Ensemble Clustering by Noise-Aware Graph Decomposition

Ensemble clustering based on evidence extracted from the co-association matrix

The clustering ensemble technique that integrates multiple clustering results can improve the accuracy and robustness of the final clustering. In many clustering ensemble algorithms, the co-association matrix (CA matrix), which reflects the frequency of any two samples being partitioned into the same cluster, plays an important role. However, generally, the CA matrix is highly sparse with low value density, which may limit the performance of an algorithm based on it. To handle these issues, in this paper, we propose a growing tree model (GoT). In this model, the CA matrix is firstly refined by the shortest path technique so that its sparsity will be mitigated. Then, a set of representative prototype examples is discovered. Finally, to handle the low value density of the CA matrix, the prototypes gradually connect to their neighborhood, which likes a set of trees growing up. The rationality of the discovered prototype examples is illustrated by theoretical analysis and experimental analysis. The working mechanism of the GoT is visually shown on synthetic data sets. Experimental analyses on eight UCI data sets and eight image data sets show that the GoT outperforms nine representative clustering ensemble algorithms.

Weighted adaptively ensemble clustering method based on fuzzy Co-association matrix

Clustering ensemble technique has been shown to be effective in improving the accuracy and stability of single clustering algorithms. With the development of information technology, the amount of data, such as image, text and video, has increased rapidly. Efficiently clustering these large-scale datasets is a challenge. Clustering ensembles usually transform clustering results to a co-association matrix, and then to a graph-partition problem. These methods may suffer from information loss when computing the similarity among samples or base clusterings. Rich information between samples and base clusterings is ignored. Moreover, the results are not discrete. They need post-processing steps to obtain the final clustering result, which will deviate greatly from the real clustering result. To address this problem, we propose a co-clustering ensemble based on bilateral k-means (CEBKM) algorithm. Our algorithm can simultaneously cluster samples and base clusterings of a dataset, to fully exploit the potential information between the samples and the base clusterings. In addition, it can directly obtain the final clustering results without using other clustering algorithms. The proposed method, outperformed several state-of-the-art clustering ensemble methods in experiments conducted on real-world and toy datasets.

SummaryCluster ensemble combines multiple base clustering results in a suitable way to improve the accuracy of the clustering result. In the conventional cluster ensemble frameworks, pairwise constraints and constraint projections have not been used together, and spectral clustering algorithm is rarely adopted to serve as the consensus function. In this paper, we design a constraint projections for semi‐supervised spectral clustering ensemble (CPSSSCE) model. It takes advantages of spectral clustering algorithm and executes semi‐supervised learning twice. Compared to traditional cluster ensemble approaches, CPSSSCE is characterized by several properties. First, the original data are transformed to lower‐dimensional representations by constraint projection before base clustering. Second, a similarity matrix is constructed using the base clustering results and modified using pairwise constraints. Third, the spectral clustering algorithm is applied to process the similarity matrix to obtain a consensus cluster result. Extensive experiments on standard University of California Irvine Machine Learning Repository (UCI) and Microsoft datasets demonstrated that the CPSSSCE is superior to other cluster ensemble algorithms including a semi‐supervised spectral clustering ensemble.

Data clustering is an important task and has found applications in numerous real-world problems. Since no single clustering algorithm is able to identify all different types of cluster shapes and structures, ensemble clustering was proposed to combine different partitions of the same data generated by multiple clustering algorithms. The key idea of most ensemble clustering algorithms is to find a partition that is consistent with most of the available partitions of the input data. One problem with these algorithms is their inability to handle uncertain data pairs, i.e. data pairs for which about half of the partitions put them into the same cluster and the other half do the opposite. When the number of uncertain data pairs is large, they can mislead the ensemble clustering algorithm in generating the final partition. To overcome this limitation, we propose an ensemble clustering approach based on the technique of matrix completion. The proposed algorithm constructs a partially observed similarity matrix based on the data pairs whose cluster memberships are agreed upon by most of the clustering algorithms in the ensemble. It then deploys the matrix completion algorithm to complete the similarity matrix. The final data partition is computed by applying an efficient spectral clustering algorithm to the completed matrix. Our empirical studies with multiple real-world datasets show that the proposed algorithm performs significantly better than the state-of-the-art algorithms for ensemble clustering.

