Robust Ensemble Clustering by Matrix Completion

Clustering ensemble is an open proposition that aims to solve the limitation of a single cluster algorithm on the diversity of data structures in data partitions. It obtains a consensus model by integrating multiple clustering algorithms. Compared with a single clustering algorithm, it can process the data more accurately and stably, and has a better tolerance for the structure and diversity of the data. This paper proposes to integrate particle swarm optimization for clustering ensemble model. Particularly, the idea is to first use multiple classic clustering algorithms to cluster the original data set to obtain a preliminary clustering result set, and then search for a clustering result that is most consistent with the preliminary results by a particle swarm optimization procedure. In the experiments, we make use of public data sets and micro-precision to evaluate the effectiveness of the proposed clustering ensemble model, and compare it with both base clustering algorithms and other common clustering ensemble algorithms. The evaluation results show that this model can significantly improve the base clustering algorithms and outperforms common clustering ensemble algorithms in most cases.

The localization accuracy of a fingerprint-based localization system is dependent on several factors, one of which is the accuracy and efficiency at which the fingerprint database is clustered. Most highly efficient and accurate clustering algorithms have high time-dependent computational complexity (CC), which tends to limit their practical applicability. A technique that has yet to be explored is the sequential hybridization of multiple low-time CC clustering algorithms to produce a single moderate-time CC clustering algorithm with high localization accuracy. As a result, this paper proposes a clustering algorithm with a moderate time CC that is based on the sequential hybridization of the closest access point (CAP) and improved k-means clustering algorithms. The performance of the proposed sequential hybrid clustering algorithm is determined and compared to the modified affinity propagation clustering (m-APC), fuzzy c-mean (FCM), and 2-CAP algorithms presented in earlier research works using four experimentally generated and publicly available fingerprint databases. The performance metrics considered for the comparisons are the position root mean square error (RMSE) and clustering time based on big O notation. The simulation results show that the proposed sequential hybrid clustering algorithm has improved localization accuracy with position RMSEs of about 54%, 77%, and 52%, respectively, higher than those of the m-APC, FCM, and 2-CAP algorithms. In terms of clustering time, it is 99% and 79% faster than the m-APC and FCM algorithms, respectively, but 90% slower than the 2-CAP algorithm. The results have shown that it is possible to develop a clustering algorithm that has a moderate clustering time with very high localization accuracy through sequential hybridization of multiple clustering algorithms that have a low clustering time with poor localization accuracy. Doi: 10.28991/ESJ-2024-08-02-02 Full Text: PDF

Each clustering algorithm usually optimizes a qualification metric during its learning process. The qualification metric in traditional clustering algorithms considers all the features of under-consideration dataset equally; it means each feature participates in the clustering process equivalently. Considering that some features have more information than the others in a dataset (due to their lower information or their higher variances, etc.), we proposes a fuzzy weighted clustering algorithm. We name this new clustering algorithm, Fuzzy Weighted Locally Adaptive Clustering (FWLAC) algorithm. The proposed FWLAC algorithm is capable of handling imbalanced clustering. However, FWLAC algorithm suffers from its sensitivity to the two parameters that should be tuned manually. The performance of FWLAC algorithm is affected by well-tuning of its parameters. So the paper proposes two solutions to well-tuning of its two parameters. In the first solution, we propose a simple clustering ensemble framework to show the sensitivity of the WLAC algorithm to its manual well-tuning. Although it is not a try-and-error procedure, it is like a grid search, where we use different pairs of values for both parameters h 1 and h 2. Per each pair of values for parameters h 1 and h 2, the algorithm produces a partitioning. So after the grid search, we obtain a large number of partitionings. We break any of the partitionings into its clusters, and then they form an ensemble of clusters. Finally the consensus partitioning is extracted from them by a consensus function. The algorithm is not data dependent at all. For all datasets, we use a similar grid search and a similar set of values for the parameters h 1 and h 2. In this way we have proposed an alternative solution to parameter selection. We use a selection phase to select/remove some clusters from our ensemble of clusters to obtain an elite ensemble of clusters. To do this, a stability measure, normalized mutual information (NMI), was used to validate a cluster. The paper shows the effectiveness of the proposed clustering frameworks both theoretically and experimentally.

