Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
International Journal of Machine Learning and Cybernetics
Erschienen in: International Journal of Machine Learning and Cybernetics 4/2013

01.08.2013 | Original Article

A boundary restricted adaptive particle swarm optimization for data clustering

verfasst von: S. Rana, S. Jasola, R. Kumar

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 4/2013

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Data clustering is the most popular data analysis method in data mining. It is the method that parts the data object to meaningful groups. It has been applied into many areas such as image processing, pattern recognition and machine learning where the data sets are of many shapes and sizes. The most popular K-means and other classical algorithms suffer from drawback of their initial choice of centroid selection and local optima. This paper presents a new improved algorithm named as Boundary Restricted Adaptive Particle Swam Optimization (BR-APSO) algorithm with boundary restriction strategy. The proposed BR-APSO algorithm is tested on nine data sets, and its results are compared with those of PSO, NM-PSO, K-PSO and K-means clustering algorithms. It has been found that the proposed algorithm is robust, generates more accurate results and its convergence speed is also fast compared to other algorithms.
Vorheriger Artikel Probabilistic DEAR models
Nächster Artikel SIFT based iris recognition with normalization and enhancement

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Jetzt informieren
Weitere Produktempfehlungen anzeigen
Literatur
1.
Ahmadi A, Karray F, Kamel M (2007) Multiple cooperating swarms for data clustering. In: Proceedings of IEEE swarm intelligence symposium, pp 206–212
2.
Ahmadyfard A, Modares H (2008) Combining PSO and k-means to enhance data clustering. In: International symposium on telecommunications, pp 688–691
3.
Bandyopadhyay S, Maulik U (2002) An evolutionary technique based on K-means algorithm for optimal clustering in RN. Inf Sci 146:221–237MathSciNetMATHCrossRef
4.
Yang CH, Hsiao CJ, Chuang LY (2010) Accelerated linearly decreasing weight particle swarm optimization for data clustering. Proc Int Multi Conf Eng Comput Sci (Hongkong) 1:1–6
5.
Eberhart RC, Shi Y (2001) Tracking and optimizing dynamic systems with particle swarms. In: Proceedings of the congress on evolutionary computation, Seoul, Korea, pp 94–97
6.
El-abd M, Kamel M (2005) Information exchange in multiple cooperating swarms. In: IEEE swarm intelligence symposium, pp 138–142
7.
Fan S-KS, Liang Y-C, Zahara E (2004) Hybrid simplex search and particle swarm optimization for the global optimization of multimodal functions. Eng Optim 36:401–418CrossRef
8.
Jain AR, Murthy MN, Flynn PJ (1999) Data clustering: a review. ACM Comput Surv 31(3):265–323CrossRef
9.
Jinxin D, Qi Minyong (2009) A new algorithm for clustering based on particle swarm optimization and K-means. IEEE Int Conf Artif Intell Comput Intell 4:264–268
10.
Kao Yi-Tung, Zahara E, Kao I-Wei (2007) A Hybridized Approach to Data Clustering. Expert Syst Appl 34:1754–1762
11.
Kennedy J, Eberhart RC (1995) Particle swarm optimization. Proc IEEE Int Joint Conf Neural Netw 4:1942–1948CrossRef
12.
Kennedy J (1997) Minds and Cultures: Particle Swarm Implications. In: Socially Intelligent Agents Papers AAAI Fall Symposium Technical Report FS-97-02, AAAI Press, Menlo Park, pp 67–72
13.
Murthy CA, Chowdhury N (1996) In search of optimal clusters using genetic algorithms. Pattern Recogn Lett 17:825–832CrossRef
14.
Nelder JA, Mead R (1965) A simplex method for function minimization. Comput J 7:308–313MATHCrossRef
15.
Olsson DM, Nelson LS (1975) The Nelder–Mead simplex procedure for function minimization’. Technometrics 17:45–51MATHCrossRef
16.
Paterlini S, Krink T (2006) Differential evolution and particle swarm optimization in partitional clustering’. Comput Stat Data Anal 50:1220–1247MathSciNetCrossRef
17.
Rana S, Jasola S, Kumar R (2010) A hybrid sequential approach for data clustering using K-Means and particle swarm optimization algorithm. Int J Eng Sc Technol 2(6):167–176
18.
Rana S, Jasola S, Kumar R (2011) A review on particle swarm optimization algorithm and their application to data clustering. Artificial Intelligence Review Archive 35(3):211–222
19.
Selim SZ, Ismail MA (1984) K-means type algorithms: a generalized convergence theorem and characterization of local opti-mality. IEEE Transact Pattern Anal Mach Intell 6:81–87MATHCrossRef
20.
Shi Y, Eberhart, RC (1998) Parameter selection in particle swarm optimization. Evolutionary programming, vol 1441 of lecture notes in computers science, Springer, pp 591–600
21.
Tsai CY, Chiu CC (2008) Developing a feature weight self-adjustment Mechanism for k-means clustering Algorithm. Comput Stat Data Anal 52:4658–4672MathSciNetMATHCrossRef
22.
Zalik KR (2008) An effcient kmeans clustering algorithm. Pattern Recogn Lett 29:1385–1391CrossRef
23.
Wang XZ, He YL, Dong LC, Zhao HY (2011) Particle swarm optimization for determining fuzzy measures from data. Inf Sci 181(19):4230–4252MATHCrossRef
24.
25.
Graaff AJ, Engelbrecht AP (2011) Clustering data in stationary environments with a local network neighborhood artificial immune system. Int J Mach Learn Cyber. doi:10.​1007/​s13042-011-0041-0
26.
Metadaten
Titel
A boundary restricted adaptive particle swarm optimization for data clustering
verfasst von
S. Rana
S. Jasola
R. Kumar
Publikationsdatum
01.08.2013
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 4/2013
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-012-0103-y

Weitere Artikel der Ausgabe 4/2013

International Journal of Machine Learning and Cybernetics 4/2013 Zur Ausgabe

Original Article

Investigation of relationship between object-oriented metrics and change proneness

Original Article

A multi-agent based model for collective purchasing in electronic commerce

Original Article

Implication operators on the set of ∨-irreducible element in the linguistic truth-valued intuitionistic fuzzy lattice

Original Article

Performance of global–local hybrid ensemble versus boosting and bagging ensembles

Original Article

System-on-programmable-chip implementation of diminishing learning based pattern recognition system

Original Article

SIFT based iris recognition with normalization and enhancement

Neuer Inhalt

    Bildnachweise