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Low-Rank Riemannian Optimization for graph-based clustering Applications

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2022年第9期44卷 5133-5148页

作者： Douik, Ahmed Hassibi, Babak CALTECH Dept Elect Engn Pasadena CA 91125 USA

With the abundance of data, machine learning applications engaged increased attention in the last decade. An attractive approach to robustify the statistical analysis is to preprocess the data through clustering. This paper develops a low-complexity Riemannian optimization framework for solving optimization problems on the set of positive semidefinite stochastic matrices. The low-complexity feature of the proposed algorithms stems from the factorization of the optimization variable X = YYT and deriving conditions on the number of columns of Y under which the factorization yields a satisfactory solution. The paper further investigates the embedded and quotient geometries of the resulting Riemannian manifolds. In particular, the paper explicitly derives the tangent space, Riemannian gradients and Hessians, and a retraction operator allowing the design of efficient first and second-order optimization methods for the graph-based clustering applications of interest. The numerical results reveal that the resulting algorithms present a clear complexity advantage as compared with state-of-the-art euclidean and Riemannian approaches for graph clustering applications.

关键词： Riemannian manifolds low-rank factorization graph-based clustering convex and non-convex optimization

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A self-adaptive graph-based clustering method with noise identification

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PATTERN ANALYSIS AND APPLICATIONS 2023年第3期26卷 907-916页

作者： Li, Lin Chen, Xiang Song, Chengyun Chongqing Univ Technol Coll Comp Sci & Engn Chongqing 400054 Peoples R China Chongqing Univ Coll Comp Sci Chongqing 400044 Peoples R China

graph-based clustering methods offer competitive performance in dealing with complex and nonlinear data patterns. The outstanding characteristic of such methods is the capability to mine the internal topological structure of a dataset. However, most graph-based clustering algorithms are vulnerable to parameters. In this paper, we propose a self-adaptive graph-based clustering method (SAGC) with noise identification based on directed natural neighbor graph to auto identify the desired number of clusters and simultaneously obtain reliable clustering results without prior knowledge and parameter setting. This method adopts parameter adaptive process to deal with specific data patterns and can identify clusters with diverse shapes and detect noises. We use synthetic and UCI real-world datasets to prove the validity of the innovatory method by comparing it to k-means, DBSCAN, OPTICS, AP, SC, CutPC, and WC algorithms in terms of clustering Accuracy, Adjusted Rand index, Normalized Mutual Information and Fowlkes-Mallows index. The experimental results confirm that the proposed method contributes to the progress of graph-based clustering algorithms.

关键词： Directed natural neighbor graph graph-based clustering Unsupervised learning Nonlinear dataset Noise cutting

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BCD: Decomposing Binary Code Into Components Using graph-based clustering 18

BCD: Decomposing Binary Code Into Components Using Graph-Bas...

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13th ACM Asia Conference on Information, Computer and Communications Security (ASIACCS)

作者： Karande, Vishal Chandra, Swarup Lin, Zhiqiang Caballero, Juan Khan, Latifur Hamlen, Kevin W. Univ Texas Dallas Richardson TX 75083 USA Ohio State Univ Columbus OH 43210 USA IMDEA Software Inst Madrid Spain

ISBN: (纸本)9781450355766

Complex software is built by composing components implementing largely independent blocks of functionality. However, once the sources are compiled into an executable, that modularity is lost. This is unfortunate for code recipients, for whom knowing the components has many potential benefits, such as improved program understanding for reverse -engineering, identifying shared code across different programs, binary code reuse, and authorship attribution. This paper proposes a novel approach for decomposing such source -free program executables into components. Given an executable, our approach first statically builds a decomposition graph, where nodes are functions and edges capture three types of relationships: code locality, data references, and function calls. It then applies a graph-theoretic approach to partition the functions into disjoint components. A prototype implementation, BCD, demonstrates the approach's efficacy: Evaluation of BCD with 25 C++ binary programs to recover the methods belonging to each class achieves high precision and recall scores for these tested programs.

关键词： Binary code decomposition Components graph-based clustering

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A graph-based clustering Algorithm for Anomaly Intrusion Detection

A Graph-based Clustering Algorithm for Anomaly Intrusion Det...

