Joint Learning of Spectral Clustering Structure and Fuzzy Similarity Matrix of Data

2019 ◽  
Vol 27 (1) ◽  
pp. 31-44 ◽  
Author(s):  
Zekang Bian ◽  
Hisao Ishibuchi ◽  
Shitong Wang
Keyword(s):  
Spectral Clustering ◽  
Similarity Matrix ◽  
Joint Learning ◽  
Fuzzy Similarity

Fuzzy Similarity Measure Based Spectral Clustering Framework for Noisy Image Segmentation

2017 ◽  
Vol 25 (04) ◽  
pp. 649-673 ◽  
Author(s):  
Subhanshu Goyal ◽  
Sushil Kumar ◽  
M. A. Zaveri ◽  
A. K. Shukla
Keyword(s):  
Similarity Measure ◽  
Spectral Clustering ◽  
Clustering Algorithm ◽  
Spatial Information ◽  
Clustering Algorithms ◽  
Gaussian Kernel ◽  
Similarity Matrix ◽  
Fuzzy Similarity ◽  
Level Information ◽  
Fuzzy C Means Clustering

In recent times, graph based spectral clustering algorithms have received immense attention in many areas like, data mining, object recognition, image analysis and processing. The commonly used similarity measure in the clustering algorithms is the Gaussian kernel function which uses sensitive scaling parameter and when applied to the segmentation of noise contaminated images leads to unsatisfactory performance because of neglecting the spatial pixel information. The present work introduces a novel framework for spectral clustering which embodied local spatial information and fuzzy based similarity measure to tackle the above mentioned issues. In our approach, firstly we filter the noise components from original image by using the spatial and gray–level information. The similarity matrix is then constructed by employing a similarity measure which takes into account the fuzzy c-partition matrix and vectors of the cluster centers obtained by fuzzy c-means clustering algorithm. In the last step, spectral clustering technique is realized on derived similarity matrix to obtain the desired segmentation result. Experimental results on segmentation of synthetic and Berkeley benchmark images with noise demonstrates the effectiveness and robustness of the proposed method, giving it an edge over the clustering based segmentation method reported in the literature.

Joint Learning of Fuzzy k-Means and Nonnegative Spectral Clustering With Side Information

2019 ◽  
Vol 28 (5) ◽  
pp. 2152-2162 ◽  
Author(s):  
Rui Zhang ◽  
Feiping Nie ◽  
Muhan Guo ◽  
Xian Wei ◽  
Xuelong Li
Keyword(s):  
Spectral Clustering ◽  
Side Information ◽  
Joint Learning

scholarly journals A pareto ensemble based spectral clustering framework

2020 ◽  
Author(s):  
Juanjuan Luo ◽  
Huadong Ma ◽  
Dongqing Zhou
Keyword(s):  
Phase I ◽  
Phase Ii ◽  
Spectral Clustering ◽  
Clustering Algorithms ◽  
Divide And Conquer ◽  
Nonzero Entry ◽  
Similarity Matrix ◽  
Diversity Preservation ◽  
Two Phases ◽  
Matrix Construction

Abstract Similarity matrix has a significant effect on the performance of the spectral clustering, and how to determine the neighborhood in the similarity matrix effectively is one of its main difficulties. In this paper, a “divide and conquer” strategy is proposed to model the similarity matrix construction task by adopting Multiobjective evolutionary algorithm (MOEA). The whole procedure is divided into two phases, phase I aims to determine the nonzero entries of the similarity matrix, and Phase II aims to determine the value of the nonzero entries of the similarity matrix. In phase I, the main contribution is that we model the task as a biobjective dynamic optimization problem, which optimizes the diversity and the similarity at the same time. It makes each individual determine one nonzero entry for each sample, and the encoding length decreases to O(N) in contrast with the non-ensemble multiobjective spectral clustering. In addition, a specific initialization operator and diversity preservation strategy are proposed during this phase. In phase II, three ensemble strategies are designed to determine the value of the nonzero value of the similarity matrix. Furthermore, this Pareto ensemble framework is extended to semi-supervised clustering by transforming the semi-supervised information to constraints. In contrast with the previous multiobjective evolutionary-based spectral clustering algorithms, the proposed Pareto ensemble-based framework makes a balance between time cost and the clustering accuracy, which is demonstrated in the experiments section.

Spectral clustering with fuzzy similarity measure

2011 ◽  
Vol 21 (6) ◽  
pp. 701-709 ◽  
Author(s):  
Feng Zhao ◽  
Hanqiang Liu ◽  
Licheng Jiao
Keyword(s):  
Similarity Measure ◽  
Spectral Clustering ◽  
Fuzzy Similarity

scholarly journals Spectral Clustering in Heterogeneous Information Networks

2019 ◽  
Vol 33 ◽  
pp. 4221-4228 ◽  
Author(s):  
Xiang Li ◽  
Ben Kao ◽  
Zhaochun Ren ◽  
Dawei Yin
Keyword(s):  
Spectral Clustering ◽  
Optimization Problem ◽  
Clustering Algorithms ◽  
Similarity Matrix ◽  
Heterogeneous Information ◽  
Heterogeneous Information Networks ◽  
Clustering Quality ◽  
Different Types ◽  
Meta Path ◽  
Matrix Construction

