Community detection in online social networks has been a hot research topic in recent years. Meanwhile, to enjoy more social network services, users nowadays are usually involved in multiple online social networks simultaneously, some of which can share common information and structures. Networks that involve some common users are named as multiple "partially aligned networks". In this paper, we want to detect communities of multiple partially aligned networks simultaneously, which is formally defined as the "Mutual Clustering" problem. The "Mutual Clustering" problem is very challenging as it has two important issues to address: (1) how to preserve the network characteristics in mutual community detection? and (2) how to utilize the information in other aligned networks to refine and disambiguate the community structures of the shared users? To solve these two challenges, a novel community detection method, MCD (Mutual Community Detector), is proposed in this paper. MCD can detect social community structures of users in multiple partially aligned networks at the same time with full considerations of (1) characteristics of each network, and (2) information of the shared users across aligned networks. Extensive experiments conducted on two real-world partially aligned heterogeneous social networks demonstrate that MCD can solve the "Mutual Clustering" problem very well.
Clustering is a very broad research area, which include various types of clustering problems, e.g., consensus clustering @cite_14 @cite_6 , multi-view clustering @cite_25 @cite_28 , multi-relational clustering @cite_38 , co-training based clustering @cite_35 , and dozens of papers have been published on these topics. @cite_14 propose a probabilistic consensus clustering method by using evidence accumulation. propose a bayesian consensus clustering method in @cite_2 . Meanwhile, @cite_25 propose to study the multi-view clustering problem, where the attributes of objects are split into two independent subsets. @cite_33 propose to apply multi-view K-Means clustering methods to big data. @cite_38 propose a user-guided multi-relational clustering method, CrossClus, to performs multi-relational clustering under user's guidance. propose to address the multi-view clustering problem based on a co-training setting in @cite_35 .
@cite_38 @cite_35 @cite_14 @cite_33 @cite_28 @cite_6 @cite_2 @cite_25
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Multi-view clustering, which seeks a partition of the data in multiple views that often provide complementary information to each other, has received considerable attention in recent years. In real life clustering problems, the data in each view may have considerable noise. However, existing clustering methods blindly combine the information from multi-view data with possibly considerable noise, which often degrades their performance. In this paper, we propose a novel Markov chain method for Robust Multi-view Spectral Clustering (RMSC). Our method has a flavor of lowrank and sparse decomposition, where we firstly construct a transition probability matrix from each single view, and then use these matrices to recover a shared low-rank transition probability matrix as a crucial input to the standard Markov chain method for clustering. The optimization problem of RMSC has a low-rank constraint on the transition probability matrix, and simultaneously a probabilistic simplex constraint on each of its rows. To solve this challenging optimization problem, we propose an optimization procedure based on the Augmented Lagrangian Multiplier scheme. Experimental results on various real world datasets show that the proposed method has superior performance over several state-of-the-art methods for multi-view clustering. Co-clustering is a generalization of unsupervised clustering that has recently drawn renewed attention, driven by emerging data mining applications in diverse areas. Whereas clustering groups entire columns of a data matrix, co-clustering groups columns over select rows only, i.e., it simultaneously groups rows and columns. The concept generalizes to data “boxes” and higher-way tensors, for simultaneous grouping along multiple modes. Various co-clustering formulations have been proposed, but no workhorse analogous to K-means has emerged. This paper starts from K-means and shows how co-clustering can be formulated as a constrained multilinear decomposition with sparse latent factors. For three- and higher-way data, uniqueness of the multilinear decomposition implies that, unlike matrix co-clustering, it is possible to unravel a large number of possibly overlapping co-clusters. A basic multi-way co-clustering algorithm is proposed that exploits multilinearity using Lasso-type coordinate updates. Various line search schemes are then introduced to speed up convergence, and suitable modifications are proposed to deal with missing values. The imposition of latent sparsity pays a collateral dividend: it turns out that sequentially extracting one co-cluster at a time is almost optimal, hence the approach scales well for large datasets. The resulting algorithms are benchmarked against the state-of-art in pertinent simulations, and applied to measured data, including the ENRON e-mail corpus. In past decade, more and more data are collected from multiple sources or represented by multiple views, where different views describe distinct perspectives of the data. Although each view could be individually used for finding patterns by clustering, the clustering performance could be more accurate by exploring the rich information among multiple views. Several multi-view clustering methods have been proposed to unsupervised integrate different views of data. However, they are graph based approaches, e.g. based on spectral clustering, such that they cannot handle the large-scale data. How to combine these heterogeneous features for unsupervised large-scale data clustering has become a challenging problem. In this paper, we propose a new robust large-scale multi-view clustering method to integrate heterogeneous representations of largescale data. We evaluate the proposed new methods by six benchmark data sets and compared the performance with several commonly used clustering approaches as well as the baseline multi-view clustering methods. In all experimental results, our proposed methods consistently achieve superiors clustering performances. Multi-view clustering has become a popular clustering technique in recent years due to its ability to analyze data collected from multiple sources or represented by multiple views. In this paper, we propose a novel multi-view clustering approach termed weighted multi-view online competitive clustering (WMLCC). We simultaneously exploit the variable weighting strategy and the online competitive learning in our approach and cast the multi-view clustering problem into an optimization problem. The multi-view clustering result can be obtained by optimizing a new objective function. We conduct Multi-view clustering has become a popular clustering technique in recent years due to its ability to analyze data collected from multiple sources or represented by multiple views. In this paper, we propose a novel multi-view clustering approach termed weighted multi-view online competitive clustering (WMLCC). We simultaneously exploit the variable weighting strategy and the online competitive learning in our approach and cast the multi-view clustering problem into an optimization problem. The multi-view clustering result can be obtained by optimizing a new objective function. We conduct experiments on two real-world multi-view datasets. Experimental results demonstrate the effectiveness and efficiency of our approach.