They use the similarity measure to combine multiple partitions, thus avoiding the label correspondence problem. Discussion of our main algorithm is presented in section 4. Recursive feature elimination with ensemble learning using. Using a split and merge strategy combined with a sparse matrix representation, we empirically show that a linear space complexity is achievable in this framework, leading to the scalability of eac method to clustering large datasets. Combining multiple clusterings using evidence accumulation, ieee trans. In the evidence accumulation clustering eac paradigm, the clustering ensemble is transformed into a pairwise coassociation matrix, thus avoiding the label correspondence problem, which is. In the unsupervised paradigm, this task is di cult due to the label. Combining multiple clusterings using evidence accumulation abstract. Index termscluster analysis, combining clustering partitions, cluster fusion, evidence accumulation, robust clustering, kmeans algorithm. Fred and jain 2002 used the kmeans algorithm as the. Merging kmeans with hierarchical clustering for identifying. Combining multiple clusterings using fast simulated. We explore the idea of evidence accumulation for combining the results of multiple clusterings.
However, usually countries are divided into groups via setting some arbitrary. In proceedings of aaai 2002, edmonton, canada, pages 9398. Ensemble clustering aims at finding a consensus partition which agrees as much as possible with base clusterings. From comparing clusterings to combining clusterings zhiwu lu and yuxin peng. Citeseerx combining multiple clusterings using evidence. Ensemble clustering can improve the generalization ability of a single clustering algorithm and generate a more robust clustering result by integrating multiple base clusterings, so it becomes the focus of current clustering research. Cluster ensembles a knowledge reuse framework for combining partitionings. Consensus clustering with robust evidence accumulation andr e louren. Exploiting context analysis for combining multiple entity. The idea of evidence accumulation based clustering is to combine the results of multiple clusterings into a single data partition, by viewing each clustering result as an independent evidence of data organization.
Lncs 2810 refined shared nearest neighbors graph for. Pdf data clustering using evidence accumulation researchgate. To find multiple clusterings on multiview data, yao et al. Comparison of clusterings requirements for multiple clustering solutions. The task of er ensemble is to combine the results of multiple baselevel er systems into a single solution with the goal of increasing the quality of er. A distance measure or, dually, similarity measure thus lies at the heart of document clustering. Pdf we explore the idea of evidence accumulation for combining the results of multiple clusterings. Jun 05, 2012 combining multiple clusterings arises in various important data mining scenarios.
Computation of initial modes for kmodes clustering algorithm. In order to solve this challenging problem we introduce a new graphbased method. The overall method for evidence accumulationbased clustering is summarized below. First, a clustering ensemblea set of object partitions, is produced. Preliminary experiments have shown promising results in terms of integrating di. These clusterings can be compared on substantive grounds, and we also describe an. Although many consensus clustering methods have been successfully used for combining multiple classifiers in many areas such as machine learning, applied statistics, pattern recognition and bioinformatics, few consensus clustering methods have been applied for combining multiple clusterings of chemical structures. A low dimensional embedding method for combining clusterings. Clustering is the most common form of unsupervised learning and this is the major difference between clustering and classification. After the similarity matrices are aggregated, a hierarchical clustering is built on it. Pdf combining multiple clusterings using evidence accumulation. The idea of evidence accumulationbased clustering is to combine the results of multiple clusterings into a single. Eac clustering combining multiple clusterings using evidence accumulation eac 2002 afj05 combo. Pairwise probabilistic clustering using evidence accumulation.
Probabilistic consensus clustering using evidence accumulation. Simpledetectoraggregator anomaly detection simpledetectorcombination. Pdf we explore the idea of evidence accumulation eac for combining the results of multiple clusterings. Clustering ensemble methods produce a consensus partition of a set of data points by combining the results of a collection of base clustering algorithms.
