A novel method of spectral clustering in attributed networks by constructing parameter-free affinity matrix

AbstractBiological networks often have complex structure consisting of meaningful clusters of nodes that are integral to understanding biological function. Community detection algorithms to identify the clustering, or community structure, of a network have been well established. These algorithms assume that data used in network construction is observed without error. However, oftentimes intermediary analyses such as regression are performed before constructing biological networks and the associated error is not propagated in community detection. In expression quantitative trait loci (eQTL) networks, one must first map eQTLs via linear regression in order to specify the matrix representation of the network. We study the effects of using estimates from regression models when applying the spectral clustering approach to community detection. We demonstrate the impacts on the affinity matrix and consider adjusted estimates of the affinity matrix for use in spectral clustering. We further provide a recommendation for selection of the tuning parameter in spectral clustering. We evaluate the proposed adjusted method for performing spectral clustering to detect gene clusters in eQTL data from the GTEx project and to assess the stability of communities in biological data.

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Wireless Propagation Multipaths using Spectral Clustering and Three-Constraint Affinity Matrix Spectral Clustering

Baghdad Science Journal ◽

10.21123/bsj.2021.18.2(suppl.).1001 ◽

2021 ◽

Vol 18 (2(Suppl.)) ◽

pp. 1001

Author(s):

Jojo Blanza

Keyword(s):

Spectral Clustering ◽

Channel Model ◽

Channel Modeling ◽

Jaccard Index ◽

Affinity Matrix ◽

Number Of Clusters ◽

Alternative Clustering ◽

Directional Cosine ◽

Indoor Scenarios ◽

The Cost

This study focused on spectral clustering (SC) and three-constraint affinity matrix spectral clustering (3CAM-SC) to determine the number of clusters and the membership of the clusters of the COST 2100 channel model (C2CM) multipath dataset simultaneously. Various multipath clustering approaches solve only the number of clusters without taking into consideration the membership of clusters. The problem of giving only the number of clusters is that there is no assurance that the membership of the multipath clusters is accurate even though the number of clusters is correct. SC and 3CAM-SC aimed to solve this problem by determining the membership of the clusters. The cluster and the cluster count were then computed through the cluster-wise Jaccard index of the membership of the multipaths to their clusters. The multipaths generated by C2CM were transformed using the directional cosine transform (DCT) and the whitening transform (WT). The transformed dataset was clustered using SC and 3CAM-SC. The clustering performance was validated using the Jaccard index by comparing the reference multipath dataset with the calculated multipath clusters. The results show that the effectiveness of SC is similar to the state-of-the-art clustering approaches. However, 3CAM-SC outperforms SC in all channel scenarios. SC can be used in indoor scenarios based on accuracy, while 3CAM-SC is applicable in indoor and semi-urban scenarios. Thus, the clustering approaches can be applied as alternative clustering techniques in the field of channel modeling.

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Spectral Clustering with Local Projection Distance Measurement

Mathematical Problems in Engineering ◽

10.1155/2015/829514 ◽

2015 ◽

Vol 2015 ◽

pp. 1-13 ◽

Cited By ~ 1

Author(s):

Chen Diao ◽

Ai-Hua Zhang ◽

Bin Wang

Keyword(s):

Spatial Structure ◽

Spectral Clustering ◽

High Performance ◽

Distance Measure ◽

Affinity Matrix ◽

Local Projection ◽

Straight Line ◽

Projection Distance ◽

Real World Datasets ◽

Synthetic Datasets

Constructing a rational affinity matrix is crucial for spectral clustering. In this paper, a novel spectral clustering via local projection distance measure (LPDM) is proposed. In this method, the Local-Projection-Neighborhood (LPN) is defined, which is a region between a pair of data, and other data in the LPN are projected onto the straight line among the data pairs. Utilizing the Euclidean distance between projective points, the local spatial structure of data can be well detected to measure the similarity of objects. Then the affinity matrix can be obtained by using a new similarity measurement, which can squeeze or widen the projective distance with the different spatial structure of data. Experimental results show that the LPDM algorithm can obtain desirable results with high performance on synthetic datasets, real-world datasets, and images.

