Ranking-based Clustering Research Articles

Fast and Parallel Ranking-based Clustering for Heterogeneous Graphs

The demands for graph data analysis methods are increasing. RankClus is a framework to extract clusters by integrating clustering and ranking on heterogeneous graphs; it enhances the clustering results by alternately updates the results of clustering and ranking for the better understanding of the clusters. However, RankClus is computationally expensive if a graph is large since it needs to iterate both clustering and ranking for all nodes. In this paper, to address this problem, we propose a novel fast RankClus algorithm for heterogeneous graphs. To speed up the entire procedure of RankClus, our proposed algorithm reduces the computational cost of the ranking process in each iteration. Our proposal measures how each node affects the clustering result; if it is not significant, we prune the node. Furthermore, we also present a parallel algorithm by extending our proposed algorithm by fully exploiting a modern manycore CPU. As a result, our extensive evaluations clarified that our fast and parallel algorithms drastically cut off the computation time of the original algorithm RancClus.

An Unsupervised Hyperspectral Band Selection Method Based on Shared Nearest Neighbor and Correlation Analysis

Band selection is an important dimensionality reduction (DR) methodology for hyperspectral images (HSI). In recent years, many ranking-based clustering band selection methods have been developed. However, these methods do not consider the combination of bands in different clusters but only select the desired number of clustering centers based on band ranking to construct the reduced band subset, which may lead to obtaining a set of bands with low redundancy but little information or a set of bands with a large amount of information but high redundancy, thus falling into the local optimal solution set. To solve this problem, an unsupervised hyperspectral band selection method based on shared nearest neighbor and correlation analysis (SNNCA) is proposed in this paper. The proposed SNNCA method considers the interaction of bands in different clusters, and can obtain a set of bands with a large amount of information and low redundancy. First, this method uses the shared nearest neighbor to describe the local density of each band and takes the product of local density and distance factor as the weight to rank each band to select the required number of clustering centers, which ensures low redundancy among the clustering centers. Then, all bands are grouped into several clusters based on the Euclidean distance matrix and the clustering centers. Finally, the correlation among intra-cluster and inter-cluster bands and the information entropy are further analyzed, and the most representative band is selected from each cluster. The experimental results on two HSI datasets demonstrate that the proposed SNNCA method achieves better classification performance than that of other state-of-the-art comparison methods and possesses competitive running time.

Alternative Ranking-Based Clustering and Reliability Index-Based Consensus Reaching Process for Hesitant Fuzzy Large Scale Group Decision Making

Recently, large scale group decision making (LSGDM) problems have become a hotspot. This paper focuses on hesitant fuzzy LSGDM problems where decision makers (DMs) use hesitant fuzzy preference relations (HFPRs) to express their assessment information. HFPRs can represent the fuzziness and hesitancy of DM assessment information well. To improve the efficiency of hesitant fuzzy LSGDM problems, we first propose a reliability index-based consensus reaching process (RI-CRP). By assessing the ordinal consistency of DM's assessment information and measuring the deviation from collective opinion, the DM's opinion reliability index is given. To avoid unreliable information, we propose an unreliable DM management method to be used in the RI-CRP, based on the computation of DM's opinion reliability index. Moreover, an alternative ranking-based clustering (ARC) method with HFPRs is proposed to improve the efficiency of the RI-CRP. The similarity index between two DMs’ opinions is provided to ensure the ARC method can be effectively implemented. Compared with those clustering methods which need to preset several correlated parameters, the presented ARC method is more objective with a different approach based on the alternative ranking. A numerical example shows that the proposed ARC method and the RI-CRP are feasible and effective for hesitant fuzzy LSGDM problems.

A hybrid model of ordinal ranking-based clustering using G+Rank K-Means

K-Means is a clustering technique that maps object features onto multidimensional coordinates and groups them based on location closeness. However, measuring closest distance can be doubtful when ranking representation of ordinal scale objects are not considered. Thus, distribution of objects in a cluster may violate ranking representation. For example, a same-rank object may be grouped into different clusters. To address this issue, an enhanced of K-Means algorithm is proposed to achieve better and meaningful result of ranking-based clustering. It is based on integration of ranking algorithm that sort objects into ranking list which also representing object closeness based on its nearby location. A new additional step in K-Means is proposed in reassigning unaligned K-Means nearest objects using ranking attribute that eventually accelerates the clustering process. AHP ranking algorithm is integrated into K-Means in achieving a ranking-based cluster. This enhancement was evaluated on three ordinal datasets covering 67 Java programs, 92 students’ marks on computer architecture subject and 456 EUFA’s football club coefficient ranking list. The results show that by integrating ranking algorithm in K-Means as proposed in G+Rank K-Means, a rank cluster representation has been successfully achieved. The purity value that represents the correctness against certain group classification has also increased.

