White Paper: Natural Attraction Forces of Data Bodies within a Data Grid to Describe Efficient Data Distribution Patterns

2005 ◽  
pp. 257-266
Keyword(s):  
Data Distribution ◽  
Distribution Patterns ◽  
White Paper ◽  
Data Grid ◽  
Efficient Data

Efficient Data Distribution for DWS

2008 ◽  
pp. 75-86 ◽  
Author(s):  
Raquel Almeida ◽  
Jorge Vieira ◽  
Marco Vieira ◽  
Henrique Madeira ◽  
Jorge Bernardino
Keyword(s):  
Data Distribution ◽  
Efficient Data

scholarly journals VDPC: Variational Density Peak Clustering Algorithm

2021 ◽  
Author(s):  
Yizhang Wang ◽  
Di Wang ◽  
You Zhou ◽  
Chai Quek ◽  
Xiaofeng Zhang
Keyword(s):  
Clustering Algorithm ◽  
Cluster Formation ◽  
Clustering Algorithms ◽  
Data Distribution ◽  
Distribution Patterns ◽  
Clustering Methods ◽  
Density Peak ◽  
Global Parameter ◽  
Density Peak Clustering ◽  
Parameter Values

<div>Clustering is an important unsupervised knowledge acquisition method, which divides the unlabeled data into different groups \cite{atilgan2021efficient,d2021automatic}. Different clustering algorithms make different assumptions on the cluster formation, thus, most clustering algorithms are able to well handle at least one particular type of data distribution but may not well handle the other types of distributions. For example, K-means identifies convex clusters well \cite{bai2017fast}, and DBSCAN is able to find clusters with similar densities \cite{DBSCAN}. </div><div>Therefore, most clustering methods may not work well on data distribution patterns that are different from the assumptions being made and on a mixture of different distribution patterns. Taking DBSCAN as an example, it is sensitive to the loosely connected points between dense natural clusters as illustrated in Figure~\ref{figconnect}. The density of the connected points shown in Figure~\ref{figconnect} is different from the natural clusters on both ends, however, DBSCAN with fixed global parameter values may wrongly assign these connected points and consider all the data points in Figure~\ref{figconnect} as one big cluster.</div>

scholarly journals Efficient Data Indexing System Based on OpenLDAP in Data Grid

2006 ◽  
pp. 960-964 ◽  
Author(s):  
Hongseok Lee ◽  
Sung-Gon Mun ◽  
Eui-Nam Huh ◽  
Hyunseung Choo
Keyword(s):  
Data Grid ◽  
Data Indexing ◽  
Indexing System ◽  
Efficient Data
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