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STABILITY CONDITIONS FOR consensus functions DEFINED ON N-TREES

MATHEMATICAL AND COMPUTER MODELLING 1995年第1期22卷 79-87页

作者： BARTHELEMY, JP MCMORRIS, FR POWERS, RC UNIV LOUISVILLE DEPT MATHLOUISVILLEKY 40292

Eight natural extensions of K. Arrow's ''independence of irrelevant alternatives'' axiom (which are called ''stability'' conditions in this paper) for consensus functions on weak or... 详细信息

Eight natural extensions of K. Arrow's ''independence of irrelevant alternatives'' axiom (which are called ''stability'' conditions in this paper) for consensus functions on weak orders are identified for n-trees, and the complete relationships among these eight axioms are determined. The behavior of consensus functions that satisfy the classical Pareto condition, in addition to one of the stability axioms, is also studied.

关键词： consensus functions N-TREES STABILITY AXIOMS

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DICTATORIAL consensus functions ON N-TREES

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MATHEMATICAL SOCIAL SCIENCES 1992年第1期25卷 59-64页

作者： BARTHELEMY, JP MCMORRIS, FR POWERS, RC UNIV LOUISVILLE DEPT MATHLOUISVILLEKY 40292 ENST BRETAGNE INTELLIGENCE ARTIFICIELLE & SYST COGNIT LABF-29285 BRESTFRANCE

The original 'independence of irrelevant alternatives' axiom of K. Arrow has a natural analog when translated from the classical weak order (preference relation) case to n-trees. Using this translated independence axiom for n-trees, it is surprising that Arrow's impossibility Theorem does not follow. Specifically, there exist consensus functions for n-trees that satisfy the independence and Pareto conditions but are not dictatorships. Conversely, a dictatorship must clearly satisfy the Pareto condition but not necessarily independence. In this note it is shown that a consensus function for n-trees is a dictatorship and satisfies independence if and only if it is a projection function.

关键词： consensus functions N-TREES INDEPENDENCE CONDITIONS

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On Seeking consensus Between Document Similarity Measures

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FUNDAMENTA INFORMATICAE 2017年第1期156卷 43-68页

作者： Klopotek, Mieczyslaw Polish Acad Sci Inst Comp Sci Ul Jana Kazimierza 5 PL-01248 Warsaw Poland

This paper investigates the application of consensus clustering and meta-clustering to the set of all possible partitions of a data set. We show that when using a "complement" of Rand Index as a measure of c... 详细信息

关键词： cluster analysis partitioning clustering consensus functions ensemble knowledge reuse unsupervised learning meta-clustering

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A correct proof of the McMorris-Powers' theorem on the consensus of phylogenies

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DISCRETE APPLIED MATHEMATICS 2007年第3期155卷 423-427页

作者： Thatte, Bhalchandra D. Massey Univ Allan Wilson Ctr Mol Ecol & Evolut Palmerston North New Zealand Massey Univ Inst Fundamental Sci Palmerston North New Zealand

McMorris and Powers proved an Arrow-type theorem on phylogenies given as collections of quartets. There is an error in one of the main lemmas used to prove this theorem. However, this lemma (and thereby the theorem) i... 详细信息

关键词： consensus functions phylogenetics

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Approximate Clustering Ensemble Method for Big Data

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IEEE TRANSACTIONS ON BIG DATA 2023年第4期9卷 1142-1155页

作者： Mahmud, Mohammad Sultan Huang, Joshua Zhexue Ruby, Rukhsana Ngueilbaye, Alladoumbaye Wu, Kaishun Shenzhen Univ Natl Engn Lab Big Data Syst Comp Technol Shenzhen 518060 Peoples R China Shenzhen Univ Coll Comp Sci & Software Engn Big Data Inst Shenzhen 518060 Peoples R China Guangdong Lab Artificial Intelligence & Digital Ec Shenzhen 518107 Peoples R China

