Homogeneous Relational Data Clustering

2010 ◽  
pp. 45-54
Keyword(s):  
Data Clustering ◽  
Relational Data

scholarly journals Evolutionary Algorithms for Robust Density-Based Data Clustering

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Amit Banerjee
Keyword(s):  
Evolutionary Algorithms ◽  
Evolutionary Computation ◽  
Data Clustering ◽  
Relational Data ◽  
Clustering Methods ◽  
Density Based Clustering ◽  
Selection Of

Density-based clustering methods are known to be robust against outliers in data; however, they are sensitive to user-specified parameters, the selection of which is not trivial. Moreover, relational data clustering is an area that has received considerably less attention than object data clustering. In this paper, two approaches to robust density-based clustering for relational data using evolutionary computation are investigated.

scholarly journals Dynamic and Optimized Prototype Clustering for Relational Data based on Multiple Prototypes

2019 ◽  
Vol 8 (3) ◽  
pp. 5630-5634
Keyword(s):  
Artificial Intelligence ◽  
Data Clustering ◽  
Synthetic Data ◽  
Medical Data ◽  
Relational Data ◽  
Data Sets ◽  
Optimization Approach ◽  
Complex Task ◽  
Data Points ◽  
Clustering Approach

In artificial intelligence related applications such as bio-medical, bio-informatics, data clustering is an important and complex task with different situations. Prototype based clustering is the reasonable and simplicity to describe and evaluate data which can be treated as non-vertical representation of relational data. Because of Barycentric space present in prototype clustering, maintain and update the structure of the cluster with different data points is still challenging task for different data points in bio-medical relational data. So that in this paper we propose and introduce A Novel Optimized Evidential C-Medoids (NOEC) which is relates to family o prototype based clustering approach for update and proximity of medical relational data. We use Ant Colony Optimization approach to enable the services of similarity with different features for relational update cluster medical data. Perform our approach on different bio-medical related synthetic data sets. Experimental results of proposed approach give better and efficient results with comparison of different parameters in terms of accuracy and time with processing of medical relational data sets.

Adaptive Hypergraph Auto-Encoder for Relational Data Clustering

2021 ◽  
pp. 1-1
Author(s):  
Youpeng Hu ◽  
Xunkai Li ◽  
Yujie Wang ◽  
Yixuan Wu ◽  
Yining Zhao ◽  
...  
Keyword(s):  
Data Clustering ◽  
Relational Data
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