scholarly journals Evaluating Clustering Algorithms for Identifying Design Subproblems

2018 ◽  
Vol 140 (8) ◽  
Author(s):  
Jeffrey W. Herrmann ◽  
Michael Morency ◽  
Azrah Anparasan ◽  
Erica L. Gralla
Keyword(s):  
Association Rule ◽  
Euclidean Distance ◽  
Clustering Algorithms ◽  
Design Teams ◽  
Clustering Methods ◽  
Design Problems ◽  
Design Variables ◽  
Complex Design ◽  
Synthetic Datasets ◽  
Better Than

Understanding how humans decompose design problems will yield insights that can be applied to develop better support for human designers. However, there are few established methods for identifying the decompositions that human designers use. This paper discusses a method for identifying subproblems by analyzing when design variables were discussed concurrently by human designers. Four clustering techniques for grouping design variables were tested on a range of synthetic datasets designed to resemble data collected from design teams, and the accuracy of the clusters created by each algorithm was evaluated. A spectral clustering method was accurate for most problems and generally performed better than hierarchical (with Euclidean distance metric), Markov, or association rule clustering methods. The method's success should enable researchers to gain new insights into how human designers decompose complex design problems.

Identification of Subproblems in Complex Design Problems: A Study of Facility Design

2016 ◽  
Author(s):  
Azrah Azhar ◽  
Erica L. Gralla ◽  
Connor Tobias ◽  
Jeffrey W. Herrmann
Keyword(s):  
Standard Method ◽  
Association Rule ◽  
Rule Learning ◽  
Problem Decomposition ◽  
Design Teams ◽  
Design Problems ◽  
Manufacturing Facility ◽  
Qualitative And Quantitative ◽  
Complex Design ◽  
Design Activities

Many design problems are too difficult to solve all at once; therefore, design teams often decompose these problems into more manageable subproblems. While there has been much interest in engineering design teams, no standard method has been developed to understand how teams solve design problems. This paper describes a method for analyzing a team’s design activities and identifying the subproblems that they considered. This method uses both qualitative and quantitative techniques; in particular, it uses association rule learning to group variables into subproblems. We used the method on data from ten teams who redesigned a manufacturing facility. This approach provides researchers with a clear structure for using observational data to identify the problem decomposition patterns of human designers.

Multi-Objective Optimization of an Autonomous Underwater Vehicle

2009 ◽  
Vol 43 (2) ◽  
pp. 48-60 ◽  
Author(s):  
M. Martz ◽  
W. L. Neu
Keyword(s):  
Design Space ◽  
Autonomous Underwater Vehicle ◽  
Underwater Vehicle ◽  
Design Problems ◽  
Single Measure ◽  
Design Variables ◽  
Complex Design ◽  
Final Design ◽  
Undersea Vehicle ◽  
Hierarchy Process

AbstractThe design of complex systems involves a number of choices, the implications of which are interrelated. If these choices are made sequentially, each choice may limit the options available in subsequent choices. Early choices may unknowingly limit the effectiveness of a final design in this way. Only a formal process that considers all possible choices (and combinations of choices) can insure that the best option has been selected. Complex design problems may easily present a number of choices to evaluate that is prohibitive. Modern optimization algorithms attempt to navigate a multidimensional design space in search of an optimal combination of design variables. A design optimization process for an autonomous underwater vehicle is developed using a multiple objective genetic optimization algorithm that searches the design space, evaluating designs based on three measures of performance: cost, effectiveness, and risk. A synthesis model evaluates the characteristics of a design having any chosen combination of design variable values. The effectiveness determined by the synthesis model is based on nine attributes identified in the U.S. Navy’s Unmanned Undersea Vehicle Master Plan and four performance-based attributes calculated by the synthesis model. The analytical hierarchy process is used to synthesize these attributes into a single measure of effectiveness. The genetic algorithm generates a set of Pareto optimal, feasible designs from which a decision maker(s) can choose designs for further analysis.

