Deriving Menu Structures through Modal Block Clustering: A Promising Alternative to Hierarchical Techniques

Modal block clustering (MBC) is proposed as an approach more suited to the derivation of menu structures than hierarchical clustering techniques. Problems with the application of hierarchical techniques and pairwise similarity ratings (PWSR) from which the clusters are derived are discussed. MBC defines clusters based on the pattern of common command attributes and provides an objective way to determine the composition and number of menu panels to include in a menu structure. The method also objectively defines command redundancy for the menu panels. The method of MBC was applied to the 97 commands that comprise the CMS operating system resulting in 17 menu categories. The menu categories were used to design a help menu system. The MBC procedure provides a viable methodology for complex systems, such as CMS, which derive increased functionality from numerous command options. System designers can fruitfully and efficiently apply this methodology both to current systems and to proposed systems for which there are no expert users.

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Telemanipulation of an Articulated Robotic Arm using a Commercial Virtual Reality Controller

Current Directions in Biomedical Engineering ◽

10.1515/cdbme-2020-3033 ◽

2020 ◽

Vol 6 (3) ◽

pp. 127-130

Author(s):

Max B. Schäfer ◽

Kent W. Stewart ◽

Nico Lösch ◽

Peter P. Pott

Keyword(s):

Virtual Reality ◽

Real Time ◽

Robotic Arm ◽

Input Device ◽

Robot Assisted ◽

Promising Alternative ◽

Input Devices ◽

Current Input ◽

Current Systems ◽

Robot Assisted Surgery

AbstractAccess to systems for robot-assisted surgery is limited due to high costs. To enable widespread use, numerous issues have to be addressed to improve and/or simplify their components. Current systems commonly use universal linkage-based input devices, and only a few applicationoriented and specialized designs are used. A versatile virtual reality controller is proposed as an alternative input device for the control of a seven degree of freedom articulated robotic arm. The real-time capabilities of the setup, replicating a system for robot-assisted teleoperated surgery, are investigated to assess suitability. Image-based assessment showed a considerable system latency of 81.7 ± 27.7 ms. However, due to its versatility, the virtual reality controller is a promising alternative to current input devices for research around medical telemanipulation systems.

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A comparative investigation of hierarchical clustering techniques and dissimilarity measures applied to the cell formation problem

Journal of Operations Management ◽

10.1016/0272-6963(95)00017-m ◽

1995 ◽

Vol 13 (2) ◽

pp. 117-138 ◽

Cited By ~ 42

Author(s):

Asoo J. Vakharia ◽

Urban Wemmerlöv

Keyword(s):

Hierarchical Clustering ◽

Cell Formation ◽

Comparative Investigation ◽

Cell Formation Problem ◽

Clustering Techniques ◽

Dissimilarity Measures ◽

Formation Problem

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A Novel Approach for Traffic Accidents Analysis Using Hierarchical Clustering Techniques

SSRN Electronic Journal ◽

10.2139/ssrn.3917726 ◽

2021 ◽

Author(s):

Kinnari Parikh ◽

Dr. Gayatrri Pandi

Keyword(s):

Hierarchical Clustering ◽

Traffic Accidents ◽

Clustering Techniques ◽

Novel Approach

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Chapter 7: Hierarchical Clustering Techniques

Data Clustering: Theory, Algorithms, and Applications, Second Edition ◽

10.1137/1.9781611976335.ch7 ◽

2020 ◽

pp. 103-138

Keyword(s):

Hierarchical Clustering ◽

Clustering Techniques

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Computational Discovery of Motifs Using Hierarchical Clustering Techniques

2008 Eighth IEEE International Conference on Data Mining ◽

10.1109/icdm.2008.21 ◽

2008 ◽

Cited By ~ 4

Author(s):

Dianhui Wang ◽

Nung Kion Lee

Keyword(s):

Hierarchical Clustering ◽

Clustering Techniques ◽

Computational Discovery

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Hierarchical clustering techniques and classification applied in Content Based Image Retrieval (CBIR)

