scholarly journals Shared Nearest-Neighbor Quantum Game-Based Attribute Reduction With Hierarchical Coevolutionary Spark and Its Application in Consistent Segmentation of Neonatal Cerebral Cortical Surfaces

2019 ◽  
Vol 30 (7) ◽  
pp. 2013-2027 ◽  
Author(s):  
Weiping Ding ◽  
Chin-Teng Lin ◽  
Zehong Cao
Keyword(s):  
Nearest Neighbor ◽  
Attribute Reduction ◽  
Quantum Game ◽  
Shared Nearest Neighbor

scholarly journals An Efficient Clustering Method for Hyperspectral Optimal Band Selection via Shared Nearest Neighbor

2019 ◽  
Vol 11 (3) ◽  
pp. 350 ◽  
Author(s):  
Qiang Li ◽  
Qi Wang ◽  
Xuelong Li
Keyword(s):  
Nearest Neighbor ◽  
Hyperspectral Image ◽  
Local Density ◽  
Computational Time ◽  
Band Selection ◽  
Data Sets ◽  
Selection Methods ◽  
Clustering Method ◽  
Slope Change ◽  
Shared Nearest Neighbor

A hyperspectral image (HSI) has many bands, which leads to high correlation between adjacent bands, so it is necessary to find representative subsets before further analysis. To address this issue, band selection is considered as an effective approach that removes redundant bands for HSI. Recently, many band selection methods have been proposed, but the majority of them have extremely poor accuracy in a small number of bands and require multiple iterations, which does not meet the purpose of band selection. Therefore, we propose an efficient clustering method based on shared nearest neighbor (SNNC) for hyperspectral optimal band selection, claiming the following contributions: (1) the local density of each band is obtained by shared nearest neighbor, which can more accurately reflect the local distribution characteristics; (2) in order to acquire a band subset containing a large amount of information, the information entropy is taken as one of the weight factors; (3) a method for automatically selecting the optimal band subset is designed by the slope change. The experimental results reveal that compared with other methods, the proposed method has competitive computational time and the selected bands achieve higher overall classification accuracy on different data sets, especially when the number of bands is small.

Adaptive K-Means Algorithm with Dynamically Changing Cluster Centers and K-Value

2012 ◽  
Vol 532-533 ◽  
pp. 1373-1377 ◽  
Author(s):  
Ai Ping Deng ◽  
Ben Xiao ◽  
Hui Yong Yuan
Keyword(s):  
Nearest Neighbor ◽  
Experimental Results ◽  
Data Set ◽  
Number Of Clusters ◽  
K Value ◽  
Testing Data ◽  
Different Types ◽  
Data Points ◽  
Shared Nearest Neighbor

In allusion to the disadvantage of having to obtain the number of clusters in advance and the sensitivity to selecting initial clustering centers in the K-means algorithm, an improved K-means algorithm is proposed, that the cluster centers and the number of clusters are dynamically changing. The new algorithm determines the cluster centers by calculating the density of data points and shared nearest neighbor similarity, and controls the clustering categories by using the average shared nearest neighbor self-similarity.The experimental results of IRIS testing data set show that the algorithm can select the cluster cennters and can distinguish between different types of cluster efficiently.

scholarly journals Discovery of new stellar groups in the Orion complex

2020 ◽  
Vol 643 ◽  
pp. A114 ◽  
Author(s):  
Boquan Chen ◽  
Elena D’Onghia ◽  
João Alves ◽  
Angela Adamo
Keyword(s):  
Nearest Neighbor ◽  
Random Parameter ◽  
Parameter Tuning ◽  
Wide Distribution ◽  
Machine Learning Algorithms ◽  
Star Forming ◽  
Full Characterization ◽  
Shared Nearest Neighbor ◽  
Two Stages ◽  
Parameter Values

We test the ability of two unsupervised machine learning algorithms, EnLink and Shared Nearest Neighbor (SNN), to identify stellar groupings in the Orion star-forming complex as an application to the 5D astrometric data from Gaia DR2. The algorithms represent two distinct approaches to limiting user bias when selecting parameter values and evaluating the relative weights among astrometric parameters. EnLink adopts a locally adaptive distance metric and eliminates the need for parameter tuning through automation. The original SNN relies only on human input for parameter tuning so we modified SNN to run in two stages. We first ran the original SNN 7000 times, each with a randomly generated sample according to within-source co-variance matrices provided in Gaia DR2 and random parameter values within reasonable ranges. During the second stage, we modified SNN to identify the most repeating stellar groups from the 25 798 we obtained in the first stage. We recovered 22 spatially and kinematically coherent groups in the Orion complex, 12 of which were previously unknown. The groups show a wide distribution of distances extending as far as about 150 pc in front of the star-forming Orion molecular clouds, to about 50 pc beyond them, where we, unexpectedly, find several groups. Our results reveal the wealth of sub-structure in the OB association, within and beyond the classical Blaauw Orion OBI sub-groups. A full characterization of the new groups is essential as it offers the potential to unveil how star formation proceeds globally in large complexes such as Orion.

scholarly journals Text Mining for Internship Titles Clustering Using Shared Nearest Neighbor

2017 ◽  
Vol 6 (3) ◽  
pp. 119-126
Author(s):  
Lisna Zahrotun
Keyword(s):  
Information Systems ◽  
Text Mining ◽  
Graduate Program ◽  
Nearest Neighbor ◽  
Cosine Similarity ◽  
Main Theme ◽  
Instructional Media ◽  
Job Description ◽  
University Courses ◽  
Shared Nearest Neighbor

An Internship course becomes one of many compulsory subjects in Under graduate Program of Informatics Engineering in Ahmad Dahlan University, Yogyakarta.In the last few semesters, we found that some students were failed in taking this subject. After being identified, they were facing some obstacles such as determining the main theme for their job description. During this study, we proposed an application to classify the internship titles by using a technique in text mining called Shared Nearest-Neighbor and Cosine Similarity. From the result, we got values from the parameter K is 7, the epsilon value is 0.5, and the value of Mint t is 0.3 with 22 clusters and 0 outlier. These values presented that all data titles of internship activitiesareclassified into each cluster. 7 topics whichtook by majority of students are:1) Information Systems (7 titles);2) Instructional Media (5 titles);3)Archiving Applications (4 titles);4) Web Profile Implementation (3 titles); 5)Instructional Media for University Courses (3 titles); Multimedia (3 titles) and 6)Workshop & Training (3 titles).

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