Understanding the Effects of Model Uncertainty in Robust Design With Computer Experiments

2006 ◽  
Author(s):  
Jun Liu ◽  
Daniel W. Apley ◽  
Wei Chen
Keyword(s):  
Robust Design ◽  
Prediction Interval ◽  
Computer Experiments ◽  
Design Problems ◽  
Deterministic Optimization ◽  
Automotive Engine ◽  
Design Objective ◽  
Design Variables ◽  
Complex Design ◽  
The Impact

The use of metamodels in simulation-based robust design introduces a new source of uncertainty that we term model interpolation uncertainty. Most existing approaches for treating interpolation uncertainty in computer experiments have been developed for deterministic optimization and are not applicable to design under uncertainty. With the randomness present in noise and/or design variables that propagates through the metamodel, the effects of model interpolation uncertainty are not nearly as transparent as in deterministic optimization. In this work, a methodology is developed within a Bayesian framework for quantifying the impact of interpolation uncertainty on robust design objective. By viewing the true response surface as a realization of a random process, as is common in kriging and other Bayesian analyses of computer experiments, we derive a closed-form analytical expression for a Bayesian prediction interval on the robust design objective function. This provides a simple, intuitively appealing tool for distinguishing the best design alternative and conducting more efficient computer experiments. Even though our proposed methodology is illustrated with a simple container design and an automotive engine piston design example here, the developed analytical approach is the most useful when applied to high-dimensional complex design problems in a similar manner.

scholarly journals Exploring centroids initialization within Deep Convolutional Embedded Clustering

2019 ◽  
Author(s):  
Leonardo Nogueira ◽  
Adriane Serapião
Keyword(s):  
Euclidean Distance ◽  
Clustering Algorithm ◽  
Neural Model ◽  
Feature Representation ◽  
Clustering Methods ◽  
Initial Weight ◽  
Deep Feature ◽  
Model Training ◽  
Harmonic Means ◽  
Better Than

Deep clustering uses a deep neural network to learn deep feature representation for performing clustering tasks. In this paper, we explored the Deep Convolutional Embedded Clustering (DCEC) method, which employs a stan- dart clustering method to get initial weight for the neural model training incor- porated to other clustering methods. The original DCEC uses K-Means with Euclidean distance for the clusters center initialization step. We have applied K-Means with Mahalanobis distance instead of Euclidean distance. In order to improve the DCEC performance, we have included the standart K-Harmonic Means clustering algorithm as well, which tries overcome the dependency of the K-Means performance on the clusters center initialization. The Kernel ba- sed K-Harmonic Means was also introduced in this study to reduce the effect of outliers and noise. We evaluated the performance of these clustering appro- aches within DCEC over benchmark image datasets and the results were better than the baseline.

scholarly journals Bayesian-OverDBC: A Bayesian Density-Based Approach for Modeling Overlapping Clusters

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Mansooreh Mirzaie ◽  
Ahmad Barani ◽  
Naser Nematbakkhsh ◽  
Majid Mohammad-Beigi
Keyword(s):  
Probability Model ◽  
Clustering Algorithms ◽  
Large Data ◽  
Clustering Methods ◽  
Overlapping Clusters ◽  
Density Based Clustering ◽  
Real World Applications ◽  
Gene Regulatory ◽  
Large Data Analysis ◽  
Better Than

Although most research in density-based clustering algorithms focused on finding distinct clusters, many real-world applications (such as gene functions in a gene regulatory network) have inherently overlapping clusters. Even with overlapping features, density-based clustering methods do not define a probabilistic model of data. Therefore, it is hard to determine how “good” clustering, predicting, and clustering new data into existing clusters are. Therefore, a probability model for overlap density-based clustering is a critical need for large data analysis. In this paper, a new Bayesian density-based method (Bayesian-OverDBC) for modeling the overlapping clusters is presented. Bayesian-OverDBC can predict the formation of a new cluster. It can also predict the overlapping of cluster with existing clusters. Bayesian-OverDBC has been compared with other algorithms (nonoverlapping and overlapping models). The results show that Bayesian-OverDBC can be significantly better than other methods in analyzing microarray data.

