Fuzzy-C-Means-Based Classification of Thermodynamic-Property Data: A Critical Assessment

REFERENCES

• O. Baker , Design of Pipelines for the Simultaneous Flow of Oil and Gas , Oil and Gas J. , vol. 53 , pp. 185 – 195 , 1954 .
   Google Scholar
• J. M. Mandhane , G. A. Gregory , and K. A. Aziz , A Flow Pattern Map for Gas-Liquid Flow in Horizontal Pipeline , Int. J. Multiphase Flow , vol. 1 , pp. 537 – 553 , 1974 .
   Google Scholar
• S. Wongwises , T. Wongchang , J. Kaewon , and C. C. Wang , A Visual Study of Two-Phase Flow Patterns of HFC-134a and Lubricant Oil Mixtures, Heat Transfer Eng. , vol. 23, pp. 13–22, 2002.
   Web of Science ®Google Scholar
• K. de Queiroz and D. A. Good , Phenetic Clustering in Biology: A Critique , Quarterly Rev. of Biology , vol. 72 , no. 1 , pp. 3 – 30 , 1997 .
   Web of Science ®Google Scholar
• P. E. Green , R. E. Frank , and P. J. Robinson , Cluster Analysis in Test Market Selection , Management Sci. , vol. 13 , pp. B387 – B400 , 1967 .
   Google Scholar
• W. J. Chen , M. L. Giger , U. Bick , and G. M. Newstead , Automatic Identification and Classification of Characteristic Kinetic Curves of Breast Lesions on DCE-MRI , Med. Physi. , vol. 33 , no. 8 , pp. 2878 – 2887 , 2006 .
   PubMed Web of Science ®Google Scholar
• K. W. Johnson , P. M. Langdon , and M. F. Ashby , Grouping Materials and Processes for the Designer: An Application of Cluster Analysis , Materi. and Des. , vol. 23 , no. 1 , pp. 1 – 10 , 2002 .
   Web of Science ®Google Scholar
• D. Guo , A. Weeks , and H. Klee , Robust Approach for Suburban Road Segmentation in High-Resolution Aerial Images , Inter. J. of Remote Sensing , vol. 28 , no. 1–2 , pp. 307 – 318 , 2007 .
   Google Scholar
• M. Faúndez-Abans , M. I. Ormeno , and M. de Oliveira-Abans , Classification of Planetary Nebulae by Cluster Analysis and Artificial Neural Networks , Astronomy Astrophysics Supplement Series , vol. 116 , no. 2 , pp. 395 – 402 , 1996 .
   Google Scholar
• A. K. Jain , M. N. Murty , and P. J. Flynn , Data Clustering: A Review , ACM Computing Surveys , vol. 31 , no. 3 , pp. 264 – 323 , 1999 .
   Web of Science ®Google Scholar
• X. Gao and W. Xie , Advances in Theory and Aplications of Fuzzy Clustering , Chinese Sci. Bull. , vol. 45 , no. 11 , pp. 961 – 970 , 2000 .
   Web of Science ®Google Scholar
• A. Pacheco-Vega , G. Díaz , M. Sen , K. T. Yang , and R. L. McClain , Heat Rate Predictions in Humid Air-Water Heat Exchangers using Correlations and Neural Networks , ASME J. Heat Transfer , vol. 123 , no. 2 , pp. 348 – 354 , 2001 .
   Web of Science ®Google Scholar
• A. Vernet and G. A. Kopp , Classification of Turbulent Flow Patterns with Fuzzy Clustering , Eng. Appl. of Artificial Intelligence , vol. 15 , no. 3–4 , pp. 315 – 326 , 2002 .
   Google Scholar
• H. Paasche and J. Tronicke , Cooperative Inversion of 2D Geophysical Data Sets: A Zonal Approach Based on Fuzzy C-Means Cluster Analysis , Geophysics , vol. 72 , no. 3 , pp. A35 – A39 , 2007 .
   Web of Science ®Google Scholar
• J. C. Dunn , A Fuzzy Relative of the ISODATA Process and Its use in Detecting Compact Well-Separated Clusters , Cybernetics and Systems: An Inter. J. , vol. 3 , no. 3 , pp. 32 – 57 , 1973 .
   Google Scholar
• R. Duda and P. Hart , Pattern Classification and Scene Analysis , Wiley Interscience , New York , 1973 .
   Google Scholar
• L.A. Zadeh , Fuzzy Sets , Information & Control , vol. 8 , pp. 338 – 353 , 1965 .
   Web of Science ®Google Scholar
• A. Baraldi and P. Blonda , A Survey of Fuzzy Clustering Algorithms for Pattern Recognition-Part I , IEEE Trans. Sys., Man, Cyber.-Part B: Cybernetics , vol. 9 , no. 6 , pp. 778 – 785 , 1999 .
   Google Scholar
• A. Baraldi and P. Blonda , A Survey of Fuzzy Clustering Algorithms for Pattern Recognition-Part II , IEEE Trans. Sys., Man, Cyber.-Part B: Cybernetics , vol. 9 , no. 6 , pp. 786 – 801 , 1999 .
   Google Scholar
• K. S. Chuang , H. L. Tzeng , S. Chen , J. Wu , and T. J. Chen , Fuzzy C-Means Clustering with Spatial Information for Image Segmentation , Computerized Medical Imaging and Graphics , vol. 30 , no. 1 , pp. 9 – 15 , 2006 .