2015 IEEE 10th Jubilee International Symposium on Applied Computational Intelligence and Informatics ◽

10.1109/saci.2015.7208188 ◽

2015 ◽

Cited By ~ 2

Author(s):

Radu Andrei Stefan ◽

Ildiko-Angelica Szoke ◽

Stefan Holban

Keyword(s):

Image Retrieval ◽

Hierarchical Clustering ◽

Content Based Image Retrieval ◽

Clustering Techniques

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Hierarchical clustering techniques for image database organization and summarization

10.1117/12.325853 ◽

1998 ◽

Cited By ~ 7

Author(s):

Asha Vellaikal ◽

C.-C. Jay Kuo

Keyword(s):

Hierarchical Clustering ◽

Image Database ◽

Clustering Techniques ◽

Database Organization

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CLUSTERING OF DECISION TABLES TOWARD ROUGH SET-BASED GROUP DECISION AID

International Journal of Uncertainty Fuzziness and Knowledge-Based Systems ◽

10.1142/s0218488511007325 ◽

2011 ◽

Vol 19 (supp01) ◽

pp. 17-32 ◽

Cited By ~ 2

Author(s):

MASAHIRO INUIGUCHI ◽

RYUTA ENOMOTO

Keyword(s):

Hierarchical Clustering ◽

Rough Set ◽

Real World ◽

Decision Aid ◽

Group Decision ◽

Decision Rules ◽

Agglomerative Hierarchical Clustering ◽

Clustering Techniques ◽

Fuzzy C Means ◽

Decision Tables

In order to analyze the distribution of individual opinions (decision rules) in a group, clustering of decision tables is proposed. An agglomerative hierarchical clustering (AHC) of decision tables has been examined. The result of AHC does not always optimize some criterion. We develop non-hierarchical clustering techniques for decision tables. In order to treat positive and negative evaluations to a common profile, we use a vector of rough membership values to represent individual opinion to a profile. Using rough membership values, we develop a K -means method as well as fuzzy c-means methods for clustering decision tables. We examined the proposed methods in clustering real world decision tables obtained by a questionnaire investigation.

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Individual Differences in Metacognitive Decision Making and Situation Assessment

Proceedings of the Human Factors and Ergonomics Society Annual Meeting ◽

10.1177/154193129503901305 ◽

1995 ◽

Vol 39 (13) ◽

pp. 878-881 ◽

Cited By ~ 1

Author(s):

Pat-Anthony Federico

Keyword(s):

Decision Making ◽

Multidimensional Scaling ◽

Two Dimensions ◽

Event Sequence ◽

Pairwise Similarity ◽

Situation Assessment ◽

Scaling Solution ◽

Similarity Ratings ◽

Tactical Decision ◽

Naval Officers

28 senior naval officers (experts) and 48 junior naval officers (novices) (1) categorized tactical situations, (2) performed pairwise similarity ratings of them, and (3) represented their metacognitive models of tactical decision making as graphic weighted networks. Multidimensional scaling was conducted employing subjects’ pairwise similarity ratings of tactical situations. Using classification measures and multidimensional weights as dependent variables and salient metacognitive link weights as independent variables, two one-way multivariate analyses of covariance between experts and novices and associated statistics were computed. Some of the results of canonical and regression analyses and product-moment correlations validated an important aspect of a metacognitive model of naturalistic schema-driven tactical decision making. They established significant associations of the two link weights connecting event sequence and similarity recognition to situation assessment with actual performances on the two experimental tasks requiring situation assessment. These findings demonstrated (1) the importance of event sequence and similarity recognition as necessary input to situation assessment, and (2) these two metacognitive links are significantly associated with the recognition of similar scenarios. Experts and novices did not differ significantly in (1) the number of categories, scenarios per category, and times to classify the tactical situations during sorting and resorting, and (2) their derived weights along the two dimensions, warfare tempo and reaction time, of the multidimensional scaling solution.

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