Pengembangan Algoritma Penentuan Titik Awal Dalam Metode Clustering Algoritma Fuzzy C-Means
61
DAFTAR PUSTAKA
Ackerman, Marcel.R, Blommer,J, Daniel Kuntze & Sohler,C. 2014. Analysis of
Agglomerative Clustering.Algorithmica69, Issue 1, pp 184-215. DOI
:10.1007/s00453-012-9717-4
Agarwal, C. C. & Reddy, C.K. 2014 (Editor). Data Clustering Algorithms and
Applications, Chapman & Hall/CRC Book,. CRC Press, Taylor and Francis
Group: Data Mining and Knowledge Discovery Series., ISBN-13: 978-1-46655822-9.
Beevi, S.Z., Satik, M.M & Senthamaraikannan,K. 2010. A Robush Fuzzy Clustering
Tehnique with Spacial Neighborhood Information for Effective Medical Image
Segmentation.(IJCSIS) International Journal of Computer Science and
Information Security7. Issue 3, March 2010 pp 132-138.
Bezdek, J. C. 1981. Pattern Recognition With Fuzzy Objective Function Algorithm,
Utah State University.,Logan, Utah: Library of Congress Cataloging in
Publication Data.,DOI 10.1007/978-1-4757-0450-1.
Bezdek,J.C., Ehlrich,R., & Full, W. 1984. FCM: The Fuzzy c-Means Clustering
Algorithm. Computer and GeoSciences10. Issue 2-3, pp 191-203. Pergamon
Press Ltd.U.S.A.
Cheng, T.W., Goldgof, D.B & Hall, L.O. 1998. Fast Fuzzy Clustering,. Journal of
Fuzzy Sets and Systems93, Issue 1(January 1998) pp 49-56.
Das, A. 2013. Pattern Recognition Using Fuzzy C-Means Tehnique. Information of
Communications. International Journal of Energy4, Issue 1 (February 2013)
pp 1-14.
Everitt, B.S., Landau,S., Leese,M. & Stahl, D. 2011. Cluster Analysis 5th Edition.
King’s College, London. Willey Series in Probability and Statistics. Jhon
Willey & Sons, Ltd. UK.
Freitas, F.D.A. & Perez, S.M. 2014. Gramatical Facial Expressions Data Set,.
Machine Learning Repositori, UCI,. https://archive.ics.uci.edu/ml/datasets/
Grammatical+Facial+Expressions, (Download, 5 Desember 2014).
Freitas, F.D.A. & Perez, S.M. 2014. Gramatical Facial Expressions Recognition with
Machine Learning.Proceeding of The Twenty Seventh International Florida
Artificial Intelegence Research Society Conference. Florida. pp 180-185.
Hastie, T., Tibshirani, R. & Friedman, J. 2010. The Element of Statistical Learning,
Data Mining, Inference and Prediction 2nd Edition. Springer Series in
Statistics. Springer,2010.
Hung, M. C. & Yang,D.L. 2001. An Efficient Fuzzy C-Means Clustering Algoritm.
Departement of Information Engineering, Proceedings IEEE International
Conference, pp 225 – 232.
62
Karlina, T., Afrida, H & Arifin, F, 2006. Pengembangan Algoritma K-Modes pada
Penentuan Titik Pusat Awal untuk Mengelompokkan Penyakit pada Kacang
Kedelai. Prosiding IES-2006-Politeknik Elektronika Negeri Surabaya-ITS, pp
253-259.
Khan, S. S. & Ahmad, A. 2004. Cluster Center Initialisation Algorithm for K-Means
Clustering, Pattern Recognition Letters25, Issue 11(August 2004) pp 12931302.
Maimon, O,. & Rokach, L. (Editor). 2010. Data Mining and Knowledge Discovery
Handbook Second Edition. Springer, DOI 10.1007/978-0-387-09823-4.
Oliveira, J.V.D & Pedrycz,W (Editor). 2007. Advance in Fuzzy Clustering., The
Atrium, Southern Gate, Chichester. British Library Cataloguing in Publication
Data. .Jhon Willey and Son,Ltd. England.
Pal, N.L., Pal, K., Keller, J.M. & Bezdek, J.C. 2005. A Possibilistic Fuzzy c-Means
Clustering Algorithm. IEEE Transactions On Fuzzy Systems3, Issue 4, August
2005.