Clustering a big distributed dataset of hundred gigabytes or more is a challenging task in distributed computing. A popular method to tackle this problem is to use a random sample of the big dataset to compute an approximate result as an estimation of the true result computed from the entire dataset. In this paper, instead of using a single random sample, we use multiple random samples to compute an ensemble result as the estimation of the true result of the big dataset. We propose a distributed computing framework to compute the ensemble result. In this framework, a big dataset is represented in the RSP data model as random sample data blocks managed in a distributed file system. To compute the ensemble clustering result, a set of RSP data blocks is randomly selected as random samples and clustered independently in parallel on the nodes of a cluster to generate the component clustering results. The component results are transferred to the master node, which computes the ensemble result. Since the random samples are disjoint and traditional consensus functions cannot be used, we propose two new methods to integrate the component clustering results into the final ensemble result. The first method uses component cluster centers to build a graph and the METIS algorithm to cut the graph into subgraphs, from which a set of candidate cluster centers is found. A hierarchical clustering method is then used to generate the final set of k cluster centers. The second method uses the clustering-by-passing-messages method to generate the final set of k cluster centers. Finally, the k-means algorithm was used to allocate the entire dataset into k clusters. Experiments were conducted on both synthetic and real-world datasets. The results show that the new ensemble clustering methods performed better than the comparison methods and that the distributed computing framework is efficient and scalable in clustering big datasets.

关键词： Clustering approximation method clustering ensemble consensus functions distributed clustering RSP data model

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Effects of resampling method and adaptation on clustering ensemble efficacy

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ARTIFICIAL INTELLIGENCE REVIEW 2014年第1期41卷 27-48页

作者： Minaei-Bidgoli, Behrouz Parvin, Hamid Alinejad-Rokny, Hamid Alizadeh, Hosein Punch, William F. Iran Univ Sci & Technol Dept Comp Engn Tehran Iran Islamic Azad Univ Sci & Res Branch Dept Comp Engn Tehran Iran Michigan State Univ Dept Comp Sci & Engn E Lansing MI 48824 USA

Clustering ensembles combine multiple partitions of data into a single clustering solution of better quality. Inspired by the success of supervised bagging and boosting algorithms, we propose non-adaptive and adaptive resampling schemes for the integration of multiple independent and dependent clusterings. We investigate the effectiveness of bagging techniques, comparing the efficacy of sampling with and without replacement, in conjunction with several consensus algorithms. In our adaptive approach, individual partitions in the ensemble are sequentially generated by clustering specially selected subsamples of the given dataset. The sampling probability for each data point dynamically depends on the consistency of its previous assignments in the ensemble. New subsamples are then drawn to increasingly focus on the problematic regions of the input feature space. A measure of data point clustering consistency is therefore defined to guide this adaptation. Experimental results show improved stability and accuracy for clustering structures obtained via bootstrapping, subsampling, and adaptive techniques. A meaningful consensus partition for an entire set of data points emerges from multiple clusterings of bootstraps and subsamples. Subsamples of small size can reduce computational cost and measurement complexity for many unsupervised data mining tasks with distributed sources of data. This empirical study also compares the performance of adaptive and non-adaptive clustering ensembles using different consensus functions on a number of datasets. By focusing attention on the data points with the least consistent clustering assignments, whether one can better approximate the inter-cluster boundaries or can at least create diversity in boundaries and this results in improving clustering accuracy and convergence speed as a function of the number of partitions in the ensemble. The comparison of adaptive and non-adaptive approaches is a new avenue for research, and this study hel

关键词： Clustering ensembles consensus functions Distributed data mining Bootstrap Subsampling Adaptive clustering

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Environmental air pollution clustering using enhanced ensemble clustering methodology

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2021年第30期28卷 40746-40755页

作者： Vandhana, Soundararaj Anuradha, Jagadeesan Vellore Inst Technol Vellore 632014 Tamil Nadu India