   PubMed Web of Science ®Google Scholar
• G. Usera , A. Vernet , J. Pallares , and J. A. Ferré , A Conditional Sampling Method Based on Fuzzy Clustering for the Analysis of Large-Scale Dynamics in Turbulent Flows , Euro. J. of Mech. B: Fluids , vol. 25 , no. 2 , pp. 172 – 191 , 2006 .
   Web of Science ®Google Scholar
• P. Willett , J. M. Barnard , and G. M. Downs , Chemical Similarity Searching , J. Chem. Inf. Comput. Sci. , vol. 38 , no. 6 , pp. 983 – 996 , 1998 .
   Google Scholar
• J. D. Holliday , S. L. Rodgers , P. Willett , M. Y. Chen , M. Mahfouf , K. Lawson , and G. Mullier , Clustering Files of Chemical Structures using the Fuzzy K-Means Clustering Method, J. Chem. Inf. Comput. Sci. , vol. 44, no. 3, pp. 894–902, 2004.
   PubMedGoogle Scholar
• P. D'urso , Fuzzy C-Means Clustering Models for Multivariate Time-Varying Data: Different Approaches , Int. J. Uncertainty Fuzziness Knowledge-Based Sys. , vol. 12 , no. 3 , pp. 287 – 326 , 2004 .
   Web of Science ®Google Scholar
• S. Kobashi , Y. Hata , Y. T. Kitamura , T. Hayakata , and T. Yanagida , Brain State Recognition using Fuzzy C-Means (FCM) Clustering with Near Infrared Spectroscopy (NIRS) , Lecture Notes in Comp. Sci. , vol. 2206 , pp. 124 – 136 , 2001 .
   Google Scholar
• F. Höppner , F. Klawonn , R. Kruse , and T. Runkler , Fuzzy Cluster Analysis: Methods for Classification, Data Analysis and Image Recognition , Wiley and Sons , Baffins Lane , Chichester , 1999 .
   Google Scholar
• J. C. Bezdek , J. M. Keller , R. Krishnapuram , and N. R. Pal , Fuzzy Models and Algorithms for Pattern Recognition and Image Processing , Springer , New York , 2005 .
   Google Scholar
• J. C. Bezdek , Pattern Recognition with Fuzzy Objetive Function Algorithms , Plenum Press , New York , 1981 .
   Google Scholar
• X. L. L. Xie and G. A. Beni , A Validity Measure for Fuzzy Clustering , IEEE Trans. on Pattern Analysis and Machine Intelligence , vol. 13 , no. 8 , pp. 841 – 847 , 1991 .
   Web of Science ®Google Scholar
• M. Bouguessa , S. R. Wang , and H. J. Sun , An Objective Approach to Cluster Validation , Pattern Recognition Letters , vol. 27 , no. 13 , pp. 1419 – 1430 , 2006 .
   Web of Science ®Google Scholar
• F. Okeke and A. Karnieli , Linear Mixture Model Approach for Selecting Fuzzy Exponent Value in Fuzzy C-Means Algorithm , Ecological Informatics , vol. 1 , no. 1 , pp. 117 – 124 , 2006 .
   Web of Science ®Google Scholar
• R. E. Sonntag and G. Van Wylen , Introduction to Thermodynamics: Classical and Statistical , Wiley and Sons , New York , 1982 .
   Google Scholar
• W. Wagner and A. Pruß , The IAPWS Formulation 1995 for the Thermodynamic Properties of Ordinary Water Substance for General and Scientific Use , J. Physical and Chemical Reference Data , vol. 31 , no. 2 , pp. 387 – 535 , 2002 .
   Web of Science ®Google Scholar
• B. S. Everitt , S. Landau , and L. Morven , Cluster Analysis, , 4th ed. , Arnold , New York , 2001 .
   Google Scholar
• J. Han and M. Kamber , Data Mining: Concepts and Techniques , Morgan Kaufmann Publishers , San Francisco , CA , 2006 .
   Google Scholar
• S. Y. Kim , J. W. Lee , and J. S. Bae , Effect of Data Normalization on Fuzzy Clustering of DNA Microarray Data , BMC Bioinformatics , 7 (Article number 134) , 2006 .
   Google Scholar
• X. Li , X. Lu , J. Tian , P. Gao , H. Kong , and G. Xu , Application of Fuzzy C-Means Clustering in Data Analysis of Metabolomics , Analytical Chem. , vol. 81 , no. 11 , pp. 4468 – 4475 , 2009 .
   PubMed Web of Science ®Google Scholar
• S. M. Scott , D. James , and Z. Ali , Data Analysis for Electronic Nose Systems , Microchimica Acta , vol. 156 , no. 3–4 , pp. 183 – 207 , 2006 .
   Google Scholar
• R. Aruga , P. Mirti , and V. Zelano , Influence of Transformation and Scaling of Archaeometric Data on Clustering and Visual-Display , Analusis , vol. 18 , no. 10 , pp. 597 – 598 , 1990 .
   Google Scholar
• A. Pacheco-Vega , M. Sen , K. T. Yang , and R. L. McClain , Genetic-Algorithm-Based-Predictions of Fin-Tube Heat Exchanger Performance , in J. S. Lee , (ed.), Proceedings of the Eleventh International Heat Transfer Conference , vol. 6 , pp. 137 – 142 , New York , August 1998 , Taylor & Francis .
   Google Scholar
• A. Pacheco-Vega , M. Sen , and K. T. Yang , Simultaneous Determination of In- and Over-Tube Heat Transfer Correlations in Heat Exchangers by Global Regression , Int. J. Heat Mass Transfer , vol. 46 , no. 6 , pp. 1029 – 1040 , 2003 .
   Web of Science ®Google Scholar