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Full-Text Articles in Engineering
Meta-Icvi: Ensemble Validity Metrics For Concise Labeling Of Correct, Under- Or Over-Partitioning In Streaming Clustering, Niklas M. Melton, Sasha A. Petrenko, Donald C. Wunsch
Meta-Icvi: Ensemble Validity Metrics For Concise Labeling Of Correct, Under- Or Over-Partitioning In Streaming Clustering, Niklas M. Melton, Sasha A. Petrenko, Donald C. Wunsch
Electrical and Computer Engineering Faculty Research & Creative Works
Understanding the performance and validity of clustering algorithms is both challenging and crucial, particularly when clustering must be done online. Until recently, most validation methods have relied on batch calculation and have required considerable human expertise in their interpretation. Improving real-time performance and interpretability of cluster validation, therefore, continues to be an important theme in unsupervised learning. Building upon previous work on incremental cluster validity indices (iCVIs), this paper introduces the Meta- iCVI as a tool for explainable and concise labeling of partition quality in online clustering. Leveraging a time-series classifier and data-fusion techniques, the Meta- iCVI combines the outputs …
An Explainable And Statistically Validated Ensemble Clustering Model Applied To The Identification Of Traumatic Brain Injury Subgroups, Dacosta Yeboah, Louis Steinmeister, Daniel B. Hier, Bassam Hadi, Donald C. Wunsch, Gayla R. Olbricht, Tayo Obafemi-Ajayi
An Explainable And Statistically Validated Ensemble Clustering Model Applied To The Identification Of Traumatic Brain Injury Subgroups, Dacosta Yeboah, Louis Steinmeister, Daniel B. Hier, Bassam Hadi, Donald C. Wunsch, Gayla R. Olbricht, Tayo Obafemi-Ajayi
Electrical and Computer Engineering Faculty Research & Creative Works
We present a framework for an explainable and statistically validated ensemble clustering model applied to Traumatic Brain Injury (TBI). The objective of our analysis is to identify patient injury severity subgroups and key phenotypes that delineate these subgroups using varied clinical and computed tomography data. Explainable and statistically-validated models are essential because a data-driven identification of subgroups is an inherently multidisciplinary undertaking. In our case, this procedure yielded six distinct patient subgroups with respect to mechanism of injury, severity of presentation, anatomy, psychometric, and functional outcome. This framework for ensemble cluster analysis fully integrates statistical methods at several stages of …
Hidden Markov Model With Information Criteria Clustering And Extreme Learning Machine Regression For Wind Forecasting, Dao Lam, Shuhui Li, Donald C. Wunsch
Hidden Markov Model With Information Criteria Clustering And Extreme Learning Machine Regression For Wind Forecasting, Dao Lam, Shuhui Li, Donald C. Wunsch
Electrical and Computer Engineering Faculty Research & Creative Works
This paper proposes a procedural pipeline for wind forecasting based on clustering and regression. First, the data are clustered into groups sharing similar dynamic properties. Then, data in the same cluster are used to train the neural network that predicts wind speed. For clustering, a hidden Markov model (HMM) and the modified Bayesian information criteria (BIC) are incorporated in a new method of clustering time series data. to forecast wind, a new method for wind time series data forecasting is developed based on the extreme learning machine (ELM). the clustering results improve the accuracy of the proposed method of wind …
Development And Implementation Of Optimized Energy-Delay Sub-Network Routing Protocol For Wireless Sensor Networks, Maciej Jan Zawodniok, Jagannathan Sarangapani, Steve Eugene Watkins, James W. Fonda
Development And Implementation Of Optimized Energy-Delay Sub-Network Routing Protocol For Wireless Sensor Networks, Maciej Jan Zawodniok, Jagannathan Sarangapani, Steve Eugene Watkins, James W. Fonda
Electrical and Computer Engineering Faculty Research & Creative Works
The development and implementation of the optimized energy-delay sub-network routing (OEDSR) protocol for wireless sensor networks (WSN) is presented. This ondemand routing protocol minimizes a novel link cost factor which is defined using available energy, end-to-end (E2E) delay and distance from a node to the base station (BS), along with clustering, to effectively route information to the BS. Initially, the nodes are either in idle or sleep mode, but once an event is detected, the nodes near the event become active and start forming sub-networks. Formation of the inactive network into a sub-network saves energy because only a portion of …