Multiple Clustering Results Research Articles

Consensus Clustering of Tweet Networks via Semantic and Sentiment Similarity Estimation

Although Twitter has become an important source of information, the number of accessible tweets is too large for users to easily find their desired information. To overcome this difficulty, a method for tweet clustering is proposed in this paper. Inspired by the reports that network representation is useful for multimedia content analysis including clustering, a network-based approach is employed. Specifically, a consensus clustering method for tweet networks that represent relationships among the tweets' semantics and sentiment are newly derived. The proposed method integrates multiple clustering results obtained by applying successful clustering methods to the tweet networks. By integrating complementary clustering results obtained based on semantic and sentiment features, the accurate clustering of tweets becomes feasible. The contribution of this work can be found in the utilization of the features, which differs from existing network-based consensus clustering methods that target only the network structure. Experimental results for a real-world Twitter dataset, which includes 65 553 tweets of 25 datasets, verify the effectiveness of the proposed method.

Enriching Documents by Linking Salient Entities and Lexical-Semantic Expansion

Abstract This paper explores a multi-strategy technique that aims at enriching text documents for improving clustering quality. We use a combination of entity linking and document summarization in order to determine the identity of the most salient entities mentioned in texts. To effectively enrich documents without introducing noise, we limit ourselves to the text fragments mentioning the salient entities, in turn, belonging to a knowledge base like Wikipedia, while the actual enrichment of text fragments is carried out using WordNet. To feed clustering algorithms, we investigate different document representations obtained using several combinations of document enrichment and feature extraction. This allows us to exploit ensemble clustering, by combining multiple clustering results obtained using different document representations. Our experiments indicate that our novel enriching strategies, combined with ensemble clustering, can improve the quality of classical text clustering when applied to text corpora like The British Broadcasting Corporation (BBC) NEWS.

An Initialization Method Based on Hybrid Distance for k-Means Algorithm.

The traditional [Formula: see text]-means algorithm has been widely used as a simple and efficient clustering method. However, the performance of this algorithm is highly dependent on the selection of initial cluster centers. Therefore, the method adopted for choosing initial cluster centers is extremely important. In this letter, we redefine the density of points according to the number of its neighbors, as well as the distance between points and their neighbors. In addition, we define a new distance measure that considers both Euclidean distance and density. Based on that, we propose an algorithm for selecting initial cluster centers that can dynamically adjust the weighting parameter. Furthermore, we propose a new internal clustering validation measure, the clustering validation index based on the neighbors (CVN), which can be exploited to select the optimal result among multiple clustering results. Experimental results show that the proposed algorithm outperforms existing initialization methods on real-world data sets and demonstrates the adaptability of the proposed algorithm to data sets with various characteristics.

RGBD Co-saliency Detection via Bagging-Based Clustering

With the additional depth information, RGBD co-saliency detection, which is an emerging and interesting issue in saliency detection, aims to discover the common salient objects in a set of RGBD images. This letter proposes a novel RGBD co-saliency model using bagging-based clustering. First, candidate object regions are generated based on RGBD single saliency maps and region pre-segmentation. Then, in order to make regional clustering more robust to different image sets, the feature bagging method is introduced to randomly generate multiple clustering results and the cluster-level weak co-saliency maps. Finally, a clustering quality (CQ) criterion is devised to adaptively integrate the weak co-saliency maps into the final co-saliency map for each image. Experimental results on a public RGBD co-saliency dataset show that the proposed co-saliency model significantly outperforms the state-of-the-art co-saliency models.

Semi-supervised hierarchical clustering ensemble and its application

Clustering ensemble is an important part of ensemble learning. It aims to study and integrate multiple clustering results from different clustering algorithms or same algorithm with different initial parameters for the same dataset. CHAMELEON is a hierarchical clustering algorithm which can discover natural clusters of different shapes and sizes as the result of its merging decision dynamically adapts to the different clustering model characterized. Inspired by the idea of CHAMELEON, the paper proposes a novel clustering ensemble models including semi-supervised method and discusses its application in fault diagnosis of high speed train (HST) running gear. The contributions of this paper include: constructing a sparse graph via the similarity matrix which aggregates multiple clustering results; partitioning the sparse graph (vertex=object, edge weight=similarity) into a large number of relatively small sub-clusters; obtaining the final clustering partition by merging these sub-clusters repeatedly. The experimental results demonstrate that our method outperforms some of state-of-the-art ensemble algorithms regarding the accuracy and stability and recognizes fault patterns of HST running gear effectively.

XCluSim: a visual analytics tool for interactively comparing multiple clustering results of bioinformatics data.

