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- Electrical and Computer Engineering Faculty Research & Creative Works (13)
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Articles 1 - 16 of 16
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 …
Multiple Imputation For Robust Cluster Analysis To Address Missingness In Medical Data, Arnold Harder, Gayla R. Olbricht, Godwin Ekuma, Daniel B. Hier, Tayo Obafemi-Ajayi
Multiple Imputation For Robust Cluster Analysis To Address Missingness In Medical Data, Arnold Harder, Gayla R. Olbricht, Godwin Ekuma, Daniel B. Hier, Tayo Obafemi-Ajayi
Mathematics and Statistics Faculty Research & Creative Works
Cluster Analysis Has Been Applied To A Wide Range Of Problems As An Exploratory Tool To Enhance Knowledge Discovery. Clustering Aids Disease Subtyping, I.e. Identifying Homogeneous Patient Subgroups, In Medical Data. Missing Data Is A Common Problem In Medical Research And Could Bias Clustering Results If Not Properly Handled. Yet, Multiple Imputation Has Been Under-Utilized To Address Missingness, When Clustering Medical Data. Its Limited Integration In Clustering Of Medical Data, Despite The Known Advantages And Benefits Of Multiple Imputation, Could Be Attributed To Many Factors. This Includes Methodological Complexity, Difficulties In Pooling Results To Obtain A Consensus Clustering, Uncertainty Regarding …
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 …
Incremental Cluster Validity Indices For Online Learning Of Hard Partitions: Extensions And Comparative Study, Leonardo Enzo Brito Da Silva, Niklas Max Melton, Donald C. Wunsch
Incremental Cluster Validity Indices For Online Learning Of Hard Partitions: Extensions And Comparative Study, Leonardo Enzo Brito Da Silva, Niklas Max Melton, Donald C. Wunsch
Electrical and Computer Engineering Faculty Research & Creative Works
Validation is one of the most important aspects of clustering, particularly when the user is designing a trustworthy or explainable system. However, most clustering validation approaches require batch calculation. This is an important gap because of the value of clustering in real-time data streaming and other online learning applications. Therefore, interest has grown in providing online alternatives for validation. This paper extends the incremental cluster validity index (iCVI) family by presenting incremental versions of Calinski-Harabasz (iCH), Pakhira-Bandyopadhyay-Maulik (iPBM), WB index (iWB), Silhouette (iSIL), Negentropy Increment (iNI), Representative Cross Information Potential (irCIP), Representative Cross Entropy (irH), and Conn_Index (iConn_Index). This paper …
Genotype Combinations Linked To Phenotype Subgroups In Autism Spectrum Disorders, Junya Zhao, Thy Nguyen, Jonathan Kopel, Perry B. Koob, Donald A. Adieroh, Tayo Obafemi-Ajayi
Genotype Combinations Linked To Phenotype Subgroups In Autism Spectrum Disorders, Junya Zhao, Thy Nguyen, Jonathan Kopel, Perry B. Koob, Donald A. Adieroh, Tayo Obafemi-Ajayi
Electrical and Computer Engineering Faculty Research & Creative Works
This paper investigates a computational model that allows for systematic comparison of phenotype data with genotype (Single Nucleotide Polymorphisms (SNPs)) data based on machine learning techniques to identify discriminant genotype markers associated with the phenotypic subgroups. The proposed discriminant SNP identifier model is empirically evaluated using Autism Spectrum Disorder (ASD) simplex sample. Six phenotype markers were selected to cluster the sample in a hexagonal lattice format yielding five multidimensional subgroups based on extremities of the phenotype markers. The SNP selection model includes random subspace selection of SNPs in conjunction with feature selection algorithms to determine which set of SNPs were …
Applications Of Node-Based Resilience Graph Theoretic Framework To Clustering Autism Spectrum Disorders Phenotypes, John Matta, Junya Zhao, Gunes Ercal, Tayo Obafemi-Ajayi
Applications Of Node-Based Resilience Graph Theoretic Framework To Clustering Autism Spectrum Disorders Phenotypes, John Matta, Junya Zhao, Gunes Ercal, Tayo Obafemi-Ajayi
Electrical and Computer Engineering Faculty Research & Creative Works
