Multiview subspace clustering has turned into a promising technique due to its encouraging ability to discover the underlying subspace structure. In recent studies, a lot of subspace clustering methods have been developed to strengthen the clustering performance of multiview data, but these methods rarely consider simultaneously the nonlinear structure and multilevel representation (MLR) information in multiview data as well as the data distribution of latent representation. To address these problems, we develop a new Multiview Subspace Clustering with MLRs and Adversarial Regularization (MvSC-MRAR), where multiple deep auto-encoders are utilized to model nonlinear structure information of multiview data, multiple self-expressive layers are introduced into each deep auto-encoder to extract multilevel latent representations of each view data, and diversity regularizations are designed to preserve complementary information contained in different layers and different views. Furthermore, a universal discriminator based on adversarial training is developed to enforce the output of each encoder to obey a given prior distribution, so that the affinity matrix for spectral clustering (SPC) is more realistic. Comprehensive empirical evaluation with nine real-world multiview datasets indicates that our proposed MvSC-MRAR achieves significant improvements than several state-of-the-art methods in terms of clustering accuracy (ACC) and normalized mutual information (NMI).

Abstract Multi-view clustering (MVC), which aims to explore the underlying structure of data by leveraging heterogeneous information of different views, has brought along a growth of attention. Multi-view clustering algorithms based on different theories have been proposed and extended in various applications. However, most existing MVC algorithms are shallow models, which learn structure information of multi-view data by mapping multi-view data to low-dimensional representation space directly, ignoring the nonlinear structure information hidden in each view, and thus, the performance of multi-view clustering is weakened to a certain extent. In this paper, we propose a deep multi-view clustering algorithm based on multiple auto-encoder, termed MVC-MAE, to cluster multi-view data. MVC-MAE adopts auto-encoder to capture the nonlinear structure information of each view in a layer-wise manner and incorporate the local invariance within each view and consistent as well as complementary information between any two views together. Besides, we integrate the representation learning and clustering into a unified framework, such that two tasks can be jointly optimized. Extensive experiments on six real-world datasets demonstrate the promising performance of our algorithm compared with 15 baseline algorithms in terms of two evaluation metrics.

Multi-view clustering has attracted increasing attention in recent years since many real data sets are usually gathered from different sources or described by different feature types. Amongst various existing multi-view clustering algorithms, those that are based on non-negative matrix factorization (NMF) have exhibited superior performance. However, NMF decomposing original data directly fails to exploit global relationships between data samples and cannot be applied to datasets that are not strictly non-negative. In this paper, a network-based sparse and multi-manifold regularized multiple NMF (NSM_MNMF) for multi-view clustering is proposed, where multi-view data is transformed into multiple networks, and NMF is used to jointly factorize transformed multiple networks for capturing the shared cluster structure embedded in different views. Furthermore, sparse and multi-manifold regularization are incorporated into NMF to keep the intrinsic geometrical information of the multi-view network manifold space. Networks characterize intra-view similarity, and joint factorization reveals inter-view similarity across distinct views, while using NMF to decompose the networks instead of the original data means NSM_MNMF can be applied to datasets that are not strictly non-negative and the clustering results are interpretable. Extensive experiments are conducted on nine real data sets to assess the method proposed, and the results illustrate that NSM_MNMF outperforms other baseline approaches.

Multi-view clustering aims to group similar samples into the same clusters and dissimilar samples into different clusters by integrating heterogeneous information from multi-view data. Non-negative matrix factorization (NMF) has been widely applied to multi-view clustering owing to its interpretability. However, most NMF-based algorithms only factorize multi-view data based on the shallow structure, neglecting complex hierarchical and heterogeneous information in multi-view data. In this paper, we propose a deep multiple non-negative matrix factorization (DMNMF) framework based on AutoEncoder for multi-view clustering. DMNMF consists of multiple Encoder Components and Decoder Components with deep structures. Each pair of Encoder Component and Decoder Component are used to hierarchically factorize the input data from a view for capturing the hierarchical information, and all Encoder and Decoder Components are integrated into an abstract level to learn a common low-dimensional representation for combining the heterogeneous information across multi-view data. Furthermore, graph regularizers are also introduced to preserve the local geometric information of each view. To optimize the proposed framework, an iterative updating scheme is developed. Besides, the corresponding algorithm called MVC-DMNMF is also proposed and implemented. Extensive experiments on six benchmark datasets have been conducted, and the experimental results demonstrate the superior performance of our proposed MVC-DMNMF for multi-view clustering compared to other baseline algorithms.

Network embedding (NE) is a method that maps nodes in a network into a low-dimensional and continuous vector space while maintains inherent features of the network. Most existing algorithms for NE focus on one or two of the aspects of topological structure, node attributes or community structure information, but without integrating the three in a unified framework. In this study, we develop a deep neural network-based framework for Attributed Network Embedding with Community Preservation (ANECP), which simultaneously incorporates the topological structure, node attributes as well as community structure together to obtain the low-dimensional distributed representations of nodes in the network. The use of deep neural networks captures the underlying high non-linearity in both topology and attribute information, while the incorporation of the community structure resolves the issues of data sparsity from microscopic perspective. Consequently, the obtained node representations can preserve proximity and discriminative. We conducted experimental studies using six real-world datasets. The experimental results show that proposed ANECP has superior performance over the existing methods.

