algorithm automatically groups text documents into the appropriate number of clusters based on the behavior of firefly and cluster merging process. Experiments utilizing the proposed aFAmerge were conducted on two datasets; 20Newsgroups and Reuter’s news collection. Results indicate that the aFAmerge generates a more robust and compact clusters than the ones produced by Bisect K-means and practical General Stochastic Clustering Method (pGSCM).


Firefly algorithm Text clustering Text mining Agglomerative clustering 



Authors would like to thank the Malaysian Ministry of Higher Education for providing the financial support under the Fundamental Research Grant Scheme (s/o: 12894). Gratitude also goes to Universiti Utara Malaysia for helping in managing the study.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Athraa Jasim Mohammed
    • 1
    • 2
    Email author
  • Yuhanis Yusof
    • 1
  • Husniza Husni
    • 1
  1. 1.School of Computing, College of Arts and SciencesUniversiti Utara MalaysiaSintokMalaysia
  2. 2.University of TechnologyBaghdadIraq

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