Assessing the Performance of a Graph-Based Clustering Algorithm

2007 ◽  
pp. 215-227 ◽  
Author(s):  
Pasquale Foggia ◽  
Gennaro Percannella ◽  
Carlo Sansone ◽  
Mario Vento
Keyword(s):  
Clustering Algorithm ◽  
Graph Based Clustering

scholarly journals GRAPH BASED CLUSTERING WITH CONSTRAINTS AND ACTIVE LEARNING

2021 ◽  
Vol 37 (1) ◽  
pp. 71-89
Author(s):  
Vu-Tuan Dang ◽  
Viet-Vu Vu ◽  
Hong-Quan Do ◽  
Thi Kieu Oanh Le
Keyword(s):  
Active Learning ◽  
Clustering Algorithm ◽  
Side Information ◽  
Clustering Algorithms ◽  
Real Data ◽  
Data Sets ◽  
Data Set ◽  
Supervised Clustering ◽  
Class Labels ◽  
Graph Based Clustering

During the past few years, semi-supervised clustering has emerged as a new interesting direction in machine learning research. In a semi-supervised clustering algorithm, the clustering results can be significantly improved by using side information, which is available or collected from users. There are two main kinds of side information that can be learned in semi-supervised clustering algorithms: the class labels - called seeds or the pairwise constraints. The first semi-supervised clustering was introduced in 2000, and since that, many algorithms have been presented in literature. However, it is not easy to use both types of side information in the same algorithm. To address the problem, this paper proposes a semi-supervised graph based clustering algorithm that tries to use seeds and constraints in the clustering process, called MCSSGC. Moreover, we introduces a simple but efficient active learning method to collect the constraints that can boost the performance of MCSSGC, named KMMFFQS. In order to verify effectiveness of the proposed algorithm, we conducted a series of experiments not only on real data sets from UCI, but also on a document data set applied in an Information Extraction of Vietnamese documents. These obtained results show that the proposed algorithm can significantly improve the clustering process compared to some recent algorithms.

scholarly journals AutoGraph: Autonomous Graph Based Clustering of Small-Molecule Conformations

2020 ◽  
Author(s):  
Kiyoto Tanemura ◽  
Susanta Das ◽  
Kenneth M. Merz Jr.
Keyword(s):  
Potential Energy ◽  
Potential Energy Surface ◽  
Clustering Algorithm ◽  
Energy Surface ◽  
Conformational Ensemble ◽  
Threshold Values ◽  
Optimal Method ◽  
Flexible Molecules ◽  
Hyperparameter Selection ◽  
Graph Based Clustering

<div> <div> <div> <p>While accurately modeling the conformational ensemble is required for predicting properties of flexible molecules, the optimal method of obtaining the conformational ensemble seems as varied as their applications. Ensemble structures have been modeled by generation, refinement, and clustering of conformations with a sufficient number of samples. We present a conformational clustering algorithm intended to automate the conformational clustering step through the Louvain algorithm, which requires minimal hyperparameters and importantly no predefined number of clusters or threshold values. The conformational graphs produced by this method for O-succinyl-L-homoserine, oxidized nicotinamide adenine dinucleotide, and 200 representative metabolites each preserved the geometric/energetic correlation expected for points on the potential energy surface. Clustering based on these graphs provide partitions informed by the potential energy surface. Automating conformational clustering in a workflow with AutoGraph may mitigate human biases introduced by guess-and-check over hyperparameter selection while allowing flexibility to the result by not imposing predefined criteria other than optimizing the model’s loss function. Associated codes are available at https://github.com/TanemuraKiyoto/AutoGraph . </p> </div> </div> </div>

Joint Spectrum and Power Allocation for Femtocell Based on Graph and Game Theory

2014 ◽  
Vol 989-994 ◽  
pp. 2051-2056
Author(s):  
Ying Wang ◽  
Qi Zhu
Keyword(s):  
Game Theory ◽  
Power Allocation ◽  
Clustering Algorithm ◽  
Cooperative Game Theory ◽  
Entire System ◽  
Femtocell Networks ◽  
Joint Spectrum ◽  
Simulation Results ◽  
Graph Based Clustering