With the growing ubiquity of data in network form, clustering in the context of a network, represented as a graph, has become increasingly important. Clustering is a very useful data exploratory machine learning tool that allows us to make better sense of heterogeneous data by grouping data with similar attributes based on some criteria. This paper investigates the application of a novel graph theoretic clustering method, Node-Based Resilience clustering (NBR-Clust), to address the heterogeneity of Autism Spectrum Disorder (ASD) and identify meaningful subgroups. The hypothesis is that analysis of these subgroups would reveal relevant biomarkers that would provide a better …
Node-Based Resilience Measure Clustering With Applications To Noisy And Overlapping Communities In Complex Networks, John Matta, Tayo Obafemi-Ajayi, Jeffrey Borwey, Koushik Sinha, Donald C. Wunsch, Gunes Ercal
Node-Based Resilience Measure Clustering With Applications To Noisy And Overlapping Communities In Complex Networks, John Matta, Tayo Obafemi-Ajayi, Jeffrey Borwey, Koushik Sinha, Donald C. Wunsch, Gunes Ercal
Electrical and Computer Engineering Faculty Research & Creative Works
This paper examines a schema for graph-theoretic clustering using node-based resilience measures. Node-based resilience measures optimize an objective based on a critical set of nodes whose removal causes some severity of disconnection in the network. Beyond presenting a general framework for the usage of node-based resilience measures for variations of clustering problems, we experimentally validate the usefulness of such methods in accomplishing the following: (i) clustering a graph in one step without knowing the number of clusters a priori; (ii) removing noise from noisy data; and (iii) detecting overlapping communities. We demonstrate that this clustering schema can be applied successfully …
Analysis Of Grapevine Gene Expression Data Using Node-Based Resilience Clustering, Jeffrey Dale, John Matta, Susanne Howard, Gunes Ercal, Wenping Qiu, Tayo Obafemi-Ajayi
Analysis Of Grapevine Gene Expression Data Using Node-Based Resilience Clustering, Jeffrey Dale, John Matta, Susanne Howard, Gunes Ercal, Wenping Qiu, Tayo Obafemi-Ajayi
Electrical and Computer Engineering Faculty Research & Creative Works
Powdery mildew is the most economically important disease of cultivated grapevines worldwide. In the agricultural community, there is a great need for better understanding of the complex genetic basis of powdery mildew (PM) resistance by delineating possible gene biomarkers associated with the plants' defense mechanisms. Machine learning techniques can be applied to analysis of gene expression data to aid knowledge discovery of disease fighting genes. In this work, we apply a data-driven computational model, utilizing a graph-based clustering algorithm - Node-Based Resilience Clustering (NBRClust), to analyze grapevine gene expression data to identify possible gene biomarkers associated with powdery mildew disease …
Shape Analysis Of Traffic Flow Curves Using A Hybrid Computational Analysis, Wasim Irshad Kayani, Shikhar P. Acharya, Ivan G. Guardiola, Donald C. Wunsch, B. Schumacher, Isaac Wagner-Muns
Shape Analysis Of Traffic Flow Curves Using A Hybrid Computational Analysis, Wasim Irshad Kayani, Shikhar P. Acharya, Ivan G. Guardiola, Donald C. Wunsch, B. Schumacher, Isaac Wagner-Muns
Engineering Management and Systems Engineering Faculty Research & Creative Works
This paper highlights and validates the use of shape analysis using Mathematical Morphology tools as a means to develop meaningful clustering of historical data. Furthermore, through clustering more appropriate grouping can be accomplished that can result in the better parameterization or estimation of models. This results in more effective prediction model development. Hence, in an effort to highlight this within the research herein, a Back-Propagation Neural Network is used to validate the classification achieved through the employment of MM tools. Specifically, the Granulometric Size Distribution (GSD) is used to achieve clustering of daily traffic flow patterns based solely on their …
Clustering Data Of Mixed Categorical And Numerical Type With Unsupervised Feature Learning, Dao Lam, Mingzhen Wei, Donald C. Wunsch
Clustering Data Of Mixed Categorical And Numerical Type With Unsupervised Feature Learning, Dao Lam, Mingzhen Wei, Donald C. Wunsch
Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works