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7th International Conference on Computer Science and Education

作者： Zhou Mingqiang Huang Hui Wang Qian Chongqing Univ Coll Comp Sci Chongqing Peoples R China

ISBN: (纸本)9781467302395

Many researchers have argued that data mining can improve the performance of intrusion detection system. So as one of important techniques of data mining, clustering is an important means for intrusion detection. Due to the disadvantages of traditional clustering methods for intrusion detection, this paper presents a graph-based intrusion detection algorithm by using outlier detection method that based on local deviation coefficient (LDCGB). Compared to other intrusion detection algorithm of clustering, this algorithm is unnecessary to initial cluster number. Meanwhile, it is robust in the outlier's affection and able to detect any shape of cluster rather that the circle one only. Moreover, it still has stable rate of detection on unknown or muted attacks. LDCGB uses graph-based cluster algorithm (GB) to get an initial partition of data set which is depended on parameter of cluster precision rather than initial cluster number. On the other hand, because of this intrusion detection model is based on mixed training dataset, so it must have high label accuracy to guarantee its performance. Therefore, in labeling phrase, the algorithm imposes outlier detection algorithm of local deviation coefficient to label the result of GB algorithm again. This measure is able to improve the labeling accuracy. The detection rate and false positive rate are obtained after the algorithm is tested by the KDDCup99 data set. The experimental result shows that the proposed algorithm could get a satisfactory performance.

关键词： intrusion detection graph-based clustering cluster precision outlier detection labeling accuracy

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Assessment of graph -based clustering Algorithms for Alternative Bidding Zone Configurations 116

Assessment of Graph -Based Clustering Algorithms for Alterna...

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2024 International Annual Conference of the Italian Association of Electrotechnics

作者： Chicco, Gianfranco Colella, Pietro Mazza, Andrea Russo, Angela Ilea, Valentin Quaglia, Federico Limone, Mario Bovo, Cristian Politecn Torino Dipartimento Energia Galileo Ferraris Turin Italy Politecn Milan Dipartimento Energia Milan Italy Terna Rete Italia Spa Dispacciamento & Conduzione Rome Italy Univ Pavia Dipartimento Ingn lndustriale & Ilimnazione Pavia Italy

ISBN: (纸本)9798350378498;9788887237627

This paper focuses on model-based algorithms to be applied for identifying alternative configurations in the context of a bidding zone review process for the European Day Ahead Electricity Market operation that adopts a zonal model to manage the transmission grid congestions. In particular, the paper presents evolved graph-based clustering algorithms designed to define bidding zones formed only by interconnected nodes. Then, a method that evaluates the performance of the resulting alternative bidding zone configurations by calculating specific performance indicators is proposed. The clustering algorithms proposed are compared with other algorithms previously developed by the authors in their application to a large-scale model and extended data set related to the Italian Transmission Network, and relevant results are presented.

关键词： electricity markets bidding zones locational marginal prices model-based graph-based clustering performance indicators

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First Results in Leak Localization in Water Distribution Networks using graph-based clustering and Deep Learning 21st

First Results in Leak Localization in Water Distribution Net...

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21st IFAC World Congress on Automatic Control - Meeting Societal Challenges

作者： Romero, Luis Blesa, Joaquim Puig, Vicenc Cembrano, Gabriela Trapiello, Carlos CSIC UPC Inst Robot & Informat Ind Carrer Llorens Artigas 4-6 Barcelona 08028 Spain Univ Politecn Cataluna Supervis Safety & Automat Control Res Ctr CS2AC Campus TerrassaGaia BldgRambla St Nebridi 22 Barcelona 08222 Spain

This paper presents a methodology for the localization of leaks in water distribution networks (WDNs) by means of the combination of a deep learning (DL) approach and a graph-based clustering technique. A data set for all possible leak locations is generated from pressure measurements and utilized to feed an image encoding process based on the Gramian Angular Field (GAF) technique, hence producing an equivalent data set of images. The pressure measurements are generated through the WDN simulation engine EPANET. To accomplish the training stage, the network is iteratively segmented into clusters using the graph Agglomerative clustering (GAC) method, and a deep learning neural network (DLNN) is trained to correctly indicate the leak location at one of the created clusters. The achieved neural networks tree can be traversed through its different branches depending on each classification result, until the final cluster is reached. Consequently, leaks can be located with a success rate that grows inversely to the size of the clusters. Due to the dependency of the latter on the number of clusters, which can be settled, the presented method is adaptable to the considered network features ( as e.g. dimensions, sensors placement and accuracy) and requisites (as e.g. localization area size). Copyright (C) 2020 The Authors.

关键词： leak localization WDN deep learning graph-based clustering neural networks

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Adaptive-order proximity learning for graph-based clustering

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PATTERN RECOGNITION 2022年 126卷 108550-108550页

作者： Wu, Danyang Chang, Wei Lu, Jitao Nie, Feiping Wang, Rong Li, Xuelong Northwestern Polytech Univ Sch Comp Sci Xian 710072 Shaanxi Peoples R China Northwestern Polytech Univ Sch Artificial Intelligence Opt & Elect iOPEN Xian 710072 Shaanxi Peoples R China Northwestern Polytech Univ Key Lab Intelligent Interact & Applicat Minist Ind & Informat Technol Xian 710072 Shaanxi Peoples R China

