Rosana Veroneze

  • Citations Per Year
Learn More
Biclustering involves the simultaneous clustering of objects and their attributes, thus defining local two-way clustering models. Recently, efficient algorithms were conceived to enumerate all biclusters in real-valued datasets. In this case, the solution composes a complete set of maximal and non-redundant biclusters. However, the ability to enumerate(More)
Biclustering has proved to be a powerful data analysis technique due to its wide success in various application domains. However, the existing literature presents efficient solutions only for enumerating maximal biclusters with constant values, or heuristic-based approaches which can not find all biclusters or even support the maximality of the obtained(More)
Although the missing data problem has been studied for many years, it is still a relevant and challenging problem nowadays. Data can be missing for a variety of reasons, and there are several techniques capable of processing missing data. A parcel of them tries to estimate the missing values. This technique is called imputation. Recently, it was proposed a(More)
This paper presents a novel enumerative biclustering algorithm to directly mine all maximal biclusters in mixed-attribute datasets, with or without missing values. The independent attributes are mixed or heterogeneous, in the sense that both numerical (real or integer values) and categorical (ordinal or nominal values) attribute types may appear together in(More)
  • 1