The Improved K-Means Cluster Analysis on Diagnosis Data Fusion of the Aero-Engine

2013 ◽  
Vol 328 ◽  
pp. 463-467 ◽  
Author(s):  
Xiao Bo Liu ◽  
Bei Bei Deng ◽  
Liang Ni Shen
Keyword(s):  
Hierarchical Clustering ◽  
Data Clustering ◽  
Clustering Algorithm ◽  
Number Of Clusters ◽  
K Value ◽  
Vibration Data ◽  
Engine Vibration ◽  
Aero Engine ◽  
Clustering Center ◽  
Hierarchical Clustering Algorithm

Aiming at the problem about initial clustering center was randomly assigned in K-means clustering algorithm, the improved K-means clustering algorithm based on hierarchical clustering algorithm and K-means clustering algorithm was proposed in this paper. In the improved algorithm, first of all K was calculated by hierarchical clustering. When K was determined, K-means clustering was implemented. The results of the aero-engine vibration data clustering shown that not only the k value was to quickly and accurately determined, but also the number of clusters can be reduced and higher computing efficiency can be attained by the improved K-means clustering algorithm.

Research on Psychology Data Clustering Algorithm Based on CUDA

2014 ◽  
Vol 989-994 ◽  
pp. 1664-1670 ◽  
Author(s):  
Li Li Dong ◽  
Zong Shuai Ma ◽  
Wei Dong ◽  
Xiang Zhang
Keyword(s):  
Hierarchical Clustering ◽  
Experimental Verification ◽  
Data Clustering ◽  
Clustering Algorithm ◽  
Psychological Data ◽  
Cuda Technology ◽  
Clustering Center ◽  
Hierarchical Clustering Algorithm ◽  
A Company

This paper analyzed the employees' MMPI Psychological data of a company. Aiming at the problem that traditional K-Means algorithm is sensitive to the initial clustering center, this paper used hierarchical clustering algorithm CURE to mitigate the problem. Finally using CUDA technology clustered several times, so as to improve the execution efficiency of the algorithm. Through experimental verification, the improved K-Means algorithm behaved well in both execution efficiency and clustering results.

scholarly journals Follicle Detection in Ultrasound Images using Adaptive Clustering Algorithms and Empirical Mode Decomposition

2019 ◽  
Vol 9 (2) ◽  
pp. 2137-2141
Keyword(s):  
Human Body ◽  
Data Clustering ◽  
Clustering Algorithm ◽  
Polycystic Ovary ◽  
Clustering Algorithms ◽  
Ultrasound Image ◽  
Human Reproduction ◽  
Ultrasound Images ◽  
Number Of Clusters ◽  
K Value

Ultrasound Imaging is one of the techniques used to study inside human body with images generated using high frequency sounds waves. The applications of ultrasound images include examination of human body parts such as Kidney, Liver, Heart and Ovaries. This paper mainly concentrates on ultrasound images of ovaries.Monitoring of follicle is important in human reproduction. This paper presents a method for follicle detection in ultrasound image of ovaries using Adaptive data clustering algorithms. The main requirements for any clustering algorithm are to initialize the value of K, i.e. the number of clusters. Estimating this K value is difficult task for given data. This paper presents adaptive data clustering algorithm which generates accurate segmentation results with simple operation and avoids the interactive input K (number of clusters) value for segmentation. The results represent adaptive data clustering algorithms are better than normal algorithms for clustering in ultrasound image segmentation. After segmentation, using the region properties of the image, the follicles in the ovary image are identified. The proposed algorithm is tested on sample ultrasound images of ovaries for identification of follicles and with the region properties, the ovaries are classified into categories, normal, cystic and polycystic ovary with its geometric properties.

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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