Mixed-type categorical and numerical data are a challenge in many applications. This general area of mixed-type data is among the frontier areas, where computational intelligence approaches are often brittle compared with the capabilities of living creatures. In this paper, unsupervised feature learning (UFL) is applied to the mixed-type data to achieve a sparse representation, which makes it easier for clustering algorithms to separate the data. Unlike other UFL methods that work with homogeneous data, such as image and video data, the presented UFL works with the mixed-type data using fuzzy adaptive resonance theory (ART). UFL with fuzzy ART (UFLA) obtains …
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 …
Computational Intelligence In Wireless Sensor Networks: A Survey, Raghavendra V. Kulkarni, Anna Förster, Ganesh K. Venayagamoorthy
Computational Intelligence In Wireless Sensor Networks: A Survey, Raghavendra V. Kulkarni, Anna Förster, Ganesh K. Venayagamoorthy
Electrical and Computer Engineering Faculty Research & Creative Works
Wireless sensor networks (WSNs) are networks of distributed autonomous devices that can sense or monitor physical or environmental conditions cooperatively. WSNs face many challenges, mainly caused by communication failures, storage and computational constraints and limited power supply. Paradigms of computational intelligence (CI) have been successfully used in recent years to address various challenges such as data aggregation and fusion, energy aware routing, task scheduling, security, optimal deployment and localization. CI provides adaptive mechanisms that exhibit intelligent behavior in complex and dynamic environments like WSNs. CI brings about flexibility, autonomous behavior, and robustness against topology changes, communication failures and scenario changes. …
Particle Swarm Optimization In Wireless-Sensor Networks: A Brief Survey, Raghavendra V. Kulkarni, Ganesh K. Venayagamoorthy
Particle Swarm Optimization In Wireless-Sensor Networks: A Brief Survey, Raghavendra V. Kulkarni, Ganesh K. Venayagamoorthy
Electrical and Computer Engineering Faculty Research & Creative Works
Wireless-sensor networks (WSNs) are networks of autonomous nodes used for monitoring an environment. Developers of WSNs face challenges that arise from communication link failures, memory and computational constraints, and limited energy. Many issues in WSNs are formulated as multidimensional optimization problems and approached through bioinspired techniques. Particle swarm optimization (PSO) is a simple, effective, and computationally efficient optimization algorithm. It has been applied to address WSN issues such as optimal deployment, node localization, clustering, and data aggregation. This paper outlines issues in WSNs, introduces PSO, and discusses its suitability for WSN applications. It also presents a brief survey of how …
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 …
Survey Of Clustering Algorithms, Rui Xu, Donald C. Wunsch
Survey Of Clustering Algorithms, Rui Xu, Donald C. Wunsch
Electrical and Computer Engineering Faculty Research & Creative Works
Data analysis plays an indispensable role for understanding various phenomena. Cluster analysis, primitive exploration with little or no prior knowledge, consists of research developed across a wide variety of communities. The diversity, on one hand, equips us with many tools. On the other hand, the profusion of options causes confusion. We survey clustering algorithms for data sets appearing in statistics, computer science, and machine learning, and illustrate their applications in some benchmark data sets, the traveling salesman problem, and bioinformatics, a new field attracting intensive efforts. Several tightly related topics, proximity measure, and cluster validation, are also discussed.
An Industrial Application To Neural Networks To Reusable Design, Donald C. Wunsch, R. Escobedo, T. P. Caudell, S. D. G. Smith, G. C. Johnson
An Industrial Application To Neural Networks To Reusable Design, Donald C. Wunsch, R. Escobedo, T. P. Caudell, S. D. G. Smith, G. C. Johnson
Electrical and Computer Engineering Faculty Research & Creative Works
Summary form only given, as follows. The feasibility of training an adaptive resonance theory (ART-1) network to first cluster aircraft parts into families, and then to recall the most similar family when presented a new part has been demonstrated, ART-1 networks were used to adaptively group similar input vectors. The inputs to the network were generated directly from computer-aided designs of the parts and consist of binary vectors which represent bit maps of the features of the parts. This application, referred to as group technology, is of large practical value to industry, making it possible to avoid duplication of design …