By using various synthetic and real data sets, the clustering performance of the proposed method is systematically studied and compared with that of the conventional. Combining multiple clusterings via crowd agreement estimation and multigranularity link analysis dong huanga,d, jianhuang laia, changdong wangb,c aschool of information science and technology,sun yatsen university,guangzhou,china bschool of mobile information engineering, sun yatsen university, guangzhou, china csysucmu shunde international joint. In the proposed method, kernel support matching is applied to a coassociation matrix that aggregates arbitrary basic partitions in order to detect clusters of complicated shape. Although many researchers still prefer to use hierarchical clustering in one form or another, this is often suboptimal. Here, we utilize the idea of evidence accumulation for combining the results of multiple clusterings. The idea of evidence accumulationbased clustering is to combine the results of multiple clusterings into a single data partition, by viewing each clustering result as an independent evidence of data organization. Combining multiple clusterings arises in various important data mining scenarios. Clustering combining multiple clusterings using evidence accumulation eac 2002 6 anomaly detection simpledetectorcombination. Experimental results of the proposed method on several synthetic and real data sets are compared with other combination strategies, and with individual clustering results. On the scalability of evidence accumulation clustering. Improving fuzzy cmeans clustering algorithm based on a. Simple indicators, like gdp and also complex indicators such as hdi human development index, can be used to measure country development. A celllike p system of degree is defined as follows.
Cluster ensembles can provide robust and stable solutions by leveraging the consensus across multiple clustering results, while averaging out emergent spurious structures that arise due to the various biases to which each participating algorithm is tuned. Robust ensemble clustering by matrix completion biometrics. However, finding a consensus clustering from multiple clusterings is a challenging task because there is no explicit c. Section 3 introduces our novel, similarity graphbased algorithm for combining multiple clusterings. Given a data set n objects or patterns in d dimensions. First, a clustering ensemble a set of object partitions, is produced. A novel inductive ensemble clustering method is proposed. Data clustering using evidence accumulation semantic scholar. Clusterer ensemble combines multiple base clustering estimators by alignment combo. The clustering results are combined using the evidence accumulation technique described in section iii, leading to a new similarity matrix between patterns. Combining multiple w eak clusterings alexander topchy, anil k. Evidence accumulation the idea of evidence accumulationbased clustering is to combine the results of multiple clusterings into a single data partition, by viewing each clustering result as an independent evidence of data organization. It is widely used for data understanding and data reduction. Given a data set n objects or patterns in d dimensions, different ways of producing data partitions are.
Combining multiple clustering using evidence accumulation. Gabased membrane evolutionary algorithm for ensemble clustering. Combining multiple clusterings using similarity graph. Clusterer ensemble combines multiple base clustering estimators by alignment. A scalable approach to balanced, highdimensional clustering of marketbaskets. Jain,fellow, ieee abstractwe explore the idea of evidence accumulation eac for combining the results of multiple clusterings. The challenges of combining multiple outlier detectors lie in its unsupervised nature and extreme data imbalance.
Consensus clustering with robust evidence accumulation. In this paper, we further study and extend the basic wsnng. Combining multiple clusterings by soft correspondence the. Dec 17, 2012 although many consensus clustering methods have been successfully used for combining multiple classifiers in many areas such as machine learning, applied statistics, pattern recognition and bioinformatics, few consensus clustering methods have been applied for combining multiple clusterings of chemical structures.
Inductive ensemble clustering using kernel support matching. Votingbased consensus clustering for combining multiple. It is far from trivial to select the most effective clustering method and its parameterization, for a particular set of gene expression data, because there are a very large number of possibilities. Combining multiple clusterings using fast simulated annealing. Combining multiple clusterings using evidence accumulation aln fred, ak jain ieee transactions on pattern analysis and machine intelligence 27 6, 835850, 2005. Mvmc extracts the individual and shared similarity matrices of multiview data based on the adapted selfrepresentation learning luo et al. A detailed discussion of an evidence accumulation based clustering algorithm, using a split and merge strategy based on the kmeans clustering algorithm, is presented. Combining multiple clusterings into a final clustering which has better overall quality has gained importance recently. Approaches to combining multiple clusterings differ in two main respects, namely the way in which the contributing component clusterings are obtained and the method by which they are combined. Combining multiple clusterings using evidence accumulation core.