BackgroundThough cluster analysis has become a routine analytic task for bioinformatics research, it is still arduous for researchers to assess the quality of a clustering result. To select the best clustering method and its parameters for a dataset, researchers have to run multiple clustering algorithms and compare them. However, such a comparison task with multiple clustering results is cognitively demanding and laborious.ResultsIn this paper, we present XCluSim, a visual analytics tool that enables users to interactively compare multiple clustering results based on the Visual Information Seeking Mantra. We build a taxonomy for categorizing existing techniques of clustering results visualization in terms of the Gestalt principles of grouping. Using the taxonomy, we choose the most appropriate interactive visualizations for presenting individual clustering results from different types of clustering algorithms. The efficacy of XCluSim is shown through case studies with a bioinformatician.ConclusionsCompared to other relevant tools, XCluSim enables users to compare multiple clustering results in a more scalable manner. Moreover, XCluSim supports diverse clustering algorithms and dedicated visualizations and interactions for different types of clustering results, allowing more effective exploration of details on demand. Through case studies with a bioinformatics researcher, we received positive feedback on the functionalities of XCluSim, including its ability to help identify stably clustered items across multiple clustering results.

Combination of Classification and Clustering Results with Label Propagation

This letter considers the combination of multiple classification and clustering results to improve the prediction accuracy. First, an object-similarity graph is constructed from multiple clustering results. The labels predicted by the classification models are then propagated on this graph to adaptively satisfy the smoothness of the prediction over the graph. The convex learning problem is efficiently solved by the label propagation algorithm. A semi-supervised extension is also provided to further improve the performance. Experiments on 11 tasks identify the validity of the proposed models.

Weighted partition consensus via kernels

The combination of multiple clustering results (clustering ensemble) has emerged as an important procedure to improve the quality of clustering solutions. In this paper we propose a new cluster ensemble method based on kernel functions, which introduces the Partition Relevance Analysis step. This step has the goal of analyzing the set of partition in the cluster ensemble and extract valuable information that can improve the quality of the combination process. Besides, we propose a new similarity measure between partitions proving that it is a kernel function. A new consensus function is introduced using this similarity measure and based on the idea of finding the median partition. Related to this consensus function, some theoretical results that endorse the suitability of our methods are proven. Finally, we conduct a numerical experimentation to show the behavior of our method on several databases by making a comparison with simple clustering algorithms as well as to other cluster ensemble methods.

Weighted cluster ensembles

Cluster ensembles offer a solution to challenges inherent to clustering arising from its ill-posed nature. 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. In this article, we address the problem of combining multiple weighted clusters that belong to different subspaces of the input space. We leverage the diversity of the input clusterings in order to generate a consensus partition that is superior to the participating ones. Since we are dealing with weighted clusters, our consensus functions make use of the weight vectors associated with the clusters. We demonstrate the effectiveness of our techniques by running experiments with several real datasets, including high-dimensional text data. Furthermore, we investigate in depth the issue of diversity and accuracy for our ensemble methods. Our analysis and experimental results show that the proposed techniques are capable of producing a partition that is as good as or better than the best individual clustering.

A Confidence Interval for the Wallace Coefficient of Concordance and Its Application to Microbial Typing Methods

Very diverse research fields frequently deal with the analysis of multiple clustering results, which should imply an objective detection of overlaps and divergences between the formed groupings. The congruence between these multiple results can be quantified by clustering comparison measures such as the Wallace coefficient (W). Since the measured congruence is dependent on the particular sample taken from the population, there is variability in the estimated values relatively to those of the true population. In the present work we propose the use of a confidence interval (CI) to account for this variability when W is used. The CI analytical formula is derived assuming a Gaussian sampling distribution and recurring to the algebraic relationship between W and the Simpson's index of diversity. This relationship also allows the estimation of the expected Wallace value under the assumption of independence of classifications. We evaluated the CI performance using simulated and published microbial typing data sets. The simulations showed that the CI has the desired 95% coverage when the W is greater than 0.5. This behaviour is robust to changes in cluster number, cluster size distributions and sample size. The analysis of the published data sets demonstrated the usefulness of the new CI by objectively validating some of the previous interpretations, while showing that other conclusions lacked statistical support.

An Efficient Clustering Algorithm Based on Local Optimality of <I>K</I>-Means

K-Means聚类算法只能保证收敛到局部最优,从而导致聚类结果对初始代表点的选择非常敏感.许多研究工作都着力于降低这种敏感性.然而,K-Means的局部最优和结果敏感性却构成了K-MeanSCAN聚类算法的基础.K-MeanSCAN算法对数据集进行多次采样和K-Means预聚类以产生多组不同的聚类结果,来自不同聚类结果的子簇之间必然会存在交集.算法的核心思想是,利用这些交集构造出关于子簇的加权连通图,并根据连通性合并子簇.理论和实验证明,K-MeanScan算法可以在很大程度上提高聚类结果的质量和算法的效率.;K-Means is the most popular clustering algorithm with the convergence to one of numerous local minima, which results in much sensitivity to initial representatives. Many researches are made to overcome the sensitivity of K-Means algorithm. However, this paper proposes a novel clustering algorithm called K-MeanSCAN by means of the local optimality and sensitivity of K-Means. The core idea is to build the connectivity between sub-clusters based on the multiple clustering results of K-Means, where these clustering results are distinct because of local optimality and sensitivity of K-Means. Then a weighted connected graph of the sub-clusters is constructed using the connectivity, and the sub-clusters are merged by the graph search algorithm. Theoretic analysis and experimental demonstrations show that K-MeanSCAN outperforms existing algorithms in clustering quality and efficiency.