Higher Order Mining from Sources with Different Competences

The value of data mining on large quantities of data is well known. However there are cases when we canpsilat access directly the raw data, such as: (i) institutions interested in sharing knowledge may not be allowed to share the raw data; (ii) data is in form of streams and it is only temporarily available for processing; (iii) finally there may also be limits on the computation speed that could be achieved. Therefore the data must be summarized to be processed efficiently. In this paper we consider mining patterns retrieved from sources with different competences where it is needed to select only the ldquomostrdquo supported knowledge through the sources. The method proposed is based on the knowledge uniformization and apply a common algorithm that will select the most supported knowledge taking into account the competency of the sources.