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Automatic Scale Parameters in Affinity Matrix Construction for Improved Spectral Clustering

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001416500233 ◽

2016 ◽

Vol 30 (10) ◽

pp. 1650023 ◽

Cited By ~ 1

Author(s):

S. Mohanavalli ◽

S. M. Jaisakthi ◽

Chandrabose Aravindan

Keyword(s):

Spectral Clustering ◽

Clustering Algorithm ◽

Scale Parameter ◽

Research Work ◽

Error Rates ◽

Scale Factor ◽

Data Sets ◽

Affinity Matrix ◽

Similarity Relationship ◽

Automatic Scale

Spectral clustering partitions data into similar groups in the eigenspace of the affinity matrix. The accuracy of the spectral clustering algorithm is affected by the affine equivariance realized in the translation of distance to similarity relationship. The similarity value computed as a Gaussian of the distance between data objects is sensitive to the scale factor [Formula: see text]. The value of [Formula: see text], a control parameter of drop in affinity value, is generally a fixed constant or determined by manual tuning. In this research work, [Formula: see text] is determined automatically from the distance values i.e. the similarity relationship that exists in the real data space. The affinity value of a data pair is determined as a location estimate of the spread of distance values of the data points with the other points. The scale factor [Formula: see text] corresponding to a data point [Formula: see text] is computed as the trimean of its distance vector and used in fixing the scale to compute the affinity matrix. Our proposed automatic scale parameter for spectral clustering resulted in a robust similarity matrix which is affine equivariant with the distance distribution and also eliminates the overhead of manual tuning to find the best [Formula: see text] value. The performance of spectral clustering using such affinity matrices was analyzed using UCI data sets and image databases. The obtained scores for NMI, ARI, Purity and F-score were observed to be equivalent to those of existing works and better for most of the data sets. The proposed scale factor was used in various state-of-the-art spectral clustering algorithms and it proves to perform well irrespective of the normalization operations applied in the algorithms. A comparison of clustering error rates obtained for various data sets across the algorithms shows that the proposed automatic scale factor is successful in clustering the data sets equivalent to that obtained using manually tuned best [Formula: see text] value. Thus the automatic scale factor proposed in this research work eliminates the need for exhaustive grid search for the best scale parameter that results in best clustering performance.

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Noises Cutting and Natural Neighbors Spectral Clustering Based on Coupling P System

Processes ◽

10.3390/pr9030439 ◽

2021 ◽

Vol 9 (3) ◽

pp. 439

Author(s):

Xiaoling Zhang ◽

Xiyu Liu

Keyword(s):

Spectral Clustering ◽

Critical Density ◽

Synthetic Data ◽

P System ◽

Data Sets ◽

Affinity Matrix ◽

Clustering Method ◽

Data Points ◽

Comparison Algorithms ◽

Searching Method

Clustering analysis, a key step for many data mining problems, can be applied to various fields. However, no matter what kind of clustering method, noise points have always been an important factor affecting the clustering effect. In addition, in spectral clustering, the construction of affinity matrix affects the formation of new samples, which in turn affects the final clustering results. Therefore, this study proposes a noise cutting and natural neighbors spectral clustering method based on coupling P system (NCNNSC-CP) to solve the above problems. The whole algorithm process is carried out in the coupled P system. We propose a natural neighbors searching method without parameters, which can quickly determine the natural neighbors and natural characteristic value of data points. Then, based on it, the critical density and reverse density are obtained, and noise identification and cutting are performed. The affinity matrix constructed using core natural neighbors greatly improve the similarity between data points. Experimental results on nine synthetic data sets and six UCI datasets demonstrate that the proposed algorithm is better than other comparison algorithms.

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