Gravitational fuzzy clustering

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5317 LNAI, Issue , 2008, Pages 524-531

  Orhan, Umut ^a   Hekim, Mahmut ^a   Ibrikci, Turgay ^b  

^a GAZIOSMANPASA UNIVERSITY   (Turkey)

^b CUKUROVA UNIVERSITY   (Turkey)

Author keywords

Fuzzy c means; Gravitational fuzzy clustering; K means; Subtractive clustering

Indexed keywords

ARTIFICIAL INTELLIGENCE; BIONICS; CLUSTER ANALYSIS; CLUSTERING ALGORITHMS; EXTRACTIVE METALLURGY; FLOW OF SOLIDS; FUZZY SETS; FUZZY SYSTEMS; GRAVITATION; GRAVITATIONAL EFFECTS; THEOREM PROVING;

CLUSTER CENTERS; CONSTANT MASSES; DATA CLUSTERING; DATA POINTS; FUZZY C-MEANS; GRAVITATIONAL FUZZY CLUSTERING; GRAVITY CENTERS; INITIAL CONDITIONS; K-MEANS; NEW ALGORITHMS; NEWTON'S LAWS; NOVEL METHODS; SUBTRACTIVE CLUSTERING;

FUZZY CLUSTERING;

EID: 57049162942     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-88636-550     Document Type: Conference Paper

References (14)

1. Hekim, M., Orhan, U.: A Validity Measure for a New Hybrid Data Clustering. In: International Symposium on Innovations in Intelligent Systems and Applications, Istanbul, pp. 70-74 (2007)
2. Jain, A.K., Murty, M.N., Flynn, P.J.: Data Clustering: A Review. ACM Computing Surveys 31(3), 264-323 (1999)
3. Jain, A.K., Duin, R.P.W., Mao, J.: Statistical Pattern Recognition: A Review. IEEE Transactions on Pattern Analysis and Machine Intelligence 22(1), 4-36 (2000)
4. Berkhin, P.: Survey of Clustering Data Mining Techniques. Grouping Multidimensional Data, 25-71 (2006)
5. Cheng, Y.: Mean Shift, Mode Seeking, and Clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence 17(8), 790-799 (1995)
6. Comaniciu, D., Meer, P.: Mean Shift: A Robust Approach toward Feature Space Analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(5), 603-619 (2002)
7. Comaniciu, D.: An Algorithm for Data-Driven Bandwidth Selection. IEEE Transactions on Pattern Analysis and Machine Intelligence 25(2), 281-288 (2003)
8. Tsoukalas, L., Uhrig, R.: Fuzzy and Neural Approaches in Engineering. John Wiley & Sons, Inc., NY (1997)
9. Yu, J.: General C-Means Clustering Model: A Unified Approach to the Theories and Methods of Partitional Clustering Algorithms. Technical Report, AI Lab, Northern Jiaotong Univ., Beijing (2003)
10. Baraldi, A., Blonda, P.: A Survey of Fuzzy Clustering Algorithms for Pattern Recognition-Part I and II. IEEE Transactions on Systems, Man and Cybernetics, Part B 29(6), 778-801 (1999)
11. Baraldi, A., Blonda, P., Parmiggiani, F., Pasquariello, G., Satalino, G.: Model Transitions in Descending FLVQ. IEEE Transactions on Neural Networks 9(5), 724-738 (1998)
12. Li, R.P., Mukaidono, M.: Gaussian Clustering Method Based on Maximum-Fuzzy-Entropy Interpretation. Fuzzy Sets and Systems 102(2), 253-258 (1999)
13. Ross, T.J.: Fuzzy Logic with Engineering Applications. McGraw-Hill, New York (1995)
14. Hand, D.J., Daly, F., Lunn, A.D., McConway, K.J., Ostrowski, E.: A Handbook of Small Data Sets. Chapman and Hall, London (1994)