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
Pattern Analysis and Applications
Erschienen in: Pattern Analysis and Applications 2/2004

01.07.2004 | Theoretical Advances

A new cluster validity measure and its application to image compression

verfasst von: C.-H. Chou, M.-C. Su, E. Lai

Erschienen in: Pattern Analysis and Applications | Ausgabe 2/2004

Einloggen

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

search-config
loading …

Abstract

Many validity measures have been proposed for evaluating clustering results. Most of these popular validity measures do not work well for clusters with different densities and/or sizes. They usually have a tendency of ignoring clusters with low densities. In this paper, we propose a new validity measure that can deal with this situation. In addition, we also propose a modified K-means algorithm that can assign more cluster centres to areas with low densities of data than the conventional K-means algorithm does. First, several artificial data sets are used to test the performance of the proposed measure. Then the proposed measure and the modified K-means algorithm are applied to reduce the edge degradation in vector quantisation of image compression.
Vorheriger Artikel An optical character recognition system for printed Telugu text
Nächster Artikel Bayesian Artificial Intelligence

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
Literatur
1.
Jain AK, Dubes RC (1988) Algorithms for clustering data. Prentice Hall, Englewood Cliffs, NJ
2.
Höppner F, Klawonn F, Kruse R, Runkler T (1999) Fuzzy cluster analysis-methods for classification, Data Analysis and Image Recognition. Wiley, Chichester
3.
Bezdek JC (1981) Pattern recognition with fuzzy objective function algorithms. Plenum Press, New York
4.
Bezdek JC (1973) Fuzzy mathematics in pattern classification. PhD thesis, Cornell University
5.
Dunn JC (1973) A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. J Cybern 3(3):32–57MATH
6.
Gustafson DE, Kessel WC (1979) Fuzzy clustering with a fuzzy covariance matrix. In: Proceedings of the IEEE Conference on Decision and control, San Diego, January 1979, pp 761–766
7.
Gath I, Geva AB (1989) Unsupervised optimal fuzzy clustering. IEEE Trans Pattern Anal Machine Intell 11:773–781CrossRef
8.
Dave RN (1989) Use of the adaptive fuzzy clustering algorithm to detect lines in digital images. In: DP Casasent (ed) Intelligent Robots and Computer Vision VIII, vol 1192, part 2, pp 600–611
9.
Dave RN (1990) Fuzzy shell-clustering and application to circle detection in digital images. Int J Gen Syst 16:343–355MATH
10.
Man Y, Gath I (1994) Detection and separation of ring-shaped clusters using fuzzy clustering. IEEE Trans Pattern Anal Machine Intell 16:855–861CrossRef
11.
Dave RN, Bhaswan K (1992) Adaptive fuzzy c-shells clustering and detection of ellipses. IEEE Trans Neural Networks 3: 643–662CrossRef
12.
Höppner F (1997) Fuzzy shell clustering algorithms in image processing: fuzzy c-rectangular and 2-rectangular shells. IEEE Trans Fuzzy Syst 5:599–613CrossRef
13.
Su MC, Chou CH (1999) A competitive learning algorithm using symmetry. IEICE Trans Fund Electron Commun Comput Sci E82-A(4):680–687
14.
Su MC, Chou CH (2001) A modified version of the k-means algorithm with a distance based on cluster symmetry. IEEE Trans Pattern Anal Machine Intell 23: 674–680CrossRef
15.
Karypis G, Han H, Kumar V (1999) Chameleon: hierarchical clustering using dynamic modeling. IEEE Comput 32:68–75CrossRef
16.
Ester M, Kriegel HP, Sander J, Xu X (1996) A density-based algorithm for discovering cluster in large spatial database with noise. In: Proceedings of the 2nd International Conference on Knowledge discovery and data mining. AAAI Press, Menlo Park, CA, pp 226–231
17.
Guha S, Rastogi R, Shim K (1999) ROCK: a robust clustering algorithm for categorical attributes. In: Proceedings of the 15th International Conference on Data engineering. IEEE Computer Society Press, Los Alimitos, CA, pp 512–521
18.
Guha S, Rastogi R, Shim K (1998) CURE: an efficient clustering algorithm for large databases. In: Proceeding of the ACM SIGMOD International Conference on Management of data, pp 73–84
19.
Agrawal R, Gehrke J, Gunopulos D, Raghavan P (1998) Automatic subspace clustering for high dimensional data for data mining application. In: Proceedings of the ACM SIGMOD International Conference on Management of data, Seattle, WA, June 1998, pp 94–105
20.
Ankerst M, Breunig M, Kriegel HP, Sander J (1999) OPTICS: ordering points to identify the clustering structure. In: Proceedings of the ACM SIGMOD International Conference on Management of data, Philadelphia, PA, June 1999. ACM Press, New York, pp 49–60
21.
