scholarly journals Computational Discovery of Motifs Using Hierarchical Clustering Techniques

2008 ◽  
Author(s):  
Dianhui Wang ◽  
Nung Kion Lee
Keyword(s):  
Hierarchical Clustering ◽  
Clustering Techniques ◽  
Computational Discovery

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

2011 ◽  
Vol 19 (supp01) ◽  
pp. 17-32 ◽  
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.

scholarly journals A comparative study on hierarchical clustering techniques applied to solar flare forecasting

2019 ◽  
Author(s):  
Mirelle Candida Bueno ◽  
Guilherme Palermo Coelho ◽  
Ana Estela Antunes da Silva
Keyword(s):  
Solar Flare ◽  
Comparative Study ◽  
Hierarchical Clustering ◽  
Solar Flares ◽  
High Intensity ◽  
Research Groups ◽  
Clustering Techniques ◽  
Harmful Effects

Due to the harmful effects that high intensity solar flares may cause, several research groups are dedicated to the task of predicting this phenomenon. Given this scenario, the present project applied and compared hierarchical clustering techniques as a preprocessing step to solar flare forecasting, in order to verify whether this approach leads to improvements.

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

1988 ◽  
Vol 32 (5) ◽  
pp. 347-351
Author(s):  
Mark S. Shurtleff ◽  
Joseph A. Jenkins ◽  
Michelle R. Sams
Keyword(s):  
Operating System ◽  
Complex Systems ◽  
Hierarchical Clustering ◽  
Pairwise Similarity ◽  
Promising Alternative ◽  
Clustering Techniques ◽  
Menu System ◽  
Similarity Ratings ◽  
Block Clustering ◽  
Current Systems

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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