Centric Model Assessment for Collaborative Data Mining

Abstract

Data mining is the task of discovering interesting patterns from large amounts of data. There are many data mining tasks, such as classification, clustering, association rule mining and sequential pattern mining. Sequential pattern mining finds sets of data items that occur together frequently in some sequences. Collaborative data mining refers to a data mining setting where different groups are geographically dispersed but work together on the same problem in a collaborative way. Such a setting requires adequate software support. Group work is widespread in education. The goal is to enable the groups and their facilitators to see relevant as pects of the groups operation and provide feedbacks if these are more likely to be associated with positive or negative outcomes and where the problems are. We explore how useful mirror information can be extracted via a theory-driven approach and a range of clustering and sequential pattern mining. In this paper we describe an experiment with a simple implementation of such a collaborative data mining environment. The experiment brings to light several problems, one of which is related to model assessment. We discuss several possible solutions. This discussion can contribute to a better understanding of how collaborative data mining is best organized.