A fuzzy approach for interpretation of ubiquitous data stream clustering and its application in road safety

Ubiquitous Data Mining is the process of analysing data emanating from distributed and heterogeneous sources in the form of a continuous stream with mobile and/or embedded devices. Unsupervised learning is clearly beneficial for initial understanding of data streams, and consequently various clustering algorithms have been developed and applied in UDM systems for the purpose of mining data streams. However, unsupervised data mining techniques require human intervention for further understanding and analysis of the clustering results. This becomes an issue as UDM applications aim to support mobile and highly dynamic users/applications and there is a need for real-time decision making and interpretation of results. In this paper we present an approach to automate the annotation of results obtained from ubiquitous data stream clustering to facilitate interpreting and use of the results to enable real-time, mobile decision making.