S
Shehroz S. Khan
Researcher at University Health Network
Publications - 70
Citations - 2755
Shehroz S. Khan is an academic researcher from University Health Network. The author has contributed to research in topics: Computer science & Cluster analysis. The author has an hindex of 18, co-authored 58 publications receiving 2108 citations. Previous affiliations of Shehroz S. Khan include Rehabilitation Institute of Michigan & University of Toronto.
Papers
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Journal ArticleDOI
Cluster center initialization algorithm for K -means clustering
Shehroz S. Khan,Amir Ahmad +1 more
TL;DR: An algorithm to compute initial cluster centers for K-means clustering based on two observations that some of the patterns are very similar to each other and that is why they have same cluster membership irrespective to the choice of initial cluster center.
Book ChapterDOI
A survey of recent trends in one class classification
TL;DR: This paper forms a taxonomy with three main categories based on the way OCC has been envisaged, implemented and applied by various researchers in different application domains and presents a survey of current state-of-the-art OCC algorithms.
Journal ArticleDOI
One-class classification: taxonomy of study and review of techniques
TL;DR: One-class classification (OCC) algorithms aim to build classification models when the negative class is either absent, poorly sampled or not well defined as discussed by the authors. This unique situation constrains the learning of efficient classifiers by defining class boundary just with the knowledge of positive class.
Journal ArticleDOI
Review of fall detection techniques: a data availability perspective
Shehroz S. Khan,Jesse Hoey +1 more
TL;DR: A taxonomy for the study of fall detection from the perspective of availability of fall data is presented and the approach of treating a fall as an abnormal activity to be a plausible research direction is identified.
Journal ArticleDOI
Survey of State-of-the-Art Mixed Data Clustering Algorithms
Amir Ahmad,Shehroz S. Khan +1 more
TL;DR: A taxonomy for the study of mixed data clustering algorithms by identifying five major research themes is presented in this article. But it is difficult to directly apply mathematical operations, such as summation or averaging, to the feature values of these datasets.