A Multi-Relational Hierarchical Clustering Algorithm Based on Shared Nearest Neighbor Similarity

2007 ◽  
Author(s):  
Jing-Feng Guo ◽  
Yu-Yan Zhao ◽  
Jing Li
Keyword(s):  
Hierarchical Clustering ◽  
Clustering Algorithm ◽  
Nearest Neighbor ◽  
Hierarchical Clustering Algorithm ◽  
Shared Nearest Neighbor

scholarly journals A Novel Local Density Hierarchical Clustering Algorithm Based on Reverse Nearest Neighbors

2019 ◽  
Vol 2019 ◽  
pp. 1-10
Author(s):  
Yaohui Liu ◽  
Dong Liu ◽  
Fang Yu ◽  
Zhengming Ma
Keyword(s):  
Hierarchical Clustering ◽  
Clustering Algorithm ◽  
Nearest Neighbor ◽  
Local Density ◽  
Clustering Algorithms ◽  
Real Data ◽  
Nearest Neighbors ◽  
Clustering Methods ◽  
Density Peak ◽  
Hierarchical Clustering Algorithm

Clustering is widely used in data analysis, and density-based methods are developed rapidly in the recent 10 years. Although the state-of-art density peak clustering algorithms are efficient and can detect arbitrary shape clusters, they are nonsphere type of centroid-based methods essentially. In this paper, a novel local density hierarchical clustering algorithm based on reverse nearest neighbors, RNN-LDH, is proposed. By constructing and using a reverse nearest neighbor graph, the extended core regions are found out as initial clusters. Then, a new local density metric is defined to calculate the density of each object; meanwhile, the density hierarchical relationships among the objects are built according to their densities and neighbor relations. Finally, each unclustered object is classified to one of the initial clusters or noise. Results of experiments on synthetic and real data sets show that RNN-LDH outperforms the current clustering methods based on density peak or reverse nearest neighbors.

Facioscapulohumeral Muscular Dystrophy Diagnosis Using Hierarchical Clustering Algorithm and K-Nearest Neighbor Based Methodology

2017 ◽  
Vol 8 (2) ◽  
pp. 33-46 ◽  
Author(s):  
Divya Anand ◽  
Babita Pandey ◽  
Devendra K. Pandey
Keyword(s):  
Discriminant Analysis ◽  
Muscular Dystrophy ◽  
Hierarchical Clustering ◽  
Clustering Algorithm ◽  
Nearest Neighbor ◽  
Facioscapulohumeral Muscular Dystrophy ◽  
Distance Metric ◽  
K Nearest Neighbor ◽  
Number Of Genes ◽  
Hierarchical Clustering Algorithm

The genetic diagnosis of neuromuscular disorder is an active area of research. Microarrays are used to detect the changes in genes for the accurate diagnosis. Unfortunately, the number of genes in gene expression data is very large as compared to number of samples. The number of genes needs to be reduced for correct diagnosis. In the present paper, the authors have made an intelligent integrated model for clustering and diagnosis of neuromuscular diseases. Wilcoxon signed rank test is used to preselect the genes. K-means and hierarchical clustering algorithms with different distance metric are employed to cluster the genes. Three classifiers namely linear discriminant analysis, quadratic discriminant analysis and k-nearest neighbor are used. For the employment of integrated techniques, a balanced facioscapulohumeral muscular dystrophy dataset is taken. A comparative analysis of the above integrated algorithms is presented which demonstrate that the integration of cosine distance metric hierarchical clustering algorithm with k-nearest neighbor has given the best performance measures.

NBC: An Efficient Hierarchical Clustering Algorithm for Large Datasets

2015 ◽  
Vol 09 (03) ◽  
pp. 307-331 ◽  
Author(s):  
Wei Zhang ◽  
Gongxuan Zhang ◽  
Yongli Wang ◽  
Zhaomeng Zhu ◽  
Tao Li
Keyword(s):  
Hierarchical Clustering ◽  
Time Complexity ◽  
Clustering Algorithm ◽  
Nearest Neighbor ◽  
Clustering Algorithms ◽  
Large Datasets ◽  
Nearest Neighbor Search ◽  
Large Dataset ◽  
Neighbor Search ◽  
Hierarchical Clustering Algorithm

Nearest neighbor search is a key technique used in hierarchical clustering and its computing complexity decides the performance of the hierarchical clustering algorithm. The time complexity of standard agglomerative hierarchical clustering is O(n3), while the time complexity of more advanced hierarchical clustering algorithms (such as nearest neighbor chain, SLINK and CLINK) is O(n2). This paper presents a new nearest neighbor search method called nearest neighbor boundary (NNB), which first divides a large dataset into independent subset and then finds nearest neighbor of each point in subset. When NNB is used, the time complexity of hierarchical clustering can be reduced to O(n log 2n). Based on NNB, we propose a fast hierarchical clustering algorithm called nearest-neighbor boundary clustering (NBC), and the proposed algorithm can be adapted to the parallel and distributed computing framework. The experimental results demonstrate that our algorithm is practical for large datasets.

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.

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