scholarly journals Outil de partitionnement hw/sw basé sur l’algorithme Kernighan/Lin amélioré

2007 ◽  
Vol Volume 7, 2007 ◽  
Author(s):  
R. Boudour ◽  
M.T. Laskri
Keyword(s):  
Hierarchical Clustering ◽  
Execution Time ◽  
Clustering Algorithm ◽  
Fast Algorithms ◽  
Software Components ◽  
System Components ◽  
Nous Montrons ◽  
International Audience ◽  
Hierarchical Clustering Algorithm

International audience Partitioning of system functionality for implementation among multiple system components, such as among hardware and software components in codesign, is becoming an increasingly important topic. Various heuristics are used in automatic partitioning. In this paper, we present our tool, called AutoDec, implemented in Visual C++ 6.0. We verified that hierarchical clustering algorithm, based on closeness metrics, can be used to merge pieces of functionality before applying Kernighan/Lin algorithm, resulting in reduced execution time with often improvements in quality. In addition, we show that our approach, when used in partitioning, fills the gap between fast algorithms and highly-optimizing ones. Le partitionnement fonctionnel d’un système, en composants matériels et logiciels, acquiert de plus en plus de l’importance en conception conjointe. Plusieurs heuristiques et algorithmes sont utilisés en partitionnement. Dans ce papier, nous présentons l'outil, appelé AutoDec, implémenté en Visual C++ 6.0. Nous vérifions que l’algorithme hierarchical clustering, basé sur des métriques de rapprochement, peut être utilisé pour fusionner des parties fonctionnelles avant l’application de l’algorithme Kernihgan/Lin, entraînant ainsi une réduction notable du temps d’exécution avec souvent une amélioration accrue en qualité. En somme, nous montrons que notre approche, utilisée en partitionnement, permet de réduire le fossé entre les algorithmes rapides et hautement optimaux

Handling WSD using Hierarchical Clustering Algorithm with sentences

2018 ◽  
pp. 83-88
Author(s):  
Mohana Priya K ◽  
Pooja Ragavi S ◽  
Krishna Priya G
Keyword(s):  
Hierarchical Clustering ◽  
Similarity Measure ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Cosine Similarity Measure ◽  
Hierarchical Clustering Algorithm ◽  
Multiple Levels ◽  
Pos Tagger ◽  
Sentence Clustering ◽  
The Right

Clustering is the process of grouping objects into subsets that have meaning in the context of a particular problem. It does not rely on predefined classes. It is referred to as an unsupervised learning method because no information is provided about the "right answer" for any of the objects. Many clustering algorithms have been proposed and are used based on different applications. Sentence clustering is one of best clustering technique. Hierarchical Clustering Algorithm is applied for multiple levels for accuracy. For tagging purpose POS tagger, porter stemmer is used. WordNet dictionary is utilized for determining the similarity by invoking the Jiang Conrath and Cosine similarity measure. Grouping is performed with respect to the highest similarity measure value with a mean threshold. This paper incorporates many parameters for finding similarity between words. In order to identify the disambiguated words, the sense identification is performed for the adjectives and comparison is performed. semcor and machine learning datasets are employed. On comparing with previous results for WSD, our work has improvised a lot which gives a percentage of 91.2%

scholarly journals Hesitant Fuzzy Linguistic Agglomerative Hierarchical Clustering Algorithm and Its Application in Judicial Practice

Mathematics ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 370
Author(s):  
Shuangsheng Wu ◽  
Jie Lin ◽  
Zhenyu Zhang ◽  
Yushu Yang
Keyword(s):  
Hierarchical Clustering ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Agglomerative Hierarchical Clustering ◽  
Research Gaps ◽  
Judicial Practice ◽  
Linguistic Term ◽  
Clustering Effect ◽  
Hierarchical Clustering Algorithm ◽  
Fuzzy Linguistic

The fuzzy clustering algorithm has become a research hotspot in many fields because of its better clustering effect and data expression ability. However, little research focuses on the clustering of hesitant fuzzy linguistic term sets (HFLTSs). To fill in the research gaps, we extend the data type of clustering to hesitant fuzzy linguistic information. A kind of hesitant fuzzy linguistic agglomerative hierarchical clustering algorithm is proposed. Furthermore, we propose a hesitant fuzzy linguistic Boole matrix clustering algorithm and compare the two clustering algorithms. The proposed clustering algorithms are applied in the field of judicial execution, which provides decision support for the executive judge to determine the focus of the investigation and the control. A clustering example verifies the clustering algorithm’s effectiveness in the context of hesitant fuzzy linguistic decision information.

A bio-inspired hierarchical clustering algorithm with backtracking strategy

2014 ◽  
Vol 42 (2) ◽  
pp. 174-194 ◽  
Author(s):  
Akil Elkamel ◽  
Mariem Gzara ◽  
Hanêne Ben-Abdallah
Keyword(s):  
Hierarchical Clustering ◽  
Clustering Algorithm ◽  
Hierarchical Clustering Algorithm

scholarly journals SIHC: A STABLE INCREMENTAL HIERARCHICAL CLUSTERING ALGORITHM

2009 ◽  
Author(s):  
Ibai Gurrutxaga ◽  
Olatz Arbelaitz ◽  
José I. Martín ◽  
Javier Muguerza ◽  
Jesús M. Pérez ◽  
...  
Keyword(s):  
Hierarchical Clustering ◽  
Clustering Algorithm ◽  
Hierarchical Clustering Algorithm
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