Sebastian Lühr and Mihai Lazarescu (2009) Incremental Clustering of Dynamic Data Streams Using Connectivity Based Representative Points. In Data and Knowledge Engineering, 68(1):1-27.

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Abstract: We present an incremental graph based clustering algorithm whose design was motivated by a need to extract and retain meaningful information from data streams produced by applications such as large scale surveillance, network packet inspection and financial transaction monitoring. To this end, the method we propose utilises representative points to both incrementally cluster new data and to selectively retain important cluster information within a knowledge repository. The repository can then be subsequently used to assist in the processing of new data, the archival of critical features for off-line analysis, and in the identification of recurrent patterns.

Sebastian Lühr, Geoff West and Svetha Venkatesh (2007) Recognition of Emergent Human Behaviour in a Smart Home: A Data Mining Approach. In Pervasive and Mobile Computing, 3(2):95-116.

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Abstract: Motivated by a growing need for intelligent housing to accommodate aging populations, we propose a novel application of intertransaction association rule (IAR) mining to detect anomalous behaviour in smart home occupants. An efficient mining algorithm that avoids the candidate generation bottleneck limiting the application of current IAR mining algorithms on smart home data sets is detailed. An original visual interface for the exploration of new and changing behaviours distilled from discovered patterns using a new process for finding emergent rules is presented. Finally, we discuss our observations on the emergent behaviours detected in the homes of two real world subjects.