The vast amount of information hidden in huge
databases has created tremendous interests in the field of data
mining. This paper examines the possibility of using data clustering
techniques in oral medicine to identify functional relationships
between different attributes and classification of similar patient
examinations. Commonly used data clustering algorithms have been
reviewed and as a result several interesting results have been
gathered.
[1] Jontell, M., Mattsson, U., Torgersson, O.: MedView: An instrument for
clinical research and education in oral medicine. Oral Surg. Oral Med.
Oral Pathol. Oral Radiol. Endod. 99 (2005) 55-63.
[2] Jain, A.K., Murty M.N., and Flynn P.J. (1999): Data Clustering: A
Review.
[3] http://en.wikipedia.org/wiki/Data_clustering, accessed 06/08/26.
[4] "CURE: an efficient clustering algorithm for large databases" Guha S.,
Rastogi R., Shim K. ACM SIGMOD Record 27(2): 73-84, 1998.
[5] T. Zhang, R. Ramakrishnan, and M. Livny, "BIRCH: An Efficient Data
Clustering Method for Very Large Databases," Proc. Conf.
Management of Data (ACM SIGMOD '96), pp. 103-114, 1996.
[6] Data Mining: Practical Machine Learning Tools and Techniques,
Second Edition by Eibe (university Of Waikato, New Zealand) Frank,
Morgan Kaufmann June 2005.
[7] Survey of Clustering Data Mining Techniques. Pavel Berkhin. Accrue
Software, Inc.
[8] http://www.resample.com/xlminer/help/HClst/HClst_ intro.htm,
accessed 07/12/22.
[9] http://en.wikipedia.org/wiki/Data_clustering, accessed 06/08/26.
[10] Anil K. Jain, Richard C. Dubes: Algorithms for Clustering Data.
Prentice-Hall 1988.
[11] CLUTO, 2003. "CLUTO version 2.1.1, Software Package for Clustering
High-Dimensional Datasets",
November2003.http://glaros.dtc.umn.edu/gkhome/views/cluto
[12] Y. Zhao and G. Karypis. Evaluation of hierarchical clustering
algorithms for document datasets. In CIKM, 2002.
[13] Ying Zhao and George Karypis. Criterion functions for document
clustering: Experiments and analysis. Technical Report TR #01-40,
Department of Computer Science, University of Minnesota,
Minneapolis, MN, 2001. http://cs.umn.edu/˜karypis/publications.
[14] http://glaros.dtc.umn.edu/gkhome/cluto/gcluto/ overview, accessed
06/09/20.
[15] wCLUTO: A Web-enabled Clustering Toolkit. Matthew Rasmussen,
Mukund Deshpande, George Karypis, James Johnson, John Crow,
Ernest Retzel. Plant Physiology, Vol. 133, pp. 510ÔÇö516, 2003.
[16] CLUTO * a Clustering Toolkit, Release 2.1.1, George Karypis,
University of Minnesota, Department of Computer Science,
Minneapolis, MN 55455, Technical Report:#02-
017,2003,http://wwwusers.cs.umn.edu/ ~karypis/cluto/index.html .
[1] Jontell, M., Mattsson, U., Torgersson, O.: MedView: An instrument for
clinical research and education in oral medicine. Oral Surg. Oral Med.
Oral Pathol. Oral Radiol. Endod. 99 (2005) 55-63.
[2] Jain, A.K., Murty M.N., and Flynn P.J. (1999): Data Clustering: A
Review.
[3] http://en.wikipedia.org/wiki/Data_clustering, accessed 06/08/26.
[4] "CURE: an efficient clustering algorithm for large databases" Guha S.,
Rastogi R., Shim K. ACM SIGMOD Record 27(2): 73-84, 1998.
[5] T. Zhang, R. Ramakrishnan, and M. Livny, "BIRCH: An Efficient Data
Clustering Method for Very Large Databases," Proc. Conf.
Management of Data (ACM SIGMOD '96), pp. 103-114, 1996.
[6] Data Mining: Practical Machine Learning Tools and Techniques,
Second Edition by Eibe (university Of Waikato, New Zealand) Frank,
Morgan Kaufmann June 2005.
[7] Survey of Clustering Data Mining Techniques. Pavel Berkhin. Accrue
Software, Inc.
[8] http://www.resample.com/xlminer/help/HClst/HClst_ intro.htm,
accessed 07/12/22.
[9] http://en.wikipedia.org/wiki/Data_clustering, accessed 06/08/26.
[10] Anil K. Jain, Richard C. Dubes: Algorithms for Clustering Data.
Prentice-Hall 1988.
[11] CLUTO, 2003. "CLUTO version 2.1.1, Software Package for Clustering
High-Dimensional Datasets",
November2003.http://glaros.dtc.umn.edu/gkhome/views/cluto
[12] Y. Zhao and G. Karypis. Evaluation of hierarchical clustering
algorithms for document datasets. In CIKM, 2002.
[13] Ying Zhao and George Karypis. Criterion functions for document
clustering: Experiments and analysis. Technical Report TR #01-40,
Department of Computer Science, University of Minnesota,
Minneapolis, MN, 2001. http://cs.umn.edu/˜karypis/publications.
[14] http://glaros.dtc.umn.edu/gkhome/cluto/gcluto/ overview, accessed
06/09/20.
[15] wCLUTO: A Web-enabled Clustering Toolkit. Matthew Rasmussen,
Mukund Deshpande, George Karypis, James Johnson, John Crow,
Ernest Retzel. Plant Physiology, Vol. 133, pp. 510ÔÇö516, 2003.
[16] CLUTO * a Clustering Toolkit, Release 2.1.1, George Karypis,
University of Minnesota, Department of Computer Science,
Minneapolis, MN 55455, Technical Report:#02-
017,2003,http://wwwusers.cs.umn.edu/ ~karypis/cluto/index.html .
@article{"International Journal of Information, Control and Computer Sciences:50852", author = "Fahad Shahbaz Khan and Rao Muhammad Anwer and Olof Torgersson", title = "Using Data Clustering in Oral Medicine", abstract = "The vast amount of information hidden in huge
databases has created tremendous interests in the field of data
mining. This paper examines the possibility of using data clustering
techniques in oral medicine to identify functional relationships
between different attributes and classification of similar patient
examinations. Commonly used data clustering algorithms have been
reviewed and as a result several interesting results have been
gathered.", keywords = "Oral Medicine, Cluto, Data Clustering, Data Mining.", volume = "2", number = "1", pages = "41-5", }