A Novel Ranking-Based Clustering Approach for Hyperspectral Band Selection

Through imaging the same spatial area by hyperspectral sensors at different spectral wavelengths simultaneously, the acquired hyperspectral imagery often contains hundreds of band images, which provide the possibility to accurately analyze and identify a ground object. However, due to the difficulty of obtaining sufficient labeled training samples in practice, the high number of spectral bands unavoidably leads to the problem of a “dimensionality disaster” (also called the Hughes phenomenon), and dimensionality reduction should be applied. Concerning band (or feature) selection, conventional methods choose the representative bands by ranking the bands with defined metrics (such as non-Gaussianity) or by formulating the band selection problem as a clustering procedure. Because of the different but complementary advantages of the two kinds of methods, it can be beneficial to use both methods together to accomplish the band selection task. Recently, a fast density-peak-based clustering (FDPC) algorithm has been proposed. Based on the computation of the local density and the intracluster distance of each point, the product of the two factors is sorted in decreasing order, and cluster centers are recognized as points with anomalously large values; hence, the FDPC algorithm can be considered a ranking-based clustering method. In this paper, the FDPC algorithm has been enhanced to make it suitable for hyperspectral band selection. First, the ranking score of each band is computed by weighting the normalized local density and the intracluster distance rather than equally taking them into account. Second, an exponential-based learning rule is employed to adjust the cutoff threshold for a different number of selected bands, where it is fixed in the FDPC. The proposed approach is thus named the enhanced FDPC (E-FDPC). Furthermore, an effective strategy, which is called the isolated-point-stopping criterion, is developed to automatically determine the appropriate number of bands to be selected. That is, the clustering process will be stopped by the emergence of an isolated point (the only point in one cluster). Experimental results on three real hyperspectral data demonstrate that the bands selected by our E-FDPC approach could achieve higher classification accuracy than the FDPC and other state-of-the-art band selection techniques, whereas the isolated-point-stopping criterion is a reasonable way to determine the preferable number of bands to be selected.

Enhancing sentence-level clustering with ranking-based clustering framework for theme-based summarization

Sentence clustering plays a pivotal role in theme-based summarization, which discovers topic themes defined as the clusters of highly related sentences in order to avoid redundancy and cover more diverse information. As the length of sentences is short and the content it contains is limited, the bag-of-words cosine similarity traditionally used for document clustering is no longer reasonably suitable. Special treatment for measuring sentence similarity is necessary. In this paper, we propose a ranking-based clustering framework that utilizes ranking distribution of documents and terms to help generate high quality sentence clusters. The effectiveness of the proposed framework is demonstrated by both the cluster quality analysis and the summarization evaluation conducted on the DUC 2004 and DUC2007 datasets.

Mining Heterogeneous Information Networks: Principles and Methodologies

Real-world physical and abstract data objects are interconnected, forming gigantic, interconnected networks. By structuring these data objects and interactions between these objects into multiple types, such networks become semi-structured heterogeneous information networks. Most real-world applications that handle big data, including interconnected social media and social networks, scientific, engineering, or medical information systems, online e-commerce systems, and most database systems, can be structured into heterogeneous information networks. Therefore, effective analysis of large-scale heterogeneous information networks poses an interesting but critical challenge. In this book, we investigate the principles and methodologies of mining heterogeneous information networks. Departing from many existing network models that view interconnected data as homogeneous graphs or networks, our semi-structured heterogeneous information network model leverages the rich semantics of typed nodes and links in a network and uncovers surprisingly rich knowledge from the network. This semi-structured heterogeneous network modeling leads to a series of new principles and powerful methodologies for mining interconnected data, including: (1) rank-based clustering and classification; (2) meta-path-based similarity search and mining; (3) relation strength-aware mining, and many other potential developments. This book introduces this new research frontier and points out some promising research directions. Table of Contents: Introduction / Ranking-Based Clustering / Classification of Heterogeneous Information Networks / Meta-Path-Based Similarity Search / Meta-Path-Based Relationship Prediction / Relation Strength-Aware Clustering with Incomplete Attributes / User-Guided Clustering via Meta-Path Selection / Research Frontiers