Smart Home is a kind of Home Automation System that provides an intelligent and integrated environment which can recognize the user activity and automate itself accordingly. The automated home environment must have the capacity to monitor, detect and record the daily activity patterns of the user. Thus this intelligent home environment must able to assist and hence increase the comfortability of living for its user. The intelligent home environment can be get automated by modeling it with the daily activity patterns of the users. This modeling of the user activities can be done by implementing the machine learning algorithms. A large amount of data are collected from many sensors from the smart home in order to train the machine learning algorithm so that it can work accurately. But in-case of supervised machine learning the usage of large amount of data for its training results in computational in-efficiency. Therefore using the unsupervised machine learning algorithms are highly recommended. Clustering is a type of unsupervised learning which is used to group the similar user activity patterns into clusters. Since the users will perform the activity in a sequence of events data clustering is not suitable for modeling the activity behavior of the user. Therefore to cluster the activities a new pattern clustering algorithm called K-Pattern clustering has to be proposed. The proposed algorithm must even able to detect the discontinuous and interleaved activity patterns of the user. Thus it overcomes the draw backs of the existing data clustering algo-rithms. After clustering the activity patterns a neural network has to be build as a predictive model to predict the future behavior of the user and thus automating the home system accordingly.
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