Using Generic Meta-Data-Models for Clustering Medical Data

We present a generic, meta-model based data storage system for research, clinical studies or disease registers, which is enabled to store data of almost arbitrary structure. The system is highly costumizeable and allows the user to set up a professional web-based data acquisition system including administration area, data input forms, overview tables and statistics within hours. Furthermore, we evaluated a number of clustering algorithms regarding their ability to cluster the stored datasets for similarity search and further statistical analysis.

[1]  Peter E. Hart,et al.  Nearest neighbor pattern classification , 1967, IEEE Trans. Inf. Theory.

[2]  Jill P. Mesirov,et al.  Consensus Clustering: A Resampling-Based Method for Class Discovery and Visualization of Gene Expression Microarray Data , 2003, Machine Learning.

[3]  Ah Chung Tsoi,et al.  Clustering XML Documents Using Self-organizing Maps for Structures , 2005, INEX.

[4]  John W. Sammon,et al.  A Nonlinear Mapping for Data Structure Analysis , 1969, IEEE Transactions on Computers.

[5]  Alessio Micheli,et al.  A general framework for unsupervised processing of structured data , 2004, Neurocomputing.

[6]  Rich Caruana,et al.  Meta Clustering , 2006, Sixth International Conference on Data Mining (ICDM'06).

[7]  Ah Chung Tsoi,et al.  A self-organizing map for adaptive processing of structured data , 2003, IEEE Trans. Neural Networks.

[8]  Vladimir Filkov,et al.  Consensus Clustering Algorithms: Comparison and Refinement , 2008, ALENEX.

[9]  Ah Chung Tsoi,et al.  A supervised training algorithm for self-organizing maps for structures , 2005, Pattern Recognit. Lett..

[10]  Paul S. Bradley,et al.  Refining Initial Points for K-Means Clustering , 1998, ICML.

[11]  Teuvo Kohonen,et al.  The self-organizing map , 1990, Neurocomputing.

[12]  R. Fisher.  THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS , 1936 .

[13]  Alberto Muñoz,et al.  Extending the SOM Algorithm to Non-Euclidean Distances via the Kernel Trick , 2004, ICONIP.

[14]  Anil K. Jain,et al.  Data clustering: a review , 1999, CSUR.

[15]  Bala Srinivasan,et al.  Dynamic self-organizing maps with controlled growth for knowledge discovery , 2000, IEEE Trans. Neural Networks Learn. Syst..

[16]  Reinhold Haux,et al.  Medical Data Management: A Practical Guide , 2003 .

[17]  Johannes Dirnberger,et al.  Meta-model based knowledge discovery , 2011, 2011 International Conference on Data and Knowledge Engineering (ICDKE).

[18]  Gabriella Kazai,et al.  Advances in XML Information Retrieval and Evaluation, 4th International Workshop of the Initiative for the Evaluation of XML Retrieval, INEX 2005, Dagstuhl Castle, Germany, November 28-30, 2005, Revised Selected Papers , 2006, INEX.