TY - GEN A1 - Galliat, Tobias T1 - Clustering Data of Different Information Levels N2 - For using Data Mining, especially cluster analysis, one needs measures to determine the similarity or distance between data objects. In many application fields the data objects can have different information levels. In this case the widely used euclidean distance is an inappropriate measure. The present paper describes a concept how to use data of different information levels in cluster analysis and suggests an appropriate similarity measure. An example from practice is included, that shows the usefulness of the concept and the measure in combination with {\sc Kohonens} Self-Organizing Map algorithm, a well-known and powerful tool for cluster analysis. T3 - ZIB-Report - SC-99-42 KW - cluster analysis KW - Data Mining KW - data preprocessing KW - information theory KW - missing values KW - Self-Organizing Maps KW - similarity measures Y1 - 1999 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-4319 ER - TY - GEN A1 - Galliat, Tobias A1 - Huisinga, Wilhelm A1 - Deuflhard, Peter T1 - Self-Organizing Maps Combined with Eigenmode Analysis for Automated Cluster Identification N2 - One of the important tasks in Data Mining is automated cluster analysis. Self-Organizing Maps (SOMs) introduced by {\sc Kohonen} are, in principle, a powerful tool for this task. Up to now, however, its cluster identification part is still open to personal bias. The present paper suggests a new approach towards automated cluster identification based on a combination of SOMs with an eigenmode analysis that has recently been developed by {\sc Deuflhard et al.} in the context of molecular conformational dynamics. Details of the algorithm are worked out. Numerical examples from Data Mining and Molecular Dynamics are included. T3 - ZIB-Report - SC-99-38 KW - Self-Organizing Maps KW - cluster analysis Y1 - 1999 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0297-zib-4279 ER -