Efficient Parallel Algorithms for Hierarchical Clustering on Arrays with Reconfigurable Optical Buses

2000 ◽  
Vol 60 (9) ◽  
pp. 1137-1153 ◽  
Author(s):  
Chin-Hsiung Wu ◽  
Shi-Jinn Horng ◽  
Horng-Ren Tsai
Keyword(s):  
Parallel Algorithms ◽  
Hierarchical Clustering ◽  
Optical Buses

ON DESIGNING COMMUNICATION-INTENSIVE ALGORITHMS FOR A SPANNING OPTICAL BUS BASED ARRAY

1995 ◽  
Vol 05 (03) ◽  
pp. 499-511 ◽  
Author(s):  
CHUNMING QIAO
Keyword(s):  
Parallel Algorithms ◽  
Parallel Implementation ◽  
Reconfigurable Array ◽  
The Matrix ◽  
Matrix Transposition ◽  
Optical Buses ◽  
And Control

The Reconfigurable Array with Spanning Optical Buses (or RASOB) architecture provides flexible reconfiguration and strong connectivities with low hardware and control complexities. We use a parallel implementation of the matrix transposition as well as multiplication algorithms as an example to show how the architectural capabilities can be taken advantage of in designing efficient parallel algorithms.

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%

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