Recently, structured proximity matrix learning, which aims to learn a structured proximity matrix with explicit clustering structures from the first-order proximity matrix, has become the mainstream of graph-based clustering. However, the first-order proximity matrix always lacks several must-links compared to the groundtruth in real-world data, which results in a mismatched problem and affects the clustering performance. To alleviate this problem, this work introduces the high-order proximity to structured prox-imity matrix learning, and explores a novel framework named Adaptive-Order Proximity Learning (AOPL) to learn a consensus structured proximity matrix from the proximities of multiple orders. To be specific, AOPL selects the appropriate orders first, then assigns weights to these selected orders adaptively. In this way, a consensus structured proximity matrix is learned from the proximity matrices of appropriate orders. based on AOPL framework, two practical models with different properties are derived, namely AOPL-Root and AOPL-Log. Besides, AOPL and the derived models are regarded as the same optimization problem subjected to some slightly different constraints. An efficient algorithm is proposed to solve them and the corresponding theoretical analyses are provided. Extensive experiments on several real-world datasets demonstrate superb performance of our model. (c) 2022 Elsevier Ltd. All rights reserved.

关键词： graph-based clustering Structured proximity matrix learning High-order proximity Adaptive learning

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CosTaL: an accurate and scalable graph-based clustering algorithm for high-dimensional single-cell data analysis

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BRIEFINGS IN BIOINFORMATICS 2023年第3期24卷 bbad157-bbad157页

作者： Li, Yijia Nguyen, Jonathan Anastasiu, David C. Arriaga, Edgar A. Univ Minnesota Dept Chem 139 Smith HallPleasant St SE Minneapolis MN 55455 USA

With the aim of analyzing large-sized multidimensional single-cell datasets, we are describing a method for Cosine-based Tanimoto similarity-refined graph for community detection using Leiden's algorithm (CosTaL). As a graph-based clustering method, CosTaL transforms the cells with high-dimensional features into a weighted k-nearest-neighbor (kNN) graph. The cells are represented by the vertices of the graph, while an edge between two vertices in the graph represents the close relatedness between the two cells. Specifically, CosTaL builds an exact kNN graph using cosine similarity and uses the Tanimoto coefficient as the refining strategy to re-weight the edges in order to improve the effectiveness of clustering. We demonstrate that CosTaL generally achieves equivalent or higher effectiveness scores on seven benchmark cytometry datasets and six single-cell RNA-sequencing datasets using six different evaluation metrics, compared with other state-of-the-art graph-based clustering methods, including Phenograph, Scanpy and PARC. As indicated by the combined evaluation metrics, Costal has high efficiency with small datasets and acceptable scalability for large datasets, which is beneficial for large-scale analysis.

关键词： clustering Mass Cytometry Flow Cytometry Single-cell RNA sequencing k nearest neighbors graph-based clustering

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First Results in Leak Localization in Water Distribution Networks using graph-based clustering and Deep Learning ⁎

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IFAC-PapersOnLine 2020年第2期53卷 16691-16696页

作者： Luis Romero Joaquim Blesa Vicenç Puig Gabriela Cembrano Carlos Trapiello Institut de Robòtica i Informàtica Industrial (CSIC-UPC). Carrer Llorens Artigas 4-6 08028 Barcelona Supervision Safety and Automatic Control Research Center (CS2AC) of the Universitat Politécnica de Catalunya Campus de Terrassa Gaia Building Rambla Sant Nebridi 22 08222 Terrassa Barcelona

关键词： leak localization WDN deep learning graph-based clustering neural networks

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On the Robustness of graph-based clustering to Random Network Alterations

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MOLECULAR & CELLULAR PROTEOMICS 2021年第1期20卷 100002-100002页

作者： Stacey, R. Greg Skinnider, Michael A. Foster, Leonard J. Univ British Columbia Michael Smith Labs Vancouver BC Canada Univ British Columbia Dept Biochem Vancouver BC Canada

Biological functions emerge from complex and dynamic networks of protein-protein interactions. Because these protein-protein interaction networks, or interactomes, represent pairwise connections within a hierarchically organized system, it is often useful to identify higher-order associations embedded within them, such as multi-member protein complexes. graph-based clustering techniques are widely used to accomplish this goal, and dozens of field-specific and general clustering algorithms exist. However, interactomes can be prone to errors, especially when inferred from high-throughput biochemical assays. Therefore, robustness to network-level noise is an important criterion. Here, we tested the robustness of a range of graph-based clustering algorithms in the presence of noise, including algorithms common across domains and those specific to protein networks. Strikingly, we found that all of the clustering algorithms tested here markedly amplified network-level noise. Randomly rewiring only 1% of network edges yielded more than a 50% change in clustering results. Moreover, we found the impact of network noise on individual clusters was not uniform: some clusters were consistently robust to injected noise, whereas others were not. Therefore we developed the *** R package and Shiny web application to measure the reