Consensus function based on multi-layer networks technique

2013 ◽  
Author(s):  
Ghaith Manita
Keyword(s):  
Consensus Function

Consensus function based on cluster-wise two level clustering

2020 ◽  
Author(s):  
Mohammad Reza Mahmoudi ◽  
Hamidreza Akbarzadeh ◽  
Hamid Parvin ◽  
Samad Nejatian ◽  
Vahideh Rezaie ◽  
...  
Keyword(s):  
Consensus Function

Consensus Function Based on Clusters Clustering and Iterative Fusion of Base Clusters

2019 ◽  
Vol 27 (01) ◽  
pp. 97-120 ◽  
Author(s):  
Musa Mojarad ◽  
Hamid Parvin ◽  
Samad Nejatian ◽  
Vahideh Rezaie
Keyword(s):  
Similarity Criteria ◽  
Clustering Methods ◽  
Clustering Ensemble ◽  
Consensus Function ◽  
Robust Clustering ◽  
Binary Clusters ◽  
Data Points ◽  
Cluster Representation ◽  
And Performance ◽  
The Given

In clustering ensemble, it is desired to combine several clustering outputs in order to create better results than the output results of the basic individual clustering methods in terms of consistency, robustness and performance. In this research, we want to present a clustering ensemble method with a new aggregation function. The proposed method is named Robust Clustering Ensemble based on Iterative Fusion of Base Clusters (RCEIFBC). This method takes into account the two similarity criteria: (a) one of them is the cluster-cluster similarity and (b) the other one is the object-cluster similarity. The proposed method has two steps and has been done on the binary cluster representation of the given ensemble. Indeed, before doing any step, the primary partitions are converted into a binary cluster representation where the primary ensemble has been broken into a number of primary binary clusters. The first step is to combine the primary binary clusters with the highest cluster-cluster similarity. This phase will be replicated as long as our desired candidate clusters are ready. The second step is to improve the merged clusters by assigning the data points to the merged clusters. The performance and robustness of the proposed method have been evaluated over different machine learning datasets. The experimentation indicates the effectiveness of the proposed method comparing to the state-of-the-art clustering methods in terms of performance and robustness.

Consensus Function Based on Matrix Factorization

2012 ◽  
Vol 235 ◽  
pp. 15-19
Author(s):  
Li Min Liu ◽  
Xiao Ping Fan ◽  
Yue Shan Xie
Keyword(s):  
Matrix Factorization ◽  
Clustering Analysis ◽  
Clustering Algorithms ◽  
Nonnegative Matrix ◽  
Large Set ◽  
Clustering Ensemble ◽  
Consensus Function ◽  
The Matrix ◽  
Factorization Algorithms ◽  
New Framework

Clustering ensemble has been known as an effective method to improve the robustness and stability of clustering analysis. Clustering ensemble solves the problem in two steps:firstly,generating a large set of clustering partitions based on the clustering algorithms;secondly,combining them using a consensus function to get the final clustering result. The key technology of clustering ensemble is the proper consensus function. Recent research proposed using the matrix factorization to solve clustering ensemble. In this paper, we firstly analyze some traditional matrix factorization algorithms; secondly, we propose a new consensus function using binary nonnegative matrix factorization (BMF) and give the optimization algorithm of BMF; lastly, we propose the new framework of clustering ensemble algorithm and give some experiments on UCI Machine Learning Repository. The experiments show that the new algorithm is effective and clustering performance could be significantly improved.

Consensus and Consensualization of High-Order Swarm Systems With Time Delays and External Disturbances

2012 ◽  
Vol 134 (4) ◽  
Author(s):  
Jianxiang Xi ◽  
Zongying Shi ◽  
Yisheng Zhong
Keyword(s):  
Sufficient Conditions ◽  
Linear Matrix ◽  
Time Varying ◽  
External Disturbances ◽  
Consensus Function ◽  
Design Problems ◽  
Consensus Analysis ◽  
Analysis And Design ◽  
Time Varying Delay ◽  
Necessary And Sufficient

By using dynamic output feedback consensus protocols, consensus analysis, and design, problems for swarm systems with external disturbances and time-varying delays are dealt with. First, two subspaces, namely, a consensus subspace and a complement consensus subspace, are defined. Based on the state projection onto the two subspaces, L2-consensus and L2-consensualization problems are introduced. Then, a necessary and sufficient condition for consensus is presented and an explicit expression of the consensus function is given. Especially, it is shown that the time-varying delay does not influence the consensus function. Finally, in terms of linear matrix inequalities, sufficient conditions for L2-consensus and L2-consensualization are presented, respectively, which possess less calculation complexity, since they are independent of the number of agents, and numerical simulations are shown to demonstrate theoretical results.

scholarly journals State Consensus Analysis and Design for High-Order Discrete-Time Linear Multiagent Systems

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Yanrong Ge ◽  
Yangzhou Chen ◽  
Yaxiao Zhang ◽  
Zhonghe He
Keyword(s):  
Multiagent Systems ◽  
Discrete Time ◽  
The State ◽  
Feedback Gain ◽  
Consensus Problem ◽  
Consensus Protocol ◽  
Consensus Function ◽  
Consensus Analysis ◽  
Analysis And Design ◽  
Time Linear

The paper deals with the state consensus problem of high-order discrete-time linear multiagent systems (DLMASs) with fixed information topologies. We consider three aspects of the consensus analysis and design problem: (1) the convergence criteria of global state consensus, (2) the calculation of the state consensus function, and (3) the determination of the weighted matrix and the feedback gain matrix in the consensus protocol. We solve the consensus problem by proposing a linear transformation to translate it into a partial stability problem. Based on the approach, we obtain necessary and sufficient criteria in terms of Schur stability of matrices and present an analytical expression of the state consensus function. We also propose a design process to determine the feedback gain matrix in the consensus protocol. Finally, we extend the state consensus to the formation control. The results are explained by several numerical examples.

An Ensemble Clusterer Framework based on Valid and Diverse Basic Small Clusters

2021 ◽  
pp. 1-31
Author(s):  
Tao Sun ◽  
Saeed Mashdour ◽  
Mohammad Reza Mahmoudi
Keyword(s):  
Clustering Algorithm ◽  
State Of The Art ◽  
Clustering Algorithms ◽  
Clustering Ensemble ◽  
High Quality ◽  
Consensus Function ◽  
Consensus Functions ◽  
Consensus Partition ◽  
Small Clusters ◽  
Minimum Quality

Clustering ensemble is a new problem where it is aimed to extract a clustering out of a pool of base clusterings. The pool of base clusterings is sometimes referred to as ensemble. An ensemble is to be considered to be a suitable one, if its members are diverse and any of them has a minimum quality. The method that maps an ensemble into an output partition (called also as consensus partition) is named consensus function. The consensus function should find a consensus partition that all of the ensemble members agree on it as much as possible. In this paper, a novel clustering ensemble framework that guarantees generation of a pool of the base clusterings with the both conditions (diversity among ensemble members and high-quality members) is introduced. According to its limitations, a novel consensus function is also introduced. We experimentally show that the proposed clustering ensemble framework is scalable, efficient and general. Using different base clustering algorithms, we show that our improved base clustering algorithm is better. Also, among different consensus functions, we show the effectiveness of our consensus function. Finally, comparing with the state of the art, we find that the clustering ensemble framework is comparable or even better in terms of scalability and efficacy.

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