Weighted Generalised Affinity Coefficient in Cluster Analysis of Complex Data of the Interval Type
Áurea Sousa, Fernando C. Nicolau, Helena Bacelar‐Nicolau, Osvaldo Silva · Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) · 2010
Complex Data Analysis is a relatively new field that provides a range of methods for analyzing complex/symbolic data, and can be defined as the extension of standard data analysis to more complex data tables. There are two steps in Complex or Symbolic Data Analysis: i) knowledge extraction from large databases as in Data Mining; and ii) application of new tools to the extracted knowledge in order to extend Data Mining to Knowledge Mining. The weighted generalized affinity coefficient appears to be an appropriate resemblance measure between elements (statistical data units or variables) in cases where we deal with complex data from large databases. In this work we apply two different processes to determine values of the weighted generalized affinity coefficient in the case where we are dealing with data units described by variables whose values are intervals of the real axis. We present one example concerned with real data (with a known structure) in the field of Biometry, in which objects are described by variables whose values are intervals, in order to illustrate the effectiveness of Ascendant Hierarchical Cluster Analysis based on the weighted generalized affinity coefficient and classical and/or probabilistic aggregation criteria. In this example, we applied a method of validation to identify the best partitions.