Dense community detection in multi-valued attributed networks

Advances made to the traditional clustering algorithms solves the various problems such as curse of dimensionality and sparsity of data for multiple attributes. The traditional H-K clustering algorithm can solve the randomness and apriority of the initial centers of K-means clustering algorithm. But when we apply it to high dimensional data it causes the dimensional disaster problem due to high computational complexity. All the advanced clustering algorithms like subspace and ensemble clustering algorithms improve the performance for clustering high dimension dataset from different aspects in different extent. Still these algorithms will improve the performance form a single perspective. The objective of the proposed model is to improve the performance of traditional H-K clustering and overcome the limitations such as high computational complexity and poor accuracy for high dimensional data by combining the three different approaches of clustering algorithm as subspace clustering algorithm and ensemble clustering algorithm with H-K clustering algorithm.

Subspace clustering based on latent low rank representation with Frobenius norm minimization

Subspace clustering is the task of grouping objects based on mutual similarity in subspaces of the full-dimensional space.<br/>The INSCY algorithm extends the well-known density-based clustering algorithm DBSCAN. It finds dimensionality-unbiased non-redundant subspace clusters using a tree structure to speed up the processing of subspaces. Still, finding density-based clusters in all subspaces implies an exponential search space in the number of dimensions. Thus, the running time of INSCY is still measured in hours on even small datasets of 2000 points. For larger datasets, it becomes prohibitively expensive.<br/><br/>To benefit from INSCY for real-world sized datasets, we propose a novel GPU-parallel approach that runs on standard graphics cards. To utilize the many cores of the GPU, we need new algorithmic strategies that fit the computational model of the GPU. While the GPU provides a large number of threads, traditional algorithms incur diverging threads and poor memory alignment, both of which lead to idle time and poor runtime performance. In INSCY, extracting subspace regions from the SCY-tree structure and the density-based clustering of regions itself are thus unfit for the GPU. <br/><br/>Our novel GPU-friendly algorithm GPU-INSCY computes the same subspace clustering as INSCY at dramatically reduced runtimes. <br/>To achieve this, we devise a restructured SCY-tree index-structure and associated operations for the GPU, as well as a GPU-parallel density-based subspace clustering.<br/><br/>We experimentally show that GPU-INSCY scales well with the size of the dataset and the number of dimensions, and improves the running time of INSCY by a factor of several thousand for large datasets of high dimensionality.

Sparse component analysis (SCA) is an approach for linear matrix factorization in an instantaneous mixing system when the number of sensors is fewer than the number of sources. SCA assumes that matrix source (S) contains as many zeros as possible. According to the Georgiev's proof, under some nonstrict conditions on sparsity of the sources, called k-sparse component analysis (k-SCA), we are able to estimate both mixing system (A) and sparse sources (S) uniquely. This paper studies the problem of underdetermind blind identification (UBI) in order to estimate the mixing matrix A based on subspace clustering scheme with k-SCA assumptions. Most k-SCA based algorithms have been designed when there are only at most k = m − 1 active sources in each time instant, where m is the number of sensors. First, we address the issue of multiple active sources i.e. when there are L active sources (0 ≤ L ≤ m − 1). Second, we propose an algorithm for joint or continuous subspace clustering and estimating of the channels in order to design an online scenario to estimate the mixing matrix columns and detect the number of sources as the mixture vectors are received sequentially. Third, our proposed algorithm “subspace selective search” (S3) deals with the outliers within subspace clustering process and improve the accuracy of channel estimation. Numerical simulations are reported to confirm the advantages of our UBI method.

This paper presents a new algorithm for subspace clustering for high dimensional data. It is an iterative algorithm based on the minimization of an objective function. A major weakness of subspace clustering algorithms is that almost all of them are developed based on within- class information only or by employing both within-cluster and between- clusters information. The density of cluster is lost. The new function is developed by integrating the separation and compactness of clusters. The density of cluster is introduced also in the compactness term. The experimental results confirm that the proposed algorithm gives good results on different types of images by optimizing the runtime.

The text data of the social network platforms take the form of short texts, and the massive text data have high-dimensional and sparse characteristics, which does not make the traditional clustering algorithm perform well. In this paper, a new community detection method based on the sparse subspace clustering (SSC) algorithm is proposed to deal with the problem of sparsity and the high-dimensional characteristic of short texts in online social networks. The main ideal is as follows. First, the structured data including users’ attributions and user behavior and unstructured data such as user reviews are used to construct the vector space for the network. And the similarity of the feature words is calculated by the location relation of the feature words in the synonym word forest. Then, the dimensions of data are deduced based on the principal component analysis in order to improve the clustering accuracy. Further, a new community detection method of social network members based on the SSC is proposed. Finally, experiments on several data sets are performed and compared with the K-means clustering algorithm. Experimental results show that proper dimension reduction for high dimensional data can improve the clustering accuracy and efficiency of the SSC approach. The proposed method can achieve suitable community partition effect on online social network data sets.