Nonnegative matrix factorization for clustering ensemble based on dark knowledge

Dual-granularity weighted ensemble clustering

In this paper, a new method for enhancing the performance of Neural Network ensemble is proposed. The main idea of this method is creating diversity for training artificial neural networks (ANNs) using an interesting method which applies clustering ensemble and genetic algorithm. In combinational classifier systems, the more diversity in results of the base classifiers yields to better final performance. Inspiring from the boosting, the diversity of the base classifiers is provided by different train sets for base classifiers. The different train sets are derived from the original train set by adding some of data samples in train set. Finding near optimal sets is implemented using clustering ensemble technique and genetic algorithm. Finally, the majority vote fuses the outputs of trained MLPs on the new train sets from population of the last generation of GA. Experimental results demonstrate the strength of proposed method on three different datasets.

Region-of-interest extraction in low depth of field images using ensemble clustering and difference of Gaussian approaches

The classic Fuzzy C-means (FCM) algorithm has limited clustering performance and is prone to misclassification of border points. This study offers a bi-directional FCM clustering ensemble approach that takes local information into account (LI_BIFCM) to overcome these challenges and increase clustering quality. First, various membership matrices are created after running FCM multiple times, based on the randomization of the initial cluster centers, and a vertical ensemble is performed using the maximum membership principle. Second, after each execution of FCM, multiple local membership matrices of the sample points are created using multiple K-nearest neighbors, and a horizontal ensemble is performed. Multiple horizontal ensembles can be created using multiple FCM clustering. Finally, the final clustering results are obtained by combining the vertical and horizontal clustering ensembles. Twelve data sets were chosen for testing from both synthetic and real data sources. The LI_BIFCM clustering performance outperformed four traditional clustering algorithms and three clustering ensemble algorithms in the experiments. Furthermore, the final clustering results has a weak correlation with the bi-directional cluster ensemble parameters, indicating that the suggested technique is robust.

Spectral ensemble clustering with doubly stochastic co-association matrix

The link-based cluster ensemble (LCE) method is applied to a high resolution satellite precipitation estimation (HSPE) algorithm, a modified form of the Precipitation Estimation from Remotely Sensed Imagery using an Artificial Neural Network Cloud Classification (PERSIANN-CCS) algorithm. The HSPE involves the following four steps: (1) segmentation of infrared cloud images into patches; (2) cloud patch feature extraction; (3) clustering and classification of cloud patches using cluster ensemble technique; and (4) dynamic application of brightness temperature (Tb) and rain rate relationships, derived using satellite observations. The LCE method combines multiple data partitions from different clustering in order to cluster the cloud patches. The results show that using the cluster ensemble increase the performance of rainfall estimates if compared to the HSPE algorithm using Self Organizing Map (SOM). The Heidke Skill Score (HSS) is improved 5% to 7% at medium and high level of rainfall thresholds.

Ensemble and Consensus Clustering address the problem of unifying multiple clustering results into a single output to best reflect the agreement of input methods. They can be used to obtain more stable and robust clustering results in comparison with a single clustering approach. In this study, we propose a novel subset selection method that looks at controlling the number of clustering inputs and datasets in an efficient way. The authors propose a number of manual selection and heuristic search techniques to perform the selection. Our investigation and experiments demonstrate very promising results. Using these techniques can ensure better selection methods and datasets for Ensemble and Consensus Clustering and thus more efficient clustering results.

Cluster ensembles are approaches to combine different clustering results to obtain a robust consensus partitioning. However, many cluster ensemble methods suffer from the problem of scalability since the extensive cost of calculating co-association matrix, which makes it hard to perform cluster ensemble on large scale datasets. In this paper, we proposed a scalable co-association cluster ensemble framework using a compressed version of co-association matrix formed by selecting representative points of origin instances. Experiments show that our method could get a comparable performance on medium size datasets to existing co-association ensemble method like CSPA or spectral clustering, and is able to handle large scale datasets.