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The aim of this paper is to examine the suitability of binary similarity and dissimilarity measures in identifying frequent and abnormal human behavioural patterns in a smart home. There has been an increasing interest in this subject to help the elderly and disabled people to live alone in their own homes with little help and support from their carer. Similarity and dissimilarity measures have been applied for a wide range of pattern recognition tasks such as character recognition, image retrieval, etc. In this work, the binary similarity and dissimilarity indices are used on data generated from occupancy sensors including door and motion sensors in a smart home. These sensors indicate the presence and absence of the occupant in a specific area in the home. Many measures are introduced in the literature, the focus in this paper is on the measures that give credits to both the positive matches and the mismatching between two sensor values. This paper first gives an overview of the similarity measures to find the most common patterns and then dissimilarity or distance measures are used to identify unexpected or abnormal data. Data from two different case studies are used to validate the accuracy of these measurements.