Exploring performance of clustering methods on document sentiment analysis

2016 ◽  
Vol 43 (1) ◽  
pp. 54-74 ◽  
Author(s):  
Baojun Ma ◽  
Hua Yuan ◽  
Ye Wu
Keyword(s):  
Sentiment Analysis ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Experimental Studies ◽  
Experimental Results ◽  
Clustering Methods ◽  
Term Weighting ◽  
Weighting Method ◽  
Clustering Techniques ◽  
Better Than

Clustering is a powerful unsupervised tool for sentiment analysis from text. However, the clustering results may be affected by any step of the clustering process, such as data pre-processing strategy, term weighting method in Vector Space Model and clustering algorithm. This paper presents the results of an experimental study of some common clustering techniques with respect to the task of sentiment analysis. Different from previous studies, in particular, we investigate the combination effects of these factors with a series of comprehensive experimental studies. The experimental results indicate that, first, the K-means-type clustering algorithms show clear advantages on balanced review datasets, while performing rather poorly on unbalanced datasets by considering clustering accuracy. Second, the comparatively newly designed weighting models are better than the traditional weighting models for sentiment clustering on both balanced and unbalanced datasets. Furthermore, adjective and adverb words extraction strategy can offer obvious improvements on clustering performance, while strategies of adopting stemming and stopword removal will bring negative influences on sentiment clustering. The experimental results would be valuable for both the study and usage of clustering methods in online review sentiment analysis.

A SEQUENCE-ELEMENT-BASED HIERARCHICAL CLUSTERING ALGORITHM FOR CATEGORICAL SEQUENCE DATA

2005 ◽  
Vol 04 (01) ◽  
pp. 81-96 ◽  
Author(s):  
SEUNG-JOON OH ◽  
JAE-YEARN KIM
Keyword(s):  
Hierarchical Clustering ◽  
Clustering Algorithm ◽  
Sequence Data ◽  
Clustering Algorithms ◽  
Scientific Data ◽  
Sequence Element ◽  
Hierarchical Clustering Algorithm ◽  
Synthetic Datasets ◽  
Better Than

Recently, there has been enormous growth in the amount of commercial and scientific data, such as protein sequences, retail transactions, and web-logs. Such datasets consist of sequence data that have an inherent sequential nature. However, few existing clustering algorithms consider sequentiality. In this paper, we study how to cluster these sequence datasets. We propose a new similarity measure to compute the similarity between two sequences. In the proposed measure, subsets of a sequence are considered, and the more identical subsets there are, the more similar the two sequences. In addition, we propose a hierarchical clustering algorithm and an efficient method for measuring similarity. Using a splice dataset and synthetic datasets, we show that the quality of clusters generated by our proposed approach is better than that of clusters produced by traditional clustering algorithms.

A preliminary study on the integration of engineering and aesthetics measures via the design of vehicle silhouettes

2014 ◽  
Vol 229 (12) ◽  
pp. 2221-2230 ◽  
Author(s):  
Kuei-Yuan Chan ◽  
Shen-Cheng Chang
Keyword(s):  
Optimal Design ◽  
Level Structure ◽  
Point Of View ◽  
Design Problems ◽  
Physical Constraints ◽  
Engineering Discipline ◽  
Design Variables ◽  
Complex Design ◽  
Target Cascading ◽  
The Aesthetic

The success of a consumer product is the result of not only engineering specifications but also emotional effects. Therefore, product design must be multidisciplinary as well as transdisciplinary across both natural and social science. In this work, we investigate the optimal design of vehicle silhouettes considering various aesthetic and engineering measures. The entire design problem is modeled as a bi-level structure with the top level being the aesthetic subproblem and the lower level consists of subproblems in the engineering discipline. This multi-level system provides a feasible approach in solving complex design problems; it also resembles the interactions of different departments in the auto industry. The aesthetic subproblem uses 11 proportionality measures and curvature to quantify a vehicle silhouette. The engineering discipline includes safety, handling, and aerodynamics of a vehicle with physical constraints on vehicle geometry. The design variables are the locations of 15 nodal points in describing the silhouette of a vehicle. The linking variables between subsystems are body and chassis dimensions that must be consistent for a design to be feasible. The optimal design of this hierarchical problem is obtained using the analytical target cascading from the literature. Results show that the original prohibitively expensive all-in-one problem becomes solvable if systems of smaller subproblems are created. Adding emotional measures in engineering design is invaluable and will reveal the true merits of a product from consumers’ point of view. Although such metrics are generally opaque, this research demonstrates the impacts of these measures once they become available.