Air pollution these days could cause severe effects on human health. As human health is crumbled with serious respiratory or other lung diseases, it is prominent to study air pollution. One of the ways to address this issue is by applying clustering techniques. The two main important problems that are faced in the clustering algorithm are, firstly, the exact shape of the cluster and the number of clusters that input data can produce. Secondly, choosing an appropriate algorithm for a particular problem is not clearly known. Finally, multiple replications of the same algorithm lead to alternative solutions due to the fact such as random initialization of cluster heads. Ensembling algorithms can handle these problems and overcome bias and variance in the traditional clustering process. An adequate study has not been carried out in the ensembling approach mainly for clustering. In this paper, we use an enhanced ensemble clustering method to cluster the pollution data levels. This study helps to take preventive measures that are needed to control further contamination, reduce the alarming levels, and analyze the results to find healthy and unhealthy regions in a given area. This ensemble technique also explains about uncertain objects that are found in clustering. The distinct advantage of this algorithm is that there is no requirement of prior information about the data. This experiment shows that the implemented ensemble consensus clustering has demonstrated improved performance when compared with basic clustering algorithms.

关键词： Ensemble clustering consensus functions Air pollution Ensemble members Similarity matrix Cluster certainty

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Aggregation of decomposable measures with application to utility theory

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THEORY AND DECISION 1996年第1期41卷 59-95页

作者： Dubois, D Fodor, JC Prade, H Roubens, M UNIV LIEGE INST MATH B-4000 LIEGE BELGIUM

This paper investigates the eventwise aggregations of decomposable measures preserving the same decomposable property. These operations are obtained by solving a functional equation closely related to the bisymmetry property. Known results for probability as well as possibility measures can be derived as particular cases of our approach. In addition, the unicity of weighted consensus functions is proved in the Archimedean case. An extension of Von Neumann-Morgenstern utility theory is outlined, where probabilities are changed into decomposable measures.

关键词： decomposable measures consensus functions measurable utility

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RAPID CLUSTERING WITH SEMI-SUPERVISED ENSEMBLE DENSITY CENTERS 16

RAPID CLUSTERING WITH SEMI-SUPERVISED ENSEMBLE DENSITY CENTE...

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16th IEEE International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)

作者： Kadhim, Mustafa R. Tian, Wenhong Khan, Tahseen Univ Elect Sci & Technol China Sch Informat & Software Engn Chengdu 611731 Peoples R China

ISBN: (纸本)9781728142425

Clustering algorithm regards robustness, stability, accuracy as performance measurement. In most recent studies, there has been no detailed investigation of resource usage such as CPU, memory, and executing time in most recent studies, one unanticipated finding was that several consensus functions waste many resources to give the outcomes of a small features dataset. In order to resolve these issues, we suggest two new measurement aspects that termed as resource usage and rapidity. Thus, we proposed a new method characterized by rapid implementation, as well as improved the accuracy with the best pure result. This model termed as Rapid Clustering with Semi-supervised Ensemble Density Centers. The strengths of our model is based on fewer iterations and depend on formulas and built-in functions instead of complex coding procedures, then pick objects with its features as cluster centers, and finally, utilize the semi-supervised learning specifically the pairwise constraints, and take advantage of density calculation. Furthermore, the experimental results demonstrated that our model gained the results rapidly with best accuracy and purity.

关键词： consensus functions Density peaks Resource usage Rapidity

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Experimental comparison of cluster ensemble methods

Experimental comparison of cluster ensemble methods

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9th International Conference on Information Fusion

作者： Kuncheva, L. I. Hadjitodorov, S. T. Todorova, L. P. Univ Wales Sch Informat Bangor Gwynedd Wales Bulgarian Acad Sci Cent Lab Biomed Engn CLBME Sofia Bulgaria

ISBN: (纸本)9781424409532

Cluster ensembles are deemed to be a robust and accurate alternative to single clustering runs. 24 methods for designing cluster ensembles are compared here using 24 data sets, both artificial and real. Adjusted Rand index and classification accuracy are used as accuracy criteria with respect to a known partition assumed to be the "true" one. The data sets are randomly chosen to represent medium-size problems arising within a variety of biomedical domains. Ensemble size of 10 was considered. It was found that there is a significant difference among the compared methods (Friedman's Two Way ANOVA). The best ensembles were based on k-means individual clusterers. consensus functions interpreting the consensus matrix of the ensemble as data, rather than similarity, were found to be significantly better than the traditional alternatives, including CSPA and HGPA.

关键词： clustering cluster ensembles consensus functions experimental comparison biomedical data

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