An air combat maneuver pattern extraction based on time series segmentation and clustering analysis

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

In remotely sensed data, two commonly held ways are used to obtain a relevant partitioning. The first one consists to apply several clustering algorithms individually, then to choose the best partition. The second commonly held way consists to apply the same clustering algorithm with several initializations and to choose the best partition. It is worth noting that the best partition is chosen according to a given evaluation criterion. Conversely, the Clustering ensembles can provide the best partition with high accuracy and consequently overcome limitations of traditional approaches. Clustering ensembles usually involve two stages. First, multiple partitions are obtained through several runs of initial clustering analysis. Subsequently, the specific consensus function is used in order to find a final consensus partition from multiple input partitions. In this paper, we investigate this technique in the unsupervised classification by using Synthetic data and composite data image. The first stage is assumed by four clustering algorithms, the well-known k-means algorithm, the k-harmonic means algorithm, Fuzzy c-means and the self-organizing map. The best clustering is obtained according to WB index. The relabeling and the voting methods are used in the second stage. Experimental results obtained are satisfactory.

Image segmentation is a classical problem in the area of image processing, multimedia, medical image, and so on. Although there exist a lot of approaches to perform image segmentation, few of them study the image segmentation by the cluster ensemble approach. In this paper, we propose a new algorithm called the cluster ensemble algorithm (CEA) for image segmentation. Specifically, CEA first obtains two set of segmented regions which are partitioned by EM according to the color feature and the texture feature respectively. Then, it integrates these regions to k segmented regions based on the similarity measure and the fuzzy membership function. Finally, CEA performs the denoise algorithm on the segmented regions to remove the noise. The experiments show that CEA works well during the process of image segmentation.KeywordsImage SegmentationTexture FeatureCore RegionColor FeatureExpectation Maximization AlgorithmThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

There are some general procedures to generate the clusterings in clustering ensembles. One can apply different algorithms to create different clusterings of the data. Some clustering algorithms like k-means require initialization of parameters. Different initializations can lead to different clustering results. The parameters of a clustering algorithm, such as the number of clusters, can be altered to create different data clusterings. Different versions of the data can also be used as the input to the clustering algorithm, leading to different partitions. In this paper, we propose a new scheme for constructing multiple independent clusterings using additional artificially generated data. Then, we compare our new method with seven general clustering ensemble constructing methods. The experiments show that our approach can achieve higher or comparable performance than the other methods.

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.