A heterogeneous information network (HIN) is one whose objects are of different types and links between objects could model different object relations. We study how spectral clustering can be effectively applied to HINs. In particular, we focus on how meta-path relations are used to construct an effective similarity matrix based on which spectral clustering is done. We formulate the similarity matrix construction as an optimization problem and propose the SClump algorithm for solving the problem. We conduct extensive experiments comparing SClump with other state-of-the-art clustering algorithms on HINs. Our results show that SClump outperforms the competitors over a range of datasets w.r.t. different clustering quality measures.

scholarly journals Large-Scale Spectral Clustering Based on Representative Points

2019 ◽  
Vol 2019 ◽  
pp. 1-7
Author(s):  
Libo Yang ◽  
Xuemei Liu ◽  
Feiping Nie ◽  
Mingtang Liu
Keyword(s):  
Computational Complexity ◽  
Spectral Clustering ◽  
Large Scale ◽  
Cluster Structure ◽  
Original Data ◽  
Representative Point ◽  
Similarity Matrix ◽  
Clustering Methods ◽  
Assignment Method ◽  
Representative Points

Spectral clustering (SC) has attracted more and more attention due to its effectiveness in machine learning. However, most traditional spectral clustering methods still face challenges in the successful application of large-scale spectral clustering problems mainly due to their high computational complexity οn3, where n is the number of samples. In order to achieve fast spectral clustering, we propose a novel approach, called representative point-based spectral clustering (RPSC), to efficiently deal with the large-scale spectral clustering problem. The proposed method first generates two-layer representative points successively by BKHK (balanced k-means-based hierarchical k-means). Then it constructs the hierarchical bipartite graph and performs spectral analysis on the graph. Specifically, we construct the similarity matrix using the parameter-free neighbor assignment method, which avoids the need to tune the extra parameters. Furthermore, we perform the coclustering on the final similarity matrix. The coclustering mechanism takes advantage of the cooccurring cluster structure among the representative points and the original data to strengthen the clustering performance. As a result, the computational complexity can be significantly reduced and the clustering accuracy can be improved. Extensive experiments on several large-scale data sets show the effectiveness, efficiency, and stability of the proposed method.

scholarly journals Spectral Clustering Algorithm Based on Improved Gaussian Kernel Function and Beetle Antennae Search with Damping Factor

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Zhe Zhang ◽  
Xiyu Liu ◽  
Lin Wang
Keyword(s):  
Kernel Function ◽  
Spectral Clustering ◽  
Clustering Algorithm ◽  
Gaussian Kernel ◽  
Damping Factor ◽  
Data Sets ◽  
Similarity Matrix ◽  
Scale Parameters ◽  
Gaussian Kernel Function ◽  
Spectral Clustering Algorithm

There are two problems in the traditional spectral clustering algorithm. Firstly, when it uses Gaussian kernel function to construct the similarity matrix, different scale parameters in Gaussian kernel function will lead to different results of the algorithm. Secondly, K-means algorithm is often used in the clustering stage of the spectral clustering algorithm. It needs to initialize the cluster center randomly, which will result in the instability of the results. In this paper, an improved spectral clustering algorithm is proposed to solve these two problems. In constructing a similarity matrix, we proposed an improved Gaussian kernel function, which is based on the distance information of some nearest neighbors and can adaptively select scale parameters. In the clustering stage, beetle antennae search algorithm with damping factor is proposed to complete the clustering to overcome the problem of instability of the clustering results. In the experiment, we use four artificial data sets and seven UCI data sets to verify the performance of our algorithm. In addition, four images in BSDS500 image data sets are segmented in this paper, and the results show that our algorithm is better than other comparison algorithms in image segmentation.

scholarly journals SYSTEMATIC METHOD FOR MONITORING AND EARLY-WARNING OF GARDEN HERITAGE ONTOLOGY USED IN THE SUZHOU CLASSICAL GARDEN HERITAGE

2020 ◽  
Vol 28 (4) ◽  
pp. 157-173
Author(s):  
Jie Zhao ◽  
Rikun Wen ◽  
Wen Mei
Keyword(s):  
Early Warning ◽  
Evaluation Model ◽  
Monitoring Program ◽  
Warning System ◽  
Fuzzy Cluster ◽  
Similarity Matrix ◽  
Systematic Method ◽  
Fuzzy Similarity ◽  
Membership Matrix ◽  
Monitoring And Early Warning

Taking garden heritage ontologies as the object, this paper explores monitoring and early-warning methods of heritage based on fuzzy cluster analysis. A monitoring and early-warning system for garden heritage ontologies is designed and consists of monitoring indexes, a monitoring program, monitoring data collection, application of an early-warning grading evaluation model and conclusion of early-warning grading. Taking the Suzhou classical garden heritage as an example, it can be concluded that the systematic method can integrate various qualitative and quantitative index values and collectively reflect the overall state of garden heritage ontologies as well as match a heritage monitoring ontology with an early warning grade by calculating the data similarity matrix, membership matrix, fuzzy similarity matrix, fuzzy equivalent matrix and cut matrix. Five kinds of heritage ontologies with a total of twenty-seven heritage monitoring indicators are applied in the model and then be matched with MATLAB software to obtain accurate early-warning results. When types of heritage ontology need to be expanded, the heritage is further refined, or the heritage is more comprehensive, this method is applicable.

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