Sebastian Lühr and Mihai Lazarescu (2008) Connectivity Based Stream Clustering Using Localised Density Exemplars. In Proc. Pacific-Asia Conference on Knowledge Discovery and Data Mining, volume 5012 of Lecture Notes in Artificial Intelligence, pages 983–984. Springer-Verlag.

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Abstract: Advances in data acquisition have allowed large data collections of millions of time varying records in the form of data streams. The challenge is to effectively process the stream data with limited resources while maintaining sufficient historical information to define the changes and patterns over time. It is highly desirable to handle recurrent changes without requiring the re-learning of previously observed patterns. This paper describes an evidence-based approach that uses representative points to incrementally process stream data by using a graph based method to cluster points based on connectivity and density. Critical cluster features are archived in repositories to allow the algorithm to cope with recurrent information and to provide a rich history of relevant cluster changes if a detailed analysis of past data is required. We have applied our algorithm to both synthetic and real world data sets and present results that clearly show that our approach performs better than the current established stream mining techniques: DenStream, HPStream and CluStream.

Sebastian Lühr and Mihai Lazarescu (2007) A Visual Data Analysis Tool for Sport Player Performance Benchmarking, Comparison and Change Detection. In IEEE International Conference on Tools with Artificial Intelligence, pages 289-297. Patras, Greece.

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Abstract: Sports coaches today have access to a wide variety of information sources that describe the performance of their players. However, despite this great wealth of information, most techniques used to analyse performance require a significant amount of manual processing and continue to rely heavily on input from human experts. In this paper we propose an automated approach to analyse player performance. Specifically, we propose a team benchmarking and concept drift tracking based system that (1) generates adaptive baseline player performance norms, (2) interprets player performance over different time lines and (3) identifies and describes key turning points in player performance. The concept drift technique that we describe uses a combination of overlapping data windows and decision tree based learning to process the data.

Sebastian Lühr, Svetha Venkatesh and Geoff West (2005) Emergent Intertransaction Association Rules for Abnormality Detection in Intelligent Environments. In International Conference on Intelligent Sensors, Sensor Networks and Information Processing, pages 343-347. Melbourne, Australia.

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Abstract: This work aims to identify anomalous behaviour of people in smart environments. We propose the use of emergent transaction mining and the use of the extended frequent pattern tree as a basis. Our experiments on two data sets demonstrate that emergent intertransaction associations are able to detect abnormality present in real world data and that both short and long term behavioural changes can be discovered. The use of intertransaction associations is shown to be advantageous in the detection of temporal association anomalies otherwise not readily detectable by traditional “market basket” intratransaction mining.

Sebastian Lühr, Svetha Venkatesh, Geoff W. West and Hung H. Bui. (2004) Explicit state duration HMM for abnormality detection in sequences of human activity. In Proc. 8th Pacific Rim Intl Conf. Artificial Intelligence, volume 3157 of Lecture Notes in Artificial Intelligence, pages 983–984. Springer-Verlag, August 2004.

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Introduction: Much of the current work in human behaviour modelling concentrates on activity recognition, recognising actions and events through pose, movement, and gesture analysis. Our work focuses on learning and detecting abnormality in higher level behavioural patterns. The hidden Markov model (HMM) is one approach for learning such behaviours given a vision tracker recording observations about a person’s activity. We show how the implicit state duration in the HMM can create a situation in which highly abnormal deviation as either less than or more than the usually observed activity duration can fail to be detected and how the explicit state duration HMM (ESD-HMM) helps alleviate the problem. Duration of human activity is an important consideration if we are to accurately model a person s behavioural patterns.

Sebastian Lühr, Hung H. Bui, Svetha Venkatesh and Geoff West (2003) Recognition of human activity through hierarchical stochastic learning. In IEEE International Conference on Pervasive Computing and Communications, pages 416–423. Texas, USA.

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Abstract: Seeking to extend the functional capability of the elderly, we explore the use of probabilistic methods to learn and recognise human activity in order to provide monitoring support. We propose a novel approach to learning the hierarchical structure of sequences of human actions through the application of the hierarchical hidden Markov model (HHMM). Experimental results are presented for learning and recognising sequences of typical activities in a home.