Web intelligence data clustering by bare bone fireworks algorithm combined with k-means

Eva Tuba, Raka Jovanovic, Romana Capor Hrosik, Adis Alihodzic, Milan Tuba*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

10 Citations (Scopus)

Abstract

Data mining and clustering are important elements of various applications in different fields. One of the areas were clustering is rather frequently used is web intelligence, which nowadays represents an important research area. Data collected from the web are usually very complex, dynamic, without structure and rather large. Traditional clustering techniques are not efficient enough and need to be improved. In this paper, we propose combination of recent swarm intelligence algorithm, bare bones fireworks algorithm, and k-means for clustering web intelligence data. The proposed method was compared with other approaches from literature. Based on the experimental results, it can be concluded that the proposed method has very promising characteristics in terms of the quality of clustering, as well as the execution time.

Original languageEnglish
Title of host publicationWIMS 2018 - 8th International Conference on Web Intelligence, Mining and Semantics
EditorsCostin Badica, Rajendra Akerkar, Mirjana Ivanovic, Milos Savic, Milos Radovanovic, Sang-Wook Kim, Riccardo Rosati, Yannis Manolopoulos
PublisherAssociation for Computing Machinery
ISBN (Electronic)9781450354899
DOIs
Publication statusPublished - 25 Jun 2018
Event8th International Conference on Web Intelligence, Mining and Semantics, WIMS 2018 - Novi Sad, Serbia
Duration: 25 Jun 201827 Jun 2018

Publication series

NameACM International Conference Proceeding Series

Conference

Conference8th International Conference on Web Intelligence, Mining and Semantics, WIMS 2018
Country/TerritorySerbia
CityNovi Sad
Period25/06/1827/06/18

Keywords

  • Bare bones fireworks algorithm
  • Clustering
  • K-means
  • Swarm intelligence
  • Web intelligence data
  • Web mining

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