A panoply of multi-view clustering algorithms has been developed to deal with prevalent multi-view data. Among them, spectral clustering-based methods have drawn much attention and demonstrated promising results recently. Despite progress, there are still two fundamental questions that stay unanswered to date. First, how to fuse different views into one graph. More often than not, the similarities between samples may be manifested differently by different views. Many existing algorithms either simply take the average of multiple views or just learn a common graph. These simple approaches fail to consider the flexible local manifold structures of all views. Hence, the rich heterogeneous information is not fully exploited. Second, how to learn the explicit cluster structure. Most existing methods do not pay attention to the quality of the graphs and perform graph learning and spectral clustering separately. Those unreliable graphs might lead to suboptimal clustering results. To fill these gaps, in this paper, we propose a novel multi-view spectral clustering model which performs graph fusion and spectral clustering simultaneously. The fusion graph approximates the original graph of each individual view but maintains an explicit cluster structure. Experiments on four widely used data sets confirm the superiority of the proposed method.

Multi-graph fusion for multi-view spectral clustering

The support vector machine (SVM) is verified to be effective for predicting cyber security situations, however, the long training time of the prediction model is a drawback to its use. To address this, a cyber security situation prediction model based on MapReduce and the SVM is proposed. The base classifier for this model uses an SVM, and parameter optimization is performed by the Cuckoo Search (CS) to determine the optimal parameters of the SVM. Considering the problem of time cost when a data set is large, we choose to use MapReduce to perform distributed training on SVMs to improve training speed. “Map” is used to map distributed training network security situation data, and “Reduce” merges and sorts the prediction results. Experimental results show that the proposed prediction model has improved the accuracy and decreased the training time cost compared to the traditional model.

/pdf/network-security-situation-prediction-based-on-mr-svm-1nf9ra96gc.pdf

Network Security Situation Prediction Based on MR-SVM

Kriging or Gaussian Process Regression is applied in many fields as a non-linear regression model as well as a surrogate model in the field of evolutionary computation. However, the computational and space complexity of Kriging, that is cubic and quadratic in the number of data points respectively, becomes a major bottleneck with more and more data available nowadays. In this paper, we propose a general methodology for the complexity reduction, called cluster Kriging, where the whole data set is partitioned into smaller clusters and multiple Kriging models are built on top of them. In addition, four Kriging approximation algorithms are proposed as candidate algorithms within the new framework. Each of these algorithms can be applied to much larger data sets while maintaining the advantages and power of Kriging. The proposed algorithms are explained in detail and compared empirically against a broad set of existing state-of-the-art Kriging approximation methods on a well-defined testing framework. According to the empirical study, the proposed algorithms consistently outperform the existing algorithms. Moreover, some practical suggestions are provided for using the proposed algorithms.

/pdf/cluster-based-kriging-approximation-algorithms-for-4sr6gogtfw.pdf

Cluster-based Kriging approximation algorithms for complexity reduction

Deeply transformed subspace clustering

One of the main challenges in Clustering Analysis is choosing the optimal number of clusters. A typical methodology is to evaluate a validity index over the data and to optimize it as a function of the number of clusters. However, this process can have a high computational cost. In this work, we introduce a new approach for recommending the number of clusters for a particular dataset by using Meta-learning. As the predictive performance of the meta-models induced by Meta-learning is affected by how datasets are described by meta-features, we propose a new set of meta-features able to improve the predictive performance of meta-models used for recommending the number of clusters. Experimental results show that the proposed approach provides a good recommendation of the number of clusters. Additionally, the proposed meta-feature obtains better results than meta-features for clustering tasks found in the literature.

A Meta-learning approach for recommending the number of clusters for clustering algorithms

The original p-spectral clustering algorithm can obtain more balanced clustering results by introducing p-Laplacian operator. However, we may not get an ideal clustering result when clustering the multi-model or multi-scale data sets. Moreover, the original p-spectral clustering algorithm is suitable to deal with bipartition situation. when solving the multi-class partition problem, we have to recursively implement bipartition process, which will bring about ineffectiveness of the graph partition, and clustering result is not stable. Therefore, we propose a multiway p-spectral clustering algorithm, which employs local scaling parameter to optimize the calculation of similarity of the data objects. Furthermore, we use multi-eigenvectors to solve the multi-class partition problem by introducing idea of classical spectral clustering NJW algorithm, which can avoid the instability of the clustering result due to the information losing. Accordingly, we can attain the approximate optimal solution of the multi-class partition problem. Experiments show that multiway p-spectral clustering algorithm has much stronger adaptability and robustness, and can produce more balanced clusters.

A multiway p-spectral clustering algorithm

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A multiway p-spectral clustering algorithm