Query: Muti-view clustering ensemble

title: Multi objective multi view clustering ensemble based on evolutionary approach
abstract: Clustering ensembles is a clustering technique which derives a better clustering solution from a set of candidate clustering solutions. Clustering ensemble methods have to address two distinct but interlinked problems: Generating multiple candidate solutions from the data and producing a final clustering solution. Our recently proposed clustering ensembles method (MMOEA) based on NSGA II used multiple views to address the first problem and a novel cluster oriented approach to address the second problem. MMOEA used a simple crossover method to explore the search space and three objective functions to determine the quality of a candidate clustering solution. The use of a simple crossover method led to slow convergence and using three objectives in NSGA II framework is often discouraged. This paper presents a new clustering ensemble method, which introduces new ideas for crossover, mutation, tuning steps and two objective functions (instead of three) in an evolutionary process. The results show that our new method outperforms recent methods for clustering ensembles on different multi view datasets.

title: Multi view ensemble learning based on distance to model and adaptive clustering for imbalanced credit risk assessment in P2P lending
abstract: Credit risk assessment is a crucial task in the peer to peer (P2P) lending industry. In recent years, ensemble learning methods have been verified to perform better in default prediction than individual classifiers and statistical techniques. Real world loan datasets are imbalanced; however, most studies focus on enhancing overall prediction accuracy rather than improving the identification ability of real default loans. Moreover, some of the features that are significantly correlated with default rates are not attached importance in the model construction of previous studies. To fill these gaps, we propose a distance to model and adaptive clustering based multi view ensemble (DM ACME) learning method for predicting default risk in P2P lending. In this method, multi view learning and an adaptive clustering method are explored to produce an ensemble of diverse ensembles constituted by gradient boosting decision trees. A novel combination strategy called distance to model and a soft probability fashion are embedded for model integration. To verify the effectiveness of the proposed ensemble approach, comprehensive analysis on DM ACME, comparative experiments with several state of the art methods, and feature importance evaluation are conducted with the data provided by Lending Club. Experimental results demonstrate the superiority of the proposed method as well as indicate the importance of some features in loan default prediction.

title: Deep low rank subspace ensemble for multi view clustering
abstract: Multi view clustering aims to incorporate complementary information from different data views for more effective clustering. However, it is difficult to obtain the true categories of data based on complex distribution and diversified latent attributes of multi view data. In this paper, we propose a new multi view clustering method that integrates deep matrix factorization, low rank subspace learning, and multiple subspace ensemble in a unified framework, which we term as the Deep Low Rank Subspace Ensemble (DLRSE) method. DLRSE first employs deep matrix factorization to capture diverse and hierarchical structures in data for robust multi view multi layer data representations. Then, low rank subspace representations are learned from the extracted factors to further reveal their correlations, from which more explicit clustering structure can be obtained. We further develop a subspace ensemble learning method with structured sparsity regularization to aggregate different subspaces into a consensus subspace, which can incorporate the intrinsic clustering structure across multiple views better. Extensive experiments on several datasets demonstrate that the proposed method can effectively explore the diversified clustering structure inherent in data and exploit multi view complementary information, and achieve considerable improvement in performance over existing methods.

title: Ensemble Method for Multi view Text Clustering
abstract: Textual data frequently occurs as an unlabeled document collection, therefore it is useful to sort this collection into clusters of related documents. On the other hand, text has different aspects, which a single representation cannot capture. To this end, multi view clustering present an efficient solution to integrate different representations called "views" by exploiting the complementary characteristics of these views. However, the existing methods consider only one representation mode for all views that is based on terms frequencies. Such representation leads to losing valuable information and fails to capture the semantic aspect of text. To overcome these issues, we propose a new method for multi view text clustering that exploits different representations of text in order to improve the quality of clustering. The experimental results show that the proposed method outperforms other methods and enhances the clustering quality.

title: Marginalized Multiview Ensemble Clustering
abstract: Multiview clustering (MVC) which aims to explore the underlying cluster structure shared by multiview data, has drawn more research efforts in recent years. To exploit the complementary information among multiple views, existing methods mainly learn a common latent subspace or develop a certain loss across different views, while ignoring the higher level information such as basic partitions (BPs) generated by the single view clustering algorithm. In light of this, we propose a novel marginalized multiview ensemble clustering (M2VEC) method in this paper. Specifically, we solve MVC in an EC way, which generates BPs for each view individually and seeks for a consensus one. By this means, we naturally leverage the complementary information of multiview data upon the same partition space. In order to boost the robustness of our approach, the marginalized denoising process is adopted to mimic the data corruptions and noises, which provides robust partition level representations for each view by training a single layer autoencoder. A low rank and sparse decomposition is seamlessly incorporated into the denoising process to explicitly capture the consistency information and meanwhile compensate the distinctness between heterogeneous features. Spectral consensus graph partitioning is also involved by our model to make M2VEC as a unified optimization framework. Moreover, a multilayer M2VEC is eventually delivered in a stacked fashion to encapsulate nonlinearity into partition level representations for handling complex data. Experimental results on eight real world data sets show the efficacy of our approach compared with several state of the art multiview and EC methods. We also showcase our method performs well with partial multiview data.