Fuzzy weighted C-ordered means clustering algorithm

Subspace clustering is an emerging area which explores clusters of objects in various subspaces. The existing subspace clustering algorithms are computationally expensive as they generate a large number of possibly redundant subspace clusters limiting the interpretability of the results. The problem gets even worse with the increase in data's dimensionality. So, this demands for efficient summarisation framework that generates limited number of interesting subspace clusters. A novel algorithm, Similarity Connectedness Based clustering on subspace clusters (SCoC) is proposed to form natural grouping of lower-dimensional subspace clusters. The concept of similarity connectedness is introduced to group and merge the subspace clusters formed in different lower-dimensional subspaces leaping through the lattice of dimensions. The resulted compact and summarised high-dimensional subspace clusters would easily be interpreted for making sound decisions. The SCoC algorithm is thoroughly tested on various benchmark datasets and found that it outperforms PCoC and SUBCLU both in cluster quality and execution time.

Nonlinear subspace clustering by functional link neural networks

With the boom of web and social networking, the amount of generated text data has increased enormously. Much of this data can be considered and modeled as a stream and the volume of such data necessitates the application of automated text classification strategies. Although streaming data classification is not new, considering text data streams for classification purposes has been extensively researched only recently. Before applying any classification method in text data streams, it is imperative that we apply them for existing well-known non-stream text data sets and evaluate their performance. One of the many characteristics of text data that has been pursued for research is its multi-labelity. A single text document may cover multiple class-labels at the same time and hence gives rise to the concept of multi-labelity. From classification perspective, an immediate drawback of such a characteristic is that traditional binary or multi-class classification techniques perform poorly on multi-label text data. In this paper, we extend our previously formulated SISC (Semi-supervised Impurity based Subspace Clustering) [1] approach and its multi-label variation SISC-ML [2]. We call this new algorithm H-SISC (Hierarchical SISC). H-SISC captures the underlying correlation that exists between each pair of class labels in a multi-label environment. Developing a robust multi-label classifier will allow us to apply such a model in classifying streaming text data more effectively. We have experimented with well known text data sets and empirical evaluation on these real world multi-label NASA ASRS (Aviation Safety Reporting System), Reuters and 20 Newsgroups data sets reveals that our proposed approach outperforms other state-of-the-art text classification as well as subspace clustering algorithms.

Most available static data are becoming more and more high-dimensional. Therefore, subspace clustering, which aims at finding clusters not only within the full dimension but also within subgroups of dimensions, has gained a significant importance. Recently, OpenSubspace framework was proposed to evaluate and explorate subspace clustering algorithms in WEKA with a rich body of most state of the art subspace clustering algorithms and measures. Parallel to it, MOA (Massive Online Analysis) framework was developed also above WEKA to provide algorithms and evaluation methods for mining tasks on evolving data streams over the full space only.

The Subspace Clustering Algorithm Based on Differential Evolution (DESC) performs well among the subspace clustering algorithms of global search. But the fixed number of iterations used may cause the algorithm to terminate when it does not converge or lead to overfitting. The improved algorithm proposed in this paper replaces the mutation operators of the DESC and uses the smoothness of the change in the optimal value of the objective function as the basis of judging convergence –– so that the algorithm can adaptively determine the termination timing and solve the above problems to a large extent. The experimental results show clearly that the proposed algorithm can terminate in the appropriate number of iterations in most cases and has certain advantages over the original algorithm in clustering accuracy and running time.

Subspace clustering using a symmetric low-rank representation

While successful in clustering multiple types of high-dimensional data, subspace clustering algorithms do not scale well as the number of data increases. The present paper puts forth a novel randomized subspace clustering algorithm for high-dimensional data based on a random sketching and validation approach. Utilizing a data-driven random sketching technique to estimate the underlying probability density function of the data, the performance of the proposed method is assessed via simulations, and is compared with state-of-the-art sparse subspace clustering methods.

Conventional clustering algorithms are not so efficient on higher dimensional data due to the problem of dimensionality curse. To address this issue searching for clusters in appropriate subspaces is performed. But searching all possible subspaces is exhaustive. In this paper we propose an efficient approach to effectively find the relevant subspaces in high dimensional data and apply clustering in those subspaces. Experiments are conducted on real and synthetic data sets and compared with other approaches and our approach is able to return good clustering results.