scholarly journals DECOMPOSITION AND RECOMPOSITION STRATEGIES OF PROFESSIONAL ENGINEERING DESIGN TEAMS

2021 ◽  
Vol 1 ◽  
pp. 871-880
Author(s):  
Julie Milovanovic ◽  
John Gero ◽  
Kurt Becker
Keyword(s):  
Design Process ◽  
Design Theory ◽  
Design Teams ◽  
Theoretical Understanding ◽  
Design Problems ◽  
Engineering Teams ◽  
Complex Design ◽  
Decomposition Strategies ◽  
Design Proposal ◽  
Empirical Foundation

AbstractDesigners faced with complex design problems use decomposition strategies to tackle manageable sub-problems. Recomposition strategies aims at synthesizing sub-solutions into a unique design proposal. Design theory describes the design process as a combination of decomposition and recomposition strategies. In this paper, we explore dynamic patterns of decomposition and recomposition strategies of design teams. Data were collected from 9 teams of professional engineers. Using protocol analysis, we examined the dominance of decomposition and recomposition strategies over time and the correlations between each strategy and design processes such as analysis, synthesis, evaluation. We expected decomposition strategies to peak early in the design process and decay overtime. Instead, teams maintain decomposition and recomposition strategies consistently during the design process. We observed fast iteration of both strategies over a one hour-long design session. The research presented provides an empirical foundation to model the behaviour of professional engineering teams, and first insights to refine theoretical understanding of the use decomposition and recomposition strategies in design practice.

Kernelised Rough Sets Based Clustering Algorithms Fused With Firefly Algorithm for Image Segmentation

2019 ◽  
Vol 8 (4) ◽  
pp. 25-38
Author(s):  
Srujan Sai Chinta
Keyword(s):  
Image Segmentation ◽  
Euclidean Distance ◽  
Firefly Algorithm ◽  
Clustering Algorithms ◽  
Kernel Functions ◽  
Gaussian Kernel ◽  
Clustering Methods ◽  
Fuzzy C Means ◽  
Intuitionistic Fuzzy ◽  
Radial Basis

Data clustering methods have been used extensively for image segmentation in the past decade. In one of the author's previous works, this paper has established that combining the traditional clustering algorithms with a meta-heuristic like the Firefly Algorithm improves the stability of the output as well as the speed of convergence. It is well known now that the Euclidean distance as a measure of similarity has certain drawbacks and so in this paper we replace it with kernel functions for the study. In fact, the authors combined Rough Fuzzy C-Means (RFCM) and Rough Intuitionistic Fuzzy C-Means (RIFCM) with Firefly algorithm and replaced Euclidean distance with either Gaussian or Hyper-tangent or Radial basis Kernels. This paper terms these algorithms as Gaussian Kernel based rough Fuzzy C-Means with Firefly Algorithm (GKRFCMFA), Hyper-tangent Kernel based rough Fuzzy C-Means with Firefly Algorithm (HKRFCMFA), Gaussian Kernel based rough Intuitionistic Fuzzy C-Means with Firefly Algorithm (GKRIFCMFA) and Hyper-tangent Kernel based rough Intuitionistic Fuzzy C-Means with Firefly Algorithm (HKRIFCMFA), Radial Basis Kernel based rough Fuzzy C-Means with Firefly Algorithm (RBKRFCMFA) and Radial Basis Kernel based rough Intuitionistic Fuzzy C-Means with Firefly Algorithm (RBKRIFCMFA). In order to establish that these algorithms perform better than the corresponding Euclidean distance-based algorithms, this paper uses measures such as DB and Dunn indices. The input data comprises of three different types of images. Also, this experimentation varies over different number of clusters.

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