Abstract Influence diagrams (IDs) are one of the most commonly used graphical and mathematical decision models for reasoning under uncertainty. In conventional IDs, both probabilities representing beliefs and utilities representing preferences of decision makers are precise point‐valued parameters. However, it is usually difficult or even impossible to directly provide such parameters. In this paper, we extend conventional IDs to allow IDs with interval‐valued parameters (IIDs) and develop a counterpart method of Copper's evaluation method to evaluate IIDs. IIDs avoid the difficulties attached to the specification of precise parameters and provide the capability to model decision‐making processes in a situation that the precise parameters cannot be specified. The counterpart method to Copper's evaluation method reduces the evaluation of IIDs into inference problems of Bayesian networks with interval‐valued probabilities. An algorithm based on the approximate inference of Bayesian networks with interval‐valued probabilities is proposed, and extensive experiments are conducted. The experimental results indicate that the proposed algorithm can find the optimal strategies effectively in IIDs, and the interval‐valued expected utilities obtained by proposed algorithm are contained in those obtained by exact evaluating algorithms. The newly development approach would significantly extend the range of IDs for managerial decision support applications where parameters of variables can be specifically defined by only estimated intervals.

Abstract Detecting communities is of great importance in social network analysis. However it is an issue that has not yet been satisfactorily solved, despite the efforts made by interdisciplinary research communities over the past few years, because of the nature of complexity in deciding how community structures should be recognized. In this paper we propose an approach based on cooperative game theory for community detection in social networks. We regard individuals as players, and regard communities as coalitions formed by players, and model community detection problem as the formation and optimization of coalitions. Furthermore, we define coalition profile for players to indicate coalitions that players joined, the order of a coalition profile is defined as the number of coalitions in a coalition profile, and we introduce a utility function to measure preference of coalition profiles. Accordingly, we propose an algorithm to detect a coalition profile with maximal utility function values. We have implemented the algorithms developed in this study and experimental results demonstrate the effectiveness of our approaches.

Online community detection is essential for social network analysis. Modularity is a quality function used to measure the strength of the community structure discovered with social networks. Existing methods detect community structures through modularity analysis. However, the existing modularities are unable to identify community structures correctly when communities are very different in size, in particular, when the size of some communities is very small compared with others. To address this problem, we propose the concept of a coupling coefficient between two communities and define a new modularity, MC modularity, to evaluate the quality of the discovered community structures. This method can provide adequate measures for the quality of community structures. In addition, we develop the DC_MC algorithm to detect community structures based on the concept of MC modularity. An algorithm for shifting a group of nodes, instead of one node at a time, amongst communities also is designed to achieve optimal results. The effectiveness and efficacy of the approach we proposed have been demonstrated through a set of experiments involving real data benchmarks and synthetic data sets that are purpose-built for evaluating different types of networks.

Datasets containing individuals’ information (stored in back-end databases) are often published and shared in social networks. The disclosure of sensitive individuals’ information in social networks is potentially a serious problem. When an attacker studies a published table in a social network, the attacker could infer valuable information of individuals if the attacker learnt some sensitive information of other related individuals from other sources which are different from the published table. This type of attack is referred to as corruption attack. Existing privacy-preserving data publication (PPDP) approaches have been developed against corruption attacks, however, they could cause severe information loss, and reduce the usefulness of the published data. In addition, PPDP models based on ℓ-diversity and its variants may lead to individual sensitive information disclosure. Motivated by providing a solution to overcome these drawbacks, an independent ℓ-diversity principle is proposed in this study. Based on this principle a PPDP model is presented. The model could prevent attacks from attackers who have known data publishing algorithms and have the corruption abilities. A new data utility measurement global loss penalty is also proposed in this study. Related algorithms to our approach have been developed and implemented. Extensive experiments have been performed and comparisons with other related methods have been made. The results have shown the effectiveness of our approaches. It has been noted that when compared with ℓ-diversity model and its variant models, our model could resist corruption attacks more effectively; furthermore, when compared with other solutions against corruption attacks, our method would result in less information loss.

Abstract Private information retrieval (PIR) is a technique for clients to retrieve data from database while hiding the identity of data. Existing PIR schemes are impractical because of the expensive cost for complete privacy. In this paper, we propose a flexible, distributed scheme for PIR. First, a new protocol row‐based computational PIR (Robac‐PIR) is designed to make a trade‐off between privacy and efficiency. Second, we apply Robac‐PIR in a distributed system to further lower the computational cost. We implement the scheme, and the results show it has a good performance and can also provide the trade‐off. Copyright © 2014 John Wiley & Sons, Ltd.

