Open Access
A Genetic Spectral Clustering Algorithm
Huiqing Wang,Junjie Chen,Kai Guo +2 more
- 01 Jan 2011
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TL;DR: The suggested algorithm reduces the input dimension of the clustering algorithm using dimension reduction of spectral clustering, and replaces the traditional k-means algorithm by genetic k-Means algorithm, which effectively improves the clustered performance on both artificial data and UCI datasets.
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Abstract: As a novel clustering algorithm, spectral clustering is applied in machine learning extensively. Spectral clustering is built upon spectral graph theory, and has the ability to process the clustering of non-convex sample spaces. Most of the existing spectral clustering algorithms are based on k-means algorithm, and k-means algorithm uses the iterative optimization method to find the optimal solution, which is easy to prematurely converge to the local optimal solution. Combined with the global search ability of genetic algorithm, a genetic spectral clustering algorithm is proposed. Compared with the original spectral clustering and k-means clustering analysis based on genetic algorithm, the suggested algorithm reduces the input dimension of the clustering algorithm using dimension reduction of spectral clustering, and replaces the traditional k-means algorithm by genetic k-means algorithm. The experiments show that the suggested algorithm obtains the stable cluster centers, and effectively improves the clustering performance on both artificial data and UCI datasets, which validate the stability and effectiveness of the suggested algorithm.
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Citations
Robust Similarity Measure for Spectral Clustering Based on Shared Neighbors
Xiucai Ye,Tetsuya Sakurai +1 more
TL;DR: A robust similarity measure based on the shared nearest neighbors in a directed kNN graph is considered, which is able to explore the underlying similarity relationships between data points, and are robust to datasets that are not well separated.
41
A genetic graph-based clustering algorithm
Héctor D. Menéndez,David Camacho +1 more
- 29 Aug 2012
TL;DR: In this paper, the authors used genetic algorithms to select the groups using the same similarity graph built by the Spectral Clustering method, which improved the robustness of the spectral clustering algorithm reducing the dependency of the similarity metric parameters.
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GPU Accelerated Genetic Algorithm with Sequence-based Clustering for Ordered Problems
Ryoma J. Ohira,Md. Saiful Islam +1 more
- 19 Jul 2020
TL;DR: A GPU accelerated island-model genetic algorithm that conducts global search by organising its populations into islands according to the similarity in genotype sequences is introduced with encouraging results demonstrating its robustness and scalability when solving ordered optimisation problems.
Application of Clustering Algorithm CLOPE to the Query Grouping Problem in the Field of Materialized View Maintenance
Kateryna Novokhatska,Oleksii Kungurtsev +1 more
- 25 Mar 2016
TL;DR: The algorithm of categorical data clustering was applied to the query grouping problem on the step of database log analysis and searching candidates for materialization to allow forming groups of queries with similar syntax around the most resource-intensive queries.
Integration of Gene Coexpression Network, GO Enrichment Analysis for Identification Gene Expression Signature of Invasive Bladder Carcinoma
Hanaa H. Gaballah
- 11 Jan 2016
TL;DR: In this paper, the authors integrated co-expression network and GO enrichment analysis for identification of prognostic markers and key genes that contribute to bladder cancer initiation and progression using a DNA microarray dataset (GSE 37317).
4
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Normalized cuts and image segmentation
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A tutorial on spectral clustering
TL;DR: In this article, the authors present the most common spectral clustering algorithms, and derive those algorithms from scratch by several different approaches, and discuss the advantages and disadvantages of these algorithms.
•Proceedings Article
On Spectral Clustering: Analysis and an algorithm
Andrew Y. Ng,Michael I. Jordan,Yair Weiss +2 more
- 03 Jan 2001
TL;DR: A simple spectral clustering algorithm that can be implemented using a few lines of Matlab is presented, and tools from matrix perturbation theory are used to analyze the algorithm, and give conditions under which it can be expected to do well.
•Posted Content
A Tutorial on Spectral Clustering
TL;DR: This tutorial describes different graph Laplacians and their basic properties, present the most common spectral clustering algorithms, and derive those algorithms from scratch by several different approaches.
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