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This paper describes main classification methods used for symbolic data (e.g. data in form of: single quantitative value, categorical value, interval, multivalued variable, multivaliued variable with weights) presents difficulties of measuring clustering quality for symbolic data (such as lack of "traditional" data matrix), presents which of known indexes like Silhouette index, Ball index,...
Researchers analyzing large (> 100,000 objects) data sets with the methods of
cluster analysis often face the problem of computational complexity of algorithms, that sometimes
makes it impossible to analyze in an acceptable time. Common solution of this problem is to use
less computationally complex algorithms (like k-means), which in turn can in many cases give
much worse results than for...
In real research problems we usually deal with relevant variables and
irrelevant (noisy) variables. Relevant variables sometimes can not be identified, by for
example HINoV method or modified HINoV method. This paper compares effectiveness
detection o f known class structure with application o f symbolic decision trees and
symbolic kernel discriminant analysis in situation where we deal with noisy...
Visualizing data in the form of illustrative diagrams and searching, in these diagrams, for structures, clusters, trends, dependencies etc. is one of the main aims of multivariate statistical analysis. In the case of symbolic data (e.g. data in form of: single quantitative value, categorical values, intervals, multi-valued variables, multi-valued variables with weights), some well-known methods are...
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