Cluster-based Similarity Partitioning Algorithm Research Articles

A novel member enhancement‐based clustering ensemble algorithm

SummaryClustering ensemble is a popular approach for identifying data clusters that combines the clustering results from multiple base clustering algorithms to produce more accurate and robust data clusters. However, the performance of clustering ensemble algorithms is highly dependent on the quality of clustering members. To address this problem, this paper proposes a member enhancement‐based clustering ensemble (MECE) algorithm that selects the ensemble members by considering their distribution consistency. MECE has two main components, called heterocluster splitting and homocluster merging. The first component estimates two probability density functions (p.d.f.s) estimated on the sample points of an heterocluster and represents them using a Gaussian distribution and a Gaussian mixture model. If the random numbers generated by these two p.d.f.s have different probability distributions, the heterocluster is then split into smaller clusters. The second component merges the clusters that have high neighborhood densities into a homocluster, where the neighborhood density is measured using a novel evaluation criterion. In addition, a co‐association matrix is presented, which serves as a summary for the ensemble of diverse clusters. A series of experiments were conducted to evaluate the feasibility and effectiveness of the proposed ensemble member generation algorithm. Results show that the proposed MECE algorithm can select high quality ensemble members and as a result yield the better clusterings than six state‐of‐the‐art ensemble clustering algorithms, that is, cluster‐based similarity partitioning algorithm (CSPA), meta‐clustering algorithm (MCLA), hybrid bipartite graph formulation (HBGF), evidence accumulation clustering (EAC), locally weighted evidence accumulation (LWEA), and locally weighted graph partition (LWGP). Specifically, MECE algorithm has the nearly 23% higher average NMI, 27% higher average ARI, 15% higher average FMI, and 10% higher average purity than CSPA, MCLA, HBGF, EAC, LWEA, and LWGA algorithms. The experimental results demonstrate that MECE algorithm is a valid approach to deal with the clustering ensemble problems.

Cluster ensemble framework based on the group method of data handling

Cluster ensemble is a powerful method for improving both the robustness and the stability of unsupervised classification solutions. This paper introduced group method of data handling (GMDH) to cluster ensemble, and proposed a new cluster ensemble framework, which named cluster ensemble framework based on the group method of data handling (CE-GMDH). CE-GMDH consists of three components: an initial solution, a transfer function and an external criterion. Several CE-GMDH models can be built according to different types of transfer functions and external criteria. In this study, three novel models were proposed based on different transfer functions: least squares approach, cluster-based similarity partitioning algorithm and semidefinite programming. The performance of CE-GMDH was compared among different transfer functions, and with some state-of-the-art cluster ensemble algorithms and cluster ensemble frameworks on synthetic and real datasets. Experimental results demonstrate that CE-GMDH can improve the performance of cluster ensemble algorithms which used as the transfer functions through its unique modelling process. It also indicates that CE-GMDH achieves a better or comparable result than the other cluster ensemble algorithms and cluster ensemble frameworks.

Combining multiple clusterings of chemical structures using cluster-based similarity partitioning algorithm

Many types of clustering techniques for chemical structures have been used in the literature, but it is known that any single method will not always give the best results for all types of applications. Recent work on consensus clustering methods is motivated because of the successes of combining multiple classifiers in many areas and the ability of consensus clustering to improve the robustness, novelty, consistency and stability of individual clusterings. In this paper, the Cluster-based Similarity Partitioning Algorithm (CSPA) was examined for improving the quality of chemical structures clustering. The effectiveness of clustering was evaluated based on the ability to separate active from inactive molecules in each cluster and the results were compared with the Ward's clustering method. The chemical dataset MDL Drug Data Report (MDDR) database was used for experiments. The results, obtained by combining multiple clusterings, showed that the consensus clustering method can improve the robustness, novelty and stability of chemical structures clustering.