Zhang T, Ramakrishnan R, Livny M (1996) BIRCH: an efficient data clustering method for very large databases. In: Proceedings of the ACM SIGMOD International Conference on Management of data, Montreal, Canada, June 1996, pp 103–114
22.
Huntsherger TL, Jacobs CL, Cannon RL (1985) Iterative fuzzy image segmentation. Pattern Recognition 18:131–138CrossRef
23.
Dave RN, Patel KJ (1990) Progressive fuzzy clustering algorithms for characteristic shape recognition. In: IB Turksen (ed) Proceedings of the North American Fuzzy Information Processing Society on Quarter century of fuzziness, vol 1, pp 121–124
24.
Krishnapuram R, Frigui H, Nasraoui O (1995) Fuzzy and possibilistic shell clustering algorithms and their application to boundary detection and surface approximation—I and II. IEEE Trans Fuzzy Syst 3:29–61CrossRef
25.
Jolion JM, Meer P, Bataouche S (1991) Robust clustering with applications in computer vision. IEEE Trans Pattern Anal Machine Intell 13:791–802CrossRef
26.
Frigui R, Krishnapuram R (1997) Clustering by competitive agglomeration. Pattern Recognition 30:1109–1119CrossRef
27.
Miyamoto S, Mukaidono M (1997) Fuzzy c-means as a regularization and maximum entropy approach. In: Proceedings of the 7th Fuzzy System Association World Confgress, Prague, Czech Republic, June 1997, vol 2, pp 86–92
28.
Dunn JC (1974) Well separated clusters and optimal fuzzy partitions. J Cybernet 4:95–104MATH
29.
Bezdek JC (1974) Numerical taxonomy with fuzzy sets. J Math Biol 1:57–71MATH
30.
Xie XL, Beni G (1991) A validity measure for fuzzy clustering. IEEE Trans Pattern Anal Machine Intell 13:841–847CrossRef
31.
Davies DL, Bouldin DW (1979) A cluster separation measure. IEEE Trans Pattern Anal Machine Intell 1:224–227
32.
Milligan GW, Cooper MC (1985) An examination of procedures for determining the number of clusters in a data set. Psychimetrika 50:159–179
33.
Jain AK, Murthy MN, Flynn PJ (1999) Data clustering: a review. ACM Comput Surv 31:265–323CrossRef
34.
Cheeseman P, Stutz J (1996) Bayesian classification (autoclass): theory and results. In: Fayyad UM, Piatesky-Shapiro G, Smyth P, Uthurusamy R (eds) Advances in knowledge discovery and data mining. AAAI Press, Menlo Park, CA
35.
Heckerman D, Geiger D, Chickering DM (1995) Learning Bayesian networks: the combination of knowledge and statistical data. Machine Learning 20:197–243CrossRefMATH
36.
37.
Bandyopadhya S, Maulik U (2001) Nonparametric genetic clustering: comparison of validity indices. IEEE Trans Syst Man Cybernet Part C: Applic Rev 31:120–125
38.
Bezdek JC, Pal NR (1998) Some new indexes of cluster validity. IEEE Trans Syst Man Cybernet 28: 301–315CrossRef
39.
Bensaid AM, Hall LO, Bezdek JC, Clarke CP, Silbiger ML, Arrington JA, Murtagh RF (1996) Validity-guided (Re) clustering with applications to image segmentation. IEEE Trans Fuzzy Syst 4:112–123CrossRef
40.
Dubes R, Jain AK (1979) Validity studies in clustering methodologies. Pattern Recognition 11:235–253CrossRefMATH
41.
Linde Y, Buzo A, Gray R (1980) An algorithm for vector quantization design. IEEE Trans Commun 28:84–95CrossRef
42.
Gersho A, Ramamurthi B (1982) Image coding using vector quantization. In: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, Paris, May 1982. IEEE, Piscataway, NJ, pp 428–431
43.
Baker RL, Gray RM (1982) Image compression using nonadaptive spatial vector quantization. In: Proceedings of the 16th Asilomar Conference on Circuits, systems, and computers, pp 55–61
44.
Baker RL (1984) Vector quantization of digital images. PhD Dissertation, Stanford University, Stanford, CA
45.
Kim YK, Ra JB (1995) Adaptive learning method in self-organizing map for edge preserving vector quantization. IEEE Trans Neural Networks 6:278–280CrossRefMATH
46.
Park DC, Woo YJ (2001) Weighted centroid neural network for edge preserving image compression. IEEE Trans Neural Networks 12:1134–1146CrossRef
Metadaten
Titel
A new cluster validity measure and its application to image compression
verfasst von
C.-H. Chou
M.-C. Su
E. Lai
Publikationsdatum
01.07.2004
Verlag
Springer-Verlag
Erschienen in
Pattern Analysis and Applications / Ausgabe 2/2004
Print ISSN: 1433-7541
Elektronische ISSN: 1433-755X
DOI
https://doi.org/10.1007/s10044-004-0218-1

Weitere Artikel der Ausgabe 2/2004

Pattern Analysis and Applications 2/2004 Zur Ausgabe

Theoretical Advances

Improving the selection of feature points for tracking

Theoretical Advances

An optical character recognition system for printed Telugu text

Theoretical Advances

A decision based one-against-one method for multi-class support vector machine

Theoretical Advances

Episode detection in videos captured using a head-mounted camera

Theoretical Advances

A probabilistic hit-and-miss transform for face localization

Theoretical Advances

Fusion of appearance-based face recognition algorithms