In order to mitigate the downlink interference in two-tier femtocell networks, a joint spectrum and power allocation algorithm based on graph and game theory is proposed in this paper. On the premise of ensuring the QoS (Quality of Service) of MUEs (microcell user equipments), we first adopt graph-based clustering algorithm to assign subbands to femtocells. Then, for femtocells of each cluster, their transmitting power is redistributed based on the non-cooperative game theory to further reduce the interference among them. Simulation results show that this algorithm can further improves the performance of the entire system compared with pure clustering strategy and ensures the QoS of MUEs at the same time.

Adaptive multi-resolution graph-based clustering algorithm for electrofacies analysis

2020 ◽  
Vol 17 (1) ◽  
pp. 13-25
Author(s):  
Hongliang Wu ◽  
Chen Wang ◽  
Zhou Feng ◽  
Ye Yuan ◽  
Hua-Feng Wang ◽  
...  
Keyword(s):  
Clustering Algorithm ◽  
Resolution Graph ◽  
Graph Based Clustering

scholarly journals Iterative Min Cut Clustering Based on Graph Cuts

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 474
Author(s):  
Bowen Liu ◽  
Zhaoying Liu ◽  
Yujian Li ◽  
Ting Zhang ◽  
Zhilin Zhang
Keyword(s):  
Machine Learning ◽  
Simple Formula ◽  
Clustering Algorithm ◽  
Graph Cut ◽  
Hard Problem ◽  
Running Time ◽  
Benchmark Datasets ◽  
Np Hard Problem ◽  
Graph Based Clustering ◽  
Min Cut

Clustering nonlinearly separable datasets is always an important problem in unsupervised machine learning. Graph cut models provide good clustering results for nonlinearly separable datasets, but solving graph cut models is an NP hard problem. A novel graph-based clustering algorithm is proposed for nonlinearly separable datasets. The proposed method solves the min cut model by iteratively computing only one simple formula. Experimental results on synthetic and benchmark datasets indicate the potential of the proposed method, which is able to cluster nonlinearly separable datasets with less running time.

scholarly journals AutoGraph: Autonomous Graph Based Clustering of Small-Molecule Conformations

2020 ◽  
Author(s):  
Kiyoto Tanemura ◽  
Susanta Das ◽  
Kenneth M. Merz Jr.
Keyword(s):  
Potential Energy ◽  
Potential Energy Surface ◽  
Clustering Algorithm ◽  
Energy Surface ◽  
Conformational Ensemble ◽  
Threshold Values ◽  
Optimal Method ◽  
Flexible Molecules ◽  
Hyperparameter Selection ◽  
Graph Based Clustering

<div> <div> <div> <p>While accurately modeling the conformational ensemble is required for predicting properties of flexible molecules, the optimal method of obtaining the conformational ensemble seems as varied as their applications. Ensemble structures have been modeled by generation, refinement, and clustering of conformations with a sufficient number of samples. We present a conformational clustering algorithm intended to automate the conformational clustering step through the Louvain algorithm, which requires minimal hyperparameters and importantly no predefined number of clusters or threshold values. The conformational graphs produced by this method for O-succinyl-L-homoserine, oxidized nicotinamide adenine dinucleotide, and 200 representative metabolites each preserved the geometric/energetic correlation expected for points on the potential energy surface. Clustering based on these graphs provide partitions informed by the potential energy surface. Automating conformational clustering in a workflow with AutoGraph may mitigate human biases introduced by guess-and-check over hyperparameter selection while allowing flexibility to the result by not imposing predefined criteria other than optimizing the model’s loss function. Associated codes are available at https://github.com/TanemuraKiyoto/AutoGraph . </p> </div> </div> </div>

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