Weak hierarchies associated with similarity measures—An additive clustering technique

1989 ◽  
Vol 51 (1) ◽  
pp. 133-166
Author(s):  
H. -J. Bandelt ◽  
A. W. M. Dress
Keyword(s):  
Similarity Measures ◽  
Additive Clustering ◽  
Clustering Technique

scholarly journals TCMVS: A Novel Trajectory Clustering Technique Based on Multi-View Similarity

2015 ◽  
Vol 15 (2) ◽  
pp. 53-62 ◽  
Author(s):  
Vijaya Bhaskar Velpula ◽  
Mhm Krishna Prasad
Keyword(s):  
Similarity Measure ◽  
Similarity Measures ◽  
Important Task ◽  
Semantic Features ◽  
Trajectory Clustering ◽  
Clustering Technique ◽  
Novel Approach

Abstract The analysis of moving entities “trajectories” is an important task in different application domains, since it enables the analyst to design, evaluate and optimize navigation spaces. Trajectory clustering is aimed at identifying the objects moving in similar paths and it helps the analysis and obtaining of efficient patterns. Since clustering depends mainly on similarity, the computing similarity between trajectories is an equally important task. For defining the similarity between two trajectories, one needs to consider both the movement and the speed (i.e., the location and time) of the objects, along with the semantic features that may vary. Traditional similarity measures are based on a single viewpoint that cannot explore novel possibilities. Hence, this paper proposes a novel approach, i.e., multi viewpoint similarity measure for clustering trajectories and presents “Trajectory Clustering Based on Multi View Similarity” technique for clustering. The authors have demonstrated the efficiency of the proposed technique by developing Java based tool, called TCMVS and have experimented on real datasets.

IMPROVING STUDENTS ACHIEVEMENT IN VOCABULARY THROUGH LUSTERING TECHNIQUE

2013 ◽  
Vol 2 (2) ◽  
Author(s):  
Lisda Yuniati Tumanggor And Zainuddin
Keyword(s):  
Action Research ◽  
Learning Process ◽  
Quantitative Data ◽  
Teaching And Learning ◽  
Qualitative Data ◽  
Clustering Technique ◽  
Class Viii ◽  
The Subject ◽  
The Mean ◽  
Vocabulary Score

This study attempts to improve students’ vocabulary achievement through Clustering Technique. This study was conducted by using classroom action research. The subject of the research was class VIII-B SMP Katolik Trisakti 2 Medan that consisted of 32 students. The research was conducted in two cycles and each cycle consisted of three meetings. The instruments for collecting data were vocabulary tests for quantitative data and diary notes, observation sheet and questionnaire sheet for qualitative data. Students’ score kept improving in every test. In the first cycle test, the mean of vocabulary score was 64.68. And in the second cycle test, the mean of vocabulary score was 79.85. Based on diary notes, observation sheet and questionnaire sheet, teaching and learning process had done effectively showed the improvement. Every student was studied actively. And from the research can be concluded that Clustering Technique can improve students’ achievement in vocabulary.

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