JSE_V6_N2_RP4 L. Karthika A.C. Sumathi Journal on Software Engineering 2230 – 7168 6 2 31 35 Activity Recognition, Data mining, Sequence Mining, Clustering, Smart Homes Recent advancements in machine learning caused interest in the development of smart environments to emerge and assist with valuable functions such as activity monitoring and intervention. In order to monitor the activities of smart home residents, there is a need of designing technologies that recognize and track the activities that people perform at home. Machine learning techniques can perform this task, but the software algorithms rely upon large amounts of sample data that is correctly matched with the corresponding activity. The data stream captured by recording inhabitant-device interactions in an environment can be mined to discover significant patterns, which an intelligent agent could use to automate device interactions. For this, an automated approach is proposed for activity tracking that identifies frequent activities that naturally occur in an individual's routines. With this capability, the occurrences of regular activities are monitored and also can detect the changes in an individual's patterns. October - December 2011 Copyright © 2011 i-manager publications. All rights reserved. i-manager Publications http://www.imanagerpublications.com/Article.aspx?ArticleId=2895