Abstract Many decisions are made in interactive situations in which decision makers interact and may also affect each other's decision outcomes. In order to model decision makings in such interactive situations, this paper incorporates game theory into influence diagrams and presents a new approach, called game theory‐based influence diagrams (GIDs). GIDs consider an extra factor, that of the choice of strategies made by other decision makers, to the list of determinants that influence the decision‐making process of each decision maker, so that decision makers can make more rational decisions. As a result of integrating influence diagrams and game theory, GIDs benefit from the simplicity and efficiency of influence diagrams for modelling complex decision problems as well as the rationality and suitability of applying game theory for making decisions in dynamic interactive scenarios. This paper also introduces genetic algorithm‐based methods to evaluate GIDs. Experimental studies have been performed for validation and evaluation.

A new type of database anomaly called Cumulated Anomaly (CA) is dealt with in this paper. It occurs when submitting the time of authorized transactions or the changed data is cumulated out of some thresholds. A database-level detection method for Cumulated Anomaly is proposed based on statistics and fuzzy set theories. By measuring each database transaction with a real number between zero and one, this method quantitatively monitors how dangerous a transaction is. The real number is termed dubiety degree; therefore the method is named as Dubiety-Determining Method (DDM). After formally presenting the concepts of Cumulated Anomaly and DDM, the algorithm of DDM is given in detail. Software system architecture to support DDM was designed and implemented. Three experiments were performed on it for testing DDM. The first experiment showed the general results of DDM with a set of randomly generated audit records, while the second one simulated a practical case. DDM monitored dubiety degrees for each database transaction and detected expected Cumulated Anomaly in two experiments. The effect on database performance by DDM was tested in the last experiment. Experimental results show that DDM method is feasible and effective.

Data recovery techniques for malicious transactions are increasingly becoming an important issue since the security for DBMSs are mainly prevention based, and they cannot defend systems from unknown attacks. Survivability and availability are essential for modern DBMSs, which require the database provide continuous services in the period of recovery, namely dynamic recovery. In this paper, we presented a data recovery model and introduce extended read-write dependency and phantoms dependency to the model. A fine grained transaction log is proposed for data recovery. The log records all the data items of the read and update-involved operations for the committed transactions, and even extracts data items read by the subqueries in the SQL statements. Based on the log, we develop a dynamic recovery system to implement the data recovery model. The system could provide continuous services while the recovery is processing. Experiments based on TPC-W benchmark show that the dynamic recovery system is high-efficient and reliable.

This work studies the problem of distributed classification in peer-to-peer(P2P) networks. While there has been a significant amount of work in distributed classification, most of existing algorithms are not designed for P2P networks. Indeed, as server-less and router-less systems, P2P networks impose several challenges for distributed classification: (1) it is not practical to have global synchronization in large-scale P2P networks; (2)there are frequent topology changes caused by frequent failure and recovery of peers; and (3) there are frequent on-the-fly data updates on each peer.

Partition entropyis the numerical metric of uncertainty within a partition of a finite set, whileconditional entropymeasures the degree of difficulty in predicting a decision partition when a condition partition is provided. Since two direct methods exist for defining conditional entropy based on its partition entropy, the inequality postulates of monotonicity, which conditional entropy satisfies, are actually additional constraints on its entropy. Thus, in this paper partition entropy is defined as a function of probability distribution, satisfying all the inequalities of not only partition entropy itself but also its conditional counterpart. These inequality postulates formalize the intuitive understandings of uncertainty contained in partitions of finite sets. We study the relationships between these inequalities, and reduce the redundancies among them. According to two different definitions of conditional entropy from its partition entropy, the convenient and unified checking conditions for any partition entropy are presented, respectively. These properties generalize and illuminate the common nature of all partition entropies.

Abstract:The computing‐intensive data mining (DM) process calls for the support of a heterogeneous computing system, which consists of multiple computers with different configurations connected by a high‐speed large‐area network for increased computational power and resources. The DM process can be described as a multi‐phase pipeline process, and in each phase there could be many optional methods. This makes the workflow for DM very complex and it can be modeled only by a directed acyclic graph (DAG). A heterogeneous computing system needs an effective and efficient scheduling framework, which orchestrates all the computing hardware to perform multiple competitive DM workflows. Motivated by the need for a practical solution of the scheduling problem for the DM workflow, this paper proposes a dynamic DAG scheduling algorithm according to the characteristics of an execution time estimation model for DM jobs. Based on an approximate estimation of job execution time, this algorithm first maps DM jobs to machines in adecentralizedanddiligent(defined in this paper) manner. Then the performance of this initial mapping can be improved throughjob migrationswhen necessary. The scheduling heuristic used considers the factors of both theminimal completion timecriterion and thecritical pathin a DAG. We implement this system in an established multi‐agent system environment, in which the reuse of existing DM algorithms is achieved by encapsulating them into agents. The system evaluation and its usage in oil well logging analysis are also discussed.

As Web applications are time vulnerable, the increasing size of XML documents and the complexity of evaluating XML queries pose new performance challenges to existing information retrieval technologies. This paper introduces a new approach for developing a purpose-built XML data management system to improve the system performance of a Web site with XML support by using parallel data processing techniques. To improve the system performance, we propose a parallelisation model for XML data processing, where the data storage strategies, data placement methods and query evaluation techniques have been studied. Other related issues are also presented.