Ieee transactions on pattern analysis and machine intelligence. September 2010, 93 pages clustering is a semi or unsupervised process of grouping similar objects together. Clustering ensemble methods produce a consensus partition of a set of data points by combining the results of a collection of base clustering. We explore the idea of evidence accumulation eac for combining the results of multiple clusterings. In this paper, a low dimensional embedding method is proposed. It also has the advantage of naturally detecting the number of clusters and assigning clusters for outofsample data. Evidence accumulation clustering, clustering selection, clustering weighting 1 introduction the combination of multiple sources of information either in the supervised or unsupervised learning setting allows to obtain improvements on the classi cation performance. This paper introduces the problem of combining multiple partitionings of a set of objects into a single consolidated clustering without accessing the features or algorithms that determined these partitionings.
The authors report an improved fuzzy cmeans algorithm in comparison with the conventional one by employing a densityinduced distance metric based on a novel calculation method of relative density degree. Definition of mv load diagrams via weighted evidenc e. An important consensus function is proposed in fred and jain, 2005 to summarize various clustering results in a coassociation matrix. Nov 26, 2019 to find multiple clusterings on multiview data, yao et al. Clustering combination has recently become a hotspot in machine learning, while its critical problem lies on how to combine multiple clusterings to yield a final superior result. We first identify several application scenarios for the resultant knowledge reuse framework that we call cluster ensembles. Combining multiple clusterings using evidence accumulation eac. Combining multiple clusterings using evidence accumulation eac first builds similarity matrix for each base clustering to model the similarity among the cluster assignment among each sample. Taking the cooccurrences of pairs of patterns in the same. Combining multiple clusterings using evidence accumulation ana l.
This yields a unique soft clustering for each number of clusters less than or equal to k. The cluster ensemble problem is then formalized as. Combining multiple clusterings using evidence accumulation ieee. It first obtains the low dimensional embeddings of hyperedges by performing spectral clustering algorithms and then obtains the low. Jain, combining multiple clusterings using evidence accumulation, ieee trans. Combining multiple clusterings using similarity graph selim mimaroglu, ertunc erdil. From comparing clusterings to combining clusterings. The idea of merging clusters is not new in the literature. Combining multiple clusterings using evidence accumulation. Multiple clusterings construct a hypergraph where each object is a vertex, and each cluster is an hyperedge.
Combining multiple classifications of chemical structures using. The research background of the paper covers the development of a country, that can be measured in various ways. Gabased membrane evolutionary algorithm for ensemble. Abstract this paper presents a fast simulated annealing framework for combining multiple clusterings i. Initially, n ddimensional data is decomposed into a large number of compact clusters. Evidence accumulation clustering combines the results of multiple clusterings into a single data partition by viewing each clustering result as an independent evidence of pairwise data organization. Finally, since our method relies on multiple independent initializations, it is inherently parallelizable. To the best of our knowledge, we provide the first theoretical guarantees characterizing the coassociation matrix resulting from evidence accumulation, as well as the first recovery guarantees for any variant of the kss algorithm. For this purpose we need a distance or similarity measure for clusterings. Combining multiple clusterings using evidence accumulation article pdf available in ieee transactions on pattern analysis and machine intelligence 276. Cspa, which is introduced in, is based on a coassociation matrix, and metis, which is a software package for partitioning unstructured graphs and hypergraphs, hgpa is introduced in as well. These are only some applications in which a mean value of multiple clusterings is needed.
Citeseerx document details isaac councill, lee giles, pradeep teregowda. It is known that any individual clustering method will not always give the best. The work on evidence accumulation clustering conducted by fred et al 46 has been used as basis for this work. Using a pairwise frequency count mechanism amongst a clustering committee, the method yields, as an intermediate result, a coassociation matrix. Computation of initial modes for kmodes clustering.
Combining multiple clusterings by soft correspondence. Some recent work on combining multiple clusterings can be found in. Anomaly detection concentrates on identifying the anomalous objects from the general data distribution 2019. The framework proposed in this paper leverages the observation that often no single er method always performs the best, consistently outperforming other er techniques in terms of quality. It is also expected that the final clustering is novel, robust, and scalable. Novel efficient and scalable methods for combining multiple clusterings yagci, arif murat.
1287 781 1228 340 165 200 704 547 129 731 100 1348 449 1500 841 294 747 1110 356 624 383 869 1005 66 38 928 29 1325 1550 1378 384 220 1241 1187 1377 1228 183 547 185 73 854 546