Fourier Transform Infrared Imaging and Unsupervised Hierarchical Clustering Applied to Cervical Biopsies

2004 ◽  
Vol 57 (12) ◽  
pp. 1139 ◽  
Author(s):  
Keith R. Bambery ◽  
Bayden R. Wood ◽  
Michael A. Quinn ◽  
Don McNaughton
Keyword(s):  
Hierarchical Clustering ◽  
Focal Plane ◽  
Mercury Cadmium Telluride ◽  
Clustering Algorithm ◽  
Infrared Imaging ◽  
Cervical Tissue ◽  
Fourier Transform Infrared Imaging ◽  
Hematoxylin And Eosin ◽  
The Mean ◽  
Hierarchical Clustering Algorithm

FTIR images of cervical tissue from patient biopsies were processed with an unsupervised hierarchical clustering algorithm and compared with hematoxylin- and eosin-stained adjacent sections. Anatomical and potential histopathological features were clearly resolved in the resultant cluster maps. The mean extracted spectra assigned to each cluster indicate that the major spectral differences between the different cells in tissue predictably occur in the amide I region (1700–1570 cm−1) and the phosphodiester/glycogen region (1200–1000 cm−1). FTIR imaging in which a focal plane array mercury–cadmium–telluride detector and unsupervised hierarchical clustering is used shows potential as a rapid, non-subjective diagnostic tool in cervical pathology.

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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