Using Soft Consensus Clustering for Combining Multiple Clusterings of Chemical Structures

The consensus clustering has shown capability to improve the robustness, novelty and stability of individual clusterings in many areas including chemoinformatics. In this paper, graph-based consensus method (cluster-based similarity partitioning algorithm CSPA) and soft consensus clustering were examined for combining multiple clusterings of chemical structures. The clustering is evaluated based on the ability to separate active from inactive molecules in each cluster. Experiments suggest that the effectiveness of soft consensus method can obtain better results than the hard consensus method (CSPA).

Voting-based consensus clustering for combining multiple clusterings of chemical structures.

BackgroundAlthough 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. It is known that any individual clustering method will not always give the best results for all types of applications. So, in this paper, three voting and graph-based consensus clusterings were used for combining multiple clusterings of chemical structures to enhance the ability of separating biologically active molecules from inactive ones in each cluster.ResultsThe cumulative voting-based aggregation algorithm (CVAA), cluster-based similarity partitioning algorithm (CSPA) and hyper-graph partitioning algorithm (HGPA) were examined. The F-measure and Quality Partition Index method (QPI) were used to evaluate the clusterings and the results were compared to the Ward’s clustering method. The MDL Drug Data Report (MDDR) dataset was used for experiments and was represented by two 2D fingerprints, ALOGP and ECFP_4. The performance of voting-based consensus clustering method outperformed the Ward’s method using F-measure and QPI method for both ALOGP and ECFP_4 fingerprints, while the graph-based consensus clustering methods outperformed the Ward’s method only for ALOGP using QPI. The Jaccard and Euclidean distance measures were the methods of choice to generate the ensembles, which give the highest values for both criteria.ConclusionsThe results of the experiments show that consensus clustering methods can improve the effectiveness of chemical structures clusterings. The cumulative voting-based aggregation algorithm (CVAA) was the method of choice among consensus clustering methods.

CONSENSUS FUNCTIONS FOR CLUSTER ENSEMBLES

The major task of clustering is to group an heterogeneous population into unknown groups based on a similarity measure. In order to enhance the robustness and the stability of unsupervised classification solutions, clustering ensembles are used; they are considered to be a powerful tool to deal with this issue. Individual clusterers consolidate the process of decision making through the concept of weighting. The aim is to determine an effective combination method that makes use of the benefits of each clusterer while avoiding its weaknesses. In this paper, we study the problem of combining multiple partitioning without accessing the original features. A genetic algorithm is proposed using three different fitness scores. Following three scenarios: Object Distributed Clustering, Feature Distributed Clustering, and Robust Centralized Clustering, the proposed consensus functions algorithm outperforms three existing ones: Cluster-based Similarity Partitioning Algorithm, HyperGraph Partitioning Algorithm and Meta-Clustering Algorithm.

Cluster ensembles in collaborative filtering recommendation

Recommender systems, which recommend items of information that are likely to be of interest to the users, and filter out less favored data items, have been developed. Collaborative filtering is a widely used recommendation technique. It is based on the assumption that people who share the same preferences on some items tend to share the same preferences on other items. Clustering techniques are commonly used for collaborative filtering recommendation. While cluster ensembles have been shown to outperform many single clustering techniques in the literature, the performance of cluster ensembles for recommendation has not been fully examined. Thus, the aim of this paper is to assess the applicability of cluster ensembles to collaborative filtering recommendation. In particular, two well-known clustering techniques (self-organizing maps (SOM) and k-means), and three ensemble methods (the cluster-based similarity partitioning algorithm (CSPA), hypergraph partitioning algorithm (HGPA), and majority voting) are used. The experimental results based on the Movielens dataset show that cluster ensembles can provide better recommendation performance than single clustering techniques in terms of recommendation accuracy and precision. In addition, there are no statistically significant differences between either the three SOM ensembles or the three k-means ensembles. Either the SOM or k-means ensembles could be considered in the future as the baseline collaborative filtering technique.