Maria M. Suarez-Alvarez

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Pre-processing or normalisation of data sets is widely used in a number of fields of machine intelligence. Contrary to the overwhelming majority of other normalisation procedures, when data is scaled to a unit range, it is argued in the paper that after normalisation of a data set, the average contributions of all features to the measure employed to assess(More)
Normalization of feature vectors of datasets is widely used in a number of fields of data mining, in particular in cluster analysis, where it is used to prevent features with large numerical values from dominating in distance-based objective functions. In this study, a unified statistical approach to normalization of all attributes of mixed databases, when(More)
A new algorithm to cluster datasets with mixed numerical and categorical values is presented. The algorithm, called RANKPRO (random search with k-prototypes algorithm), combines the advantages of a recently introduced population-based optimization algorithm called the bees algorithm (BA) and k-prototypes algorithm. The BA works with elite and good(More)
Normalization of feature vectors is often used as a step of data preprocessing for clustering. A unified statistical approach to feature vector normalization has been proposed recently by the authors. After the proposed normalization, the contributions of both numerical and categorical attributes to a specified objective function are statistically the same.(More)
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