Introduction to spectral clustering

Hamad, Denis; Biela, Philippe

doi:10.1109/ictta.2008.4529994

Cited by 34 publications

(20 citation statements)

References 11 publications

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“…1. Fully connected similarity matrix [12,44]. This similarity construction method takes each sample as the neighbor of the other samples, and a nonzero similarity value is assigned to s i j , resulting a full matrix.…”

Section: Conventional Similarity Matrix Constructionmentioning

confidence: 99%

A pareto ensemble based spectral clustering framework

Luo

Zhou³

2020

Complex Intell. Syst.

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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.

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Section: Conventional Similarity Matrix Constructionmentioning

confidence: 99%

A pareto ensemble based spectral clustering framework

Luo

Zhou³

2020

Complex Intell. Syst.

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“…It retrieves several bands of important patterns in some sense by taking advantage of the all high spectral correlation. Verified by classification accuracy, it was expected that, just using a part of original bands, the accuracy is obtained rationally, whereas computational work is significantly reduced [18,19]. Figure 4a shows the entire research step.…”

Section: Spectrum Similarity Analysismentioning

confidence: 99%

The Analysis on Similarity of Spectrum Analysis of Landslide and Bareland through Hyper-Spectrum Image Bands

Wan

Lei

Lin

et al. 2019

Water

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Landslides of Taiwan occur frequently in high mountain areas. Soil disturbance causes by the earthquake and heavy rainfall of the typhoon seasons often produced the earth and rock to landslide in the upper reaches of the catchment area. Therefore, the landslide near the hillside has an influence on the catchment area. The hyperspectral images are effectively used to monitor the landslide area with the spectral analysis. However, it is rarely studied how to interpret it in the image of the landslide. If there are no elevation data on the slope disaster, it is quite difficult to identify the landslide zone and the bareland area. More specifically, this study used a series of spectrum analysis to identify the difference between them. Therefore, this study conducted a spectrum analysis for the classification of the landslide, bareland, and vegetation area in the mountain area of NanXi District, Tainan City. On the other hand, this study used the following parallel study on Support Vector Machine (SVM) for error matrix and thematic map for comparison. The study simultaneously compared the differences between them. The spectral similarity analysis reaches 85% for testing data, and the SVM approach has 98.3%.

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“…Spectral Clustering is a method of embedding the similarity matrix between data points followed by clustering in low dimensional space. It is one such clustering technique that does not make any assumptions based on the form of clusters, rather views the problem as a case of graph partitioning [16]. It is very successful in cases where the structure of individual clusters is highly non-convex and clustering is to be done on the basis of connectedness rather than compactness.…”

Section: A About Spectral Clusteringmentioning

confidence: 99%

Entailment and Spectral Clustering based Single and Multiple Document Summarization

Gupta¹,

Kaur²,

Bajaj³

et al. 2019

IJISA

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Text connectedness is an important feature for content selection in text summarization methods. Recently, Textual Entailment (TE) has been successfully employed to measure sentence connectedness in order to determine sentence salience in single document text summarization. In literature, Analog Textual Entailment and Spectral Clustering (ATESC) is one such method which has used TE to compute inter-sentence connectedness scores. These scores are used to compute salience of sentences and are further utilized by Spectral Clustering algorithm to create segments of sentences. Finally, the most salient sentences are extracted from the most salient segments for inclusion in the final summary. The method has shown good performance earlier. But the authors observe that TE has never been employed for the task of multi-document summarization. Therefore, this paper has proposed ATESC based new methods for the same task. The experiments conducted on DUC 2003 and 2004 datasets reveal that the notion of Textual Entailment along with Spectral Clustering algorithm proves to be an effective duo for redundancy removal and generating informative summaries in multi-document summarization. Moreover, the proposed methods have exhibited faster execution times.

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Introduction to spectral clustering

Cited by 34 publications

References 11 publications

A pareto ensemble based spectral clustering framework

A pareto ensemble based spectral clustering framework

The Analysis on Similarity of Spectrum Analysis of Landslide and Bareland through Hyper-Spectrum Image Bands

Entailment and Spectral Clustering based Single and Multiple Document Summarization

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