title: From Ensemble Clustering to Multi View Clustering

title: Adaptive melanoma diagnosis using evolving clustering, ensemble and deep neural networks
abstract: In this research, we propose a variant of the Particle Swarm Optimization (PSO) algorithm, namely hybrid learning PSO (HLPSO) for skin lesion segmentation and classification. HLPSO combines diverse search mechanisms including modified Firefly Algorithm (FA) operations, a new spiral research action, probability distributions, crossover, and mutation procedures to diversify and improve the original PSO algorithm. It is used in conjunction with the K Means clustering algorithm to enhance lesion segmentation. Its cost function takes both intra class and inter class variations into account to increase scalability. Two lesion classification systems are formulated based on HLPSO. In the first system, HLPSO is used to devise evolving convolutional neural networks (CNN) with optimized topologies and hyper parameters for lesion classification. In the second system, shape and colour features, as well as texture features extracted using the Kirsch operator and Shift Local Binary Patterns are used to produce an initial discriminative lesion representation. HLPSO is then used to identify the most significant components of each feature vector for ensemble lesion classification. Evaluated using several skin lesion data sets, both systems depict superior capabilities in lesion segmentation, deep CNN architecture generation, and discriminative feature selection for ensemble lesion classification, and outperform a number of advanced PSO and FA variants, classical search methods, as well as other related models on skin lesion classification significantly. HLPSO also yields better performances over other classical and advanced search methods in solving a number of benchmark tasks related to mathematical landscapes and those in the complex CEC 2014 test suite.

title: Ultra Scalable Spectral Clustering and Ensemble Clustering
abstract: This paper focuses on scalability and robustness of spectral clustering for extremely large scale datasets with limited resources. Two novel algorithms are proposed, namely, ultra scalable spectral clustering (U SPEC) and ultra scalable ensemble clustering (U SENC) In U SPEC, a hybrid representative selection strategy and a fast approximation method for <inline formula><tex math notation="LaTeX"$K$/tex math><alternatives><mml:math><mml:mi>K</mml:mi>/mml:math><inline graphic xlink:href="wang ieq1 2903410.gif"/alternatives>/inline formula> nearest representatives are proposed for the construction of a sparse affinity sub matrix. By interpreting the sparse sub matrix as a bipartite graph, the transfer cut is then utilized to efficiently partition the graph and obtain the clustering result. In U SENC, multiple U SPEC clusterers are further integrated into an ensemble clustering framework to enhance the robustness of U SPEC while maintaining high efficiency. Based on the ensemble generation via multiple U SEPC's, a new bipartite graph is constructed between objects and base clusters and then efficiently partitioned to achieve the consensus clustering result. It is noteworthy that both U SPEC and U SENC have nearly linear time and space complexity, and are capable of robustly and efficiently partitioning 10 million level nonlinearly separable datasets on a PC with 64 GB memory. Experiments on various large scale datasets have demonstrated the scalability and robustness of our algorithms. The MATLAB code and experimental data are available at <uri>https:/www.researchgate.net/publication/330760669</uri>

title: EC2: Ensemble Clustering and Classification for Predicting Android Malware Families
abstract: As the most widely used mobile platform, Android is also the biggest target for mobile malware. Given the increasing number of Android malware variants, detecting <italic>malware families</italic> is crucial so that security analysts can identify situations where signatures of a known malware family can be adapted as opposed to manually inspecting behavior of all samples. We present <monospace>EC2</monospace> <bold>E</bold>nsemble <bold>C</bold>lustering and <bold>C</bold>lassification) a novel algorithm for discovering Android malware families of <italic>varying sizes</italic> ranging from very large to very small families (even if previously unseen) We present a performance comparison of several traditional classification and clustering algorithms for Android malware family identification on DREBIN, the largest public Android malware dataset with labeled families. We use the output of both supervised classifiers and unsupervised clustering to design <monospace>EC2</monospace> Experimental results on both the DREBIN and the more recent Koodous malware datasets show that <monospace>EC2</monospace> accurately detects both small and large families, outperforming several comparative baselines. Furthermore, we show how to automatically characterize and explain unique behaviors of specific malware families, such as <monospace>FakeInstaller</monospace> <monospace>MobileTx</monospace> <monospace>Geinimi</monospace> In short, <monospace>EC2</monospace> presents an early warning system for emerging new malware families, as well as a robust predictor of the family (when it is not new) to which a new malware sample belongs, and the design of novel strategies for data driven understanding of malware behaviors.

title: Consensus Guided Multi View Clustering
abstract: In recent decades, tremendous emerging techniques thrive the artificial intelligence field due to the increasing collected data captured from multiple sensors. These multi view data provide more rich information than traditional single view data. Fusing heterogeneous information for certain tasks is a core part of multi view learning, especially for multi view clustering. Although numerous multi view clustering algorithms have been proposed, most scholars focus on finding the common space of different views, but unfortunately ignore the benefits from partition level by ensemble clustering. For ensemble clustering, however, there is no interaction between individual partitions from each view and the final consensus one. To fill the gap, we propose a Consensus Guided Multi View Clustering (CMVC) framework, which incorporates the generation of basic partitions from each view and fusion of consensus clustering in an interactive way, i.e. the consensus clustering guides the generation of basic partitions, and high quality basic partitions positively contribute to the consensus clustering as well. We design a non trivial optimization solution to formulate CMVC into two iterative k means clusterings by an approximate calculation. In addition, the generalization of CMVC provides a rich feasibility for different scenarios, and the extension of CMVC with incomplete multi view clustering further validates the effectiveness for real world applications. Extensive experiments demonstrate the advantages of CMVC over other widely used multi view clustering methods in terms of cluster validity, and the robustness of CMVC to some important parameters and incomplete multi view data.