Data Mining by Means of Binary Representation: A Model for Similarity and Clustering
Information Systems Frontiers
An empirical evaluation of Chernoff faces, star glyphs, and spatial visualizations for binary data
APVis '03 Proceedings of the Asia-Pacific symposium on Information visualisation - Volume 24
Data Mining and Knowledge Discovery Handbook
Data Mining and Knowledge Discovery Handbook
On Binary Similarity Measures for Handwritten Character Recognition
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
APVis '06 Proceedings of the 2006 Asia-Pacific Symposium on Information Visualisation - Volume 60
A fuzzy logic system for home elderly people monitoring (EMUTEM)
FS'09 Proceedings of the 10th WSEAS international conference on Fuzzy systems
Modeling human activity from voxel person using fuzzy logic
IEEE Transactions on Fuzzy Systems
Outlier Detection in Smart Environment Structured Power Datasets
IE '10 Proceedings of the 2010 Sixth International Conference on Intelligent Environments
Automated Prompting in a Smart Home Environment
ICDMW '10 Proceedings of the 2010 IEEE International Conference on Data Mining Workshops
Abnormal behaviours identification for an elder's life activities using dissimilarity measurements
Proceedings of the 4th International Conference on PErvasive Technologies Related to Assistive Environments
Detection of Abnormal Living Patterns for Elderly Living Alone Using Support Vector Data Description
IEEE Transactions on Information Technology in Biomedicine
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In this paper, a user activities outlier detection system is introduced. The proposed system is implemented in a smart home environment equipped with appropriate sensory devices. An activity outlier detection system consist of a two-stage integration of Principal Component Analysis (PCA) and Fuzzy Rule-Based System (FRBS). In the first stage, the Hamming distance is used to measure the distances between the activities. PCA is then applied to the distance measures to find two indices of Hotelling's T2 and Squared Prediction Error (SPE). In the second stage of the process, the calculated indices are provided as inputs to FRBSs to model them heuristically. They are used to identify the outliers and classify them. Three case studies are reported to demonstrate the effectiveness of the proposed system. The proposed system successfully identifies the outliers and helps in distinguishing between the normal and abnormal behaviour patterns of the Activities of Daily Living (ADL).