Groundwater resources do not exist in isolation but are deeply connected with social and ecological systems. As humans continue to modify the land surface, drive climate change, and place greater pressures on global freshwater resources, it is increasingly necessary to assess global groundwater resources through their relationships to these coupled systems. While several global classifications of physical groundwater systems exist, there is no data-driven global typology based on groundwater interactions with connected social and ecological systems. Though physical attributes remain hydrogeologically important, a more expansive systems-oriented classification is needed for policy development, applied research, and to develop the next generation of global hydrological models.We fill this gap by producing a spatially-explicit, moderate resolution (5 arcminute) global map of groundwater system archetypes based on groundwater interactions with social and ecological systems. These include interactions with streamflow, ecosystems, climate, agriculture, the economy, and water governance and management, all underpinned by existing global data. Archetypes, each with a unique set of interaction strengths and combinations, form a finite set of characteristic “fingerprints” that represent the dominant modes of interactions between groundwater and connected social and ecological systems. We find all WHYMAP large aquifer systems of the world are characterized by multiple social-ecological archetypes, suggesting that differentiated, context-appropriate approaches are necessary within large aquifers that are often assumed as uniform in global assessments and initiatives.We derive archetypes using multiple clustering algorithms and assign archetype membership based on majority agreement across clustering methods after cluster reclassification to create comparable maps. This multiple-method approach renders the archetypes more robust and less contingent on a single clustering algorithm while simultaneously enabling greater representation of archetype uncertainty.We additionally provide an outlook on sustainable development opportunities and challenges for each archetype. We summarize data sets that represent notable social-ecological outcomes  related to the UN Sustainable Development Goals (SDGs), including: crop yield gaps (SDG 2), remotely sensed groundwater storage trends (SDG 6), economic inequality (SDG 10), human modification of terrestrial systems (SDG 15), and likelihood for hydropolitical interaction (SDG 16), among others. This work provides a number of useful contributions. First, the combination of archetyping (i.e., system characterization) and archetype-specific SDG outlook analysis provides a robust, data-driven overview of the role of groundwater in the global sustainability discourse. Secondly, the archetypes identify social-ecological system similarities across the globe, which may support interregional cooperation and networking, coordinated investment and interventions. Thirdly, as we harness the rapid growth in global data that document groundwater system interactions as the basis for our analysis, we simultaneously provide a synthesis and snapshot of the pertinent global data space. This snapshot can be used to identify the need for further data collection, especially on socio-economic interactions that remain underrepresented in global data. And finally, the archetypes raise awareness, build capacity, and shift mental models about the emerging perspective that it is necessary to conceptualize groundwater as a socially and ecologically connected resource.

Advances made to the traditional clustering algorithms solves the various problems such as curse of dimensionality and sparsity of data for multiple attributes. The traditional H-K clustering algorithm can solve the randomness and apriority of the initial centers of K-means clustering algorithm. But when we apply it to high dimensional data it causes the dimensional disaster problem due to high computational complexity. All the advanced clustering algorithms like subspace and ensemble clustering algorithms improve the performance for clustering high dimension dataset from different aspects in different extent. Still these algorithms will improve the performance form a single perspective. The objective of the proposed model is to improve the performance of traditional H-K clustering and overcome the limitations such as high computational complexity and poor accuracy for high dimensional data by combining the three different approaches of clustering algorithm as subspace clustering algorithm and ensemble clustering algorithm with H-K clustering algorithm.

Ensemble clustering has become a hot research field in intelligent information processing and machine learning. Although significant progress has been made in recent years, there are still two challenging issues in the current ensemble clustering research. First of all, most ensemble clustering algorithms tend to explore similarity at the level of object but lack the ability to explore information at the level of cluster. Secondly, many ensemble clustering algorithms only focus on the direct relationship, while ignoring the indirect relationship between clusters. In order to solve these two problems, a link-based meta-clustering algorithm (L-MCLA) have been proposed in this paper. A series of experiment results demonstrate that the proposed algorithm not only produces better clustering effect but is also less influenced by different ensemble sizes.

In the context of intelligent customer service in the electric power industry, in order to effectively classify customer data in a reasonable and correct manner, greatly improve the analysis effect of big data, and provide accurate customer service, a multi-category Active Learning algorithm based on multiple Clustering algorithms and multivariate Linear regression (ALCL) algorithm was proposed. Firstly, the category matrix corresponding to each algorithm was obtained through multiple heterogeneous clustering algorithms, the category matrix was labeled and pre-classified by querying common points. Secondly, the key examples used to train the weight coefficient model of the clustering algorithm were selected through the largest search strategy and the most confusing query strategy. Thirdly, the objective solving function was defined, the weight coefficients of each clustering algorithm were obtained by training key examples. Finally, samples with high confidence in the results were classified by the classification calculation combined with the weight coefficient. Six public customer data sets in Yuzhong District, Chongqing were used for experiments. The experimental results show that when the classification accuracy of ALCL is the highest, it is improved by 2.07%~14.01% compared with traditional supervised learning algorithms and other active learning algorithms. The results of hypothesis and significance analysis prove that ALCL has better classification effect in customer data processing.

A cluster ensemble method for clustering categorical data

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.