Parker, J.K. 2013.Accelerated Fuzzy Clustering. Dissertation Ph.D. University of
South Florida.
Sembiring, R.W., Zain, J.M & Embong, A. 2010. A Comparative Agglomerative
Hierarchical Clustering Method to Cluster Implemented Course. Jurnal of
Computing2. Issue 12,. December 2010.
Su, X.,Wang,X., Wang, Z. & Xiao,Y. 2010. An New Fuzzy Clustering Algorithm
Base on Entrophy Weighting,. Journal of Computational Information System6.
Issue 1(October 2010) pp 3319-3326.
Saad, F.M & Alimi,A.M, 2009. Modified Fuzzy Possibilistic C-means,. IMECS
Hongkong: Proceedings of International Multi Conference of Engineers and
Computer Scientists Vol 1,. pp 23-27.
Sugeno,M & Yasukawa,T. 1993. A Fuzzy-Logic-Based Approach to Qualitative
Modeling. Transactions of Fuzzy Systems1., Issue 1, 1 February 1993 pp 7-31.
Valarmathie.P., MP, Srinath., Ravichandran, T. & Dinakaran,K,. 2009. Hybrid Fuzzy
C-Means Clustering Technique for Gene Expression Data, International
Journal of Research and Reviews in Applied Sciences1, Issue 1(Oct 2009), pp
33-37.
Yatracos, Y.G. 1998. Variance and Clustering. Proceeding of The American
Mathematical Society126. Issue 4,. April 1998,. Pp 1177-1179.
Yang, H.,Han,Y & Yu,F. 2005. Improve Fuzzy C-Means Clustering Algorithm Base
on Sample Density,. Journal of Theoretical and Applied Information
Technology48. Issue 1 (February 2005) pp 210-214.
Weis, D.J. 2006. Analysis of Variance and Functional Measurement:A Practical
Guide. Oxford University Press.
63
Zarandi, F.H.M., Zarinbal,M. & Turksen, I.B. 2009. Type II Fuzzy Possibilistic CMeans Clustering,. IFSA-EUSFLAT Asturias, Spain: Proceedings European
Center of Soft Computing (2009) pp 30-35.
DAFTAR PUSTAKA
Ackerman, Marcel.R, Blommer,J, Daniel Kuntze & Sohler,C. 2014. Analysis of
Agglomerative Clustering.Algorithmica69, Issue 1, pp 184-215. DOI
:10.1007/s00453-012-9717-4
Agarwal, C. C. & Reddy, C.K. 2014 (Editor). Data Clustering Algorithms and
Applications, Chapman & Hall/CRC Book,. CRC Press, Taylor and Francis
Group: Data Mining and Knowledge Discovery Series., ISBN-13: 978-1-46655822-9.
Beevi, S.Z., Satik, M.M & Senthamaraikannan,K. 2010. A Robush Fuzzy Clustering
Tehnique with Spacial Neighborhood Information for Effective Medical Image
Segmentation.(IJCSIS) International Journal of Computer Science and
Information Security7. Issue 3, March 2010 pp 132-138.
Bezdek, J. C. 1981. Pattern Recognition With Fuzzy Objective Function Algorithm,
Utah State University.,Logan, Utah: Library of Congress Cataloging in
Publication Data.,DOI 10.1007/978-1-4757-0450-1.
Bezdek,J.C., Ehlrich,R., & Full, W. 1984. FCM: The Fuzzy c-Means Clustering
Algorithm. Computer and GeoSciences10. Issue 2-3, pp 191-203. Pergamon
Press Ltd.U.S.A.
Cheng, T.W., Goldgof, D.B & Hall, L.O. 1998. Fast Fuzzy Clustering,. Journal of
Fuzzy Sets and Systems93, Issue 1(January 1998) pp 49-56.
Das, A. 2013. Pattern Recognition Using Fuzzy C-Means Tehnique. Information of
Communications. International Journal of Energy4, Issue 1 (February 2013)
pp 1-14.
Everitt, B.S., Landau,S., Leese,M. & Stahl, D. 2011. Cluster Analysis 5th Edition.
King’s College, London. Willey Series in Probability and Statistics. Jhon
Willey & Sons, Ltd. UK.
