Refinement Properties in Agglomerative Hierarchical Clustering

2009 ◽  
pp. 259-267
Author(s):  
Sadaaki Miyamoto
Keyword(s):  
Hierarchical Clustering ◽  
Agglomerative Hierarchical Clustering

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.

Trend Analysis Using Agglomerative Hierarchical Clustering Approach for Time Series Big Data

2021 ◽  
pp. 869-876
Author(s):  
P. Subbulakshmi ◽  
S. Vimal ◽  
M. Kaliappan ◽  
Y. Harold Robinson ◽  
Mucheol Kim
Keyword(s):  
Time Series ◽  
Big Data ◽  
Trend Analysis ◽  
Hierarchical Clustering ◽  
Agglomerative Hierarchical Clustering ◽  
Clustering Approach

scholarly journals Space-Time Hierarchical Clustering for Identifying Clusters in Spatiotemporal Point Data

2020 ◽  
Vol 9 (2) ◽  
pp. 85 ◽  
Author(s):  
David Lamb ◽  
Joni Downs ◽  
Steven Reader
Keyword(s):  
Hierarchical Clustering ◽  
A Priori ◽  
Space Time ◽  
Moving Object ◽  
Agglomerative Hierarchical Clustering ◽  
Movement Data ◽  
Clustering Approach ◽  
Nested Structure ◽  
Point Data ◽  
Traditional Approaches

Finding clusters of events is an important task in many spatial analyses. Both confirmatory and exploratory methods exist to accomplish this. Traditional statistical techniques are viewed as confirmatory, or observational, in that researchers are confirming an a priori hypothesis. These methods often fail when applied to newer types of data like moving object data and big data. Moving object data incorporates at least three parts: location, time, and attributes. This paper proposes an improved space-time clustering approach that relies on agglomerative hierarchical clustering to identify groupings in movement data. The approach, i.e., space–time hierarchical clustering, incorporates location, time, and attribute information to identify the groups across a nested structure reflective of a hierarchical interpretation of scale. Simulations are used to understand the effects of different parameters, and to compare against existing clustering methodologies. The approach successfully improves on traditional approaches by allowing flexibility to understand both the spatial and temporal components when applied to data. The method is applied to animal tracking data to identify clusters, or hotspots, of activity within the animal’s home range.

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