Freitas, F.D.A. & Perez, S.M. 2014. Gramatical Facial Expressions Data Set,.
Machine Learning Repositori, UCI,. https://archive.ics.uci.edu/ml/datasets/
Grammatical+Facial+Expressions, (Download, 5 Desember 2014).
Freitas, F.D.A. & Perez, S.M. 2014. Gramatical Facial Expressions Recognition with
Machine Learning.Proceeding of The Twenty Seventh International Florida
Artificial Intelegence Research Society Conference. Florida. pp 180-185.
Hastie, T., Tibshirani, R. & Friedman, J. 2010. The Element of Statistical Learning,
Data Mining, Inference and Prediction 2nd Edition. Springer Series in
Statistics. Springer,2010.
Hung, M. C. & Yang,D.L. 2001. An Efficient Fuzzy C-Means Clustering Algoritm.
Departement of Information Engineering, Proceedings IEEE International
Conference, pp 225 – 232.
62
Karlina, T., Afrida, H & Arifin, F, 2006. Pengembangan Algoritma K-Modes pada
Penentuan Titik Pusat Awal untuk Mengelompokkan Penyakit pada Kacang
Kedelai. Prosiding IES-2006-Politeknik Elektronika Negeri Surabaya-ITS, pp
253-259.
Khan, S. S. & Ahmad, A. 2004. Cluster Center Initialisation Algorithm for K-Means
Clustering, Pattern Recognition Letters25, Issue 11(August 2004) pp 12931302.
Maimon, O,. & Rokach, L. (Editor). 2010. Data Mining and Knowledge Discovery
Handbook Second Edition. Springer, DOI 10.1007/978-0-387-09823-4.
Oliveira, J.V.D & Pedrycz,W (Editor). 2007. Advance in Fuzzy Clustering., The
Atrium, Southern Gate, Chichester. British Library Cataloguing in Publication
Data. .Jhon Willey and Son,Ltd. England.
Pal, N.L., Pal, K., Keller, J.M. & Bezdek, J.C. 2005. A Possibilistic Fuzzy c-Means
Clustering Algorithm. IEEE Transactions On Fuzzy Systems3, Issue 4, August
2005.
Parker, J.K. 2013.Accelerated Fuzzy Clustering. Dissertation Ph.D. University of
South Florida.
Sembiring, R.W., Zain, J.M & Embong, A. 2010. A Comparative Agglomerative
Hierarchical Clustering Method to Cluster Implemented Course. Jurnal of
Computing2. Issue 12,. December 2010.
Su, X.,Wang,X., Wang, Z. & Xiao,Y. 2010. An New Fuzzy Clustering Algorithm
Base on Entrophy Weighting,. Journal of Computational Information System6.
Issue 1(October 2010) pp 3319-3326.
Saad, F.M & Alimi,A.M, 2009. Modified Fuzzy Possibilistic C-means,. IMECS
Hongkong: Proceedings of International Multi Conference of Engineers and
Computer Scientists Vol 1,. pp 23-27.
Sugeno,M & Yasukawa,T. 1993. A Fuzzy-Logic-Based Approach to Qualitative
Modeling. Transactions of Fuzzy Systems1., Issue 1, 1 February 1993 pp 7-31.
Valarmathie.P., MP, Srinath., Ravichandran, T. & Dinakaran,K,. 2009. Hybrid Fuzzy
C-Means Clustering Technique for Gene Expression Data, International
Journal of Research and Reviews in Applied Sciences1, Issue 1(Oct 2009), pp
33-37.
Yatracos, Y.G. 1998. Variance and Clustering. Proceeding of The American
Mathematical Society126. Issue 4,. April 1998,. Pp 1177-1179.
Yang, H.,Han,Y & Yu,F. 2005. Improve Fuzzy C-Means Clustering Algorithm Base
on Sample Density,. Journal of Theoretical and Applied Information
Technology48. Issue 1 (February 2005) pp 210-214.
Weis, D.J. 2006. Analysis of Variance and Functional Measurement:A Practical
Guide. Oxford University Press.
63
Zarandi, F.H.M., Zarinbal,M. & Turksen, I.B. 2009. Type II Fuzzy Possibilistic CMeans Clustering,. IFSA-EUSFLAT Asturias, Spain: Proceedings European
Center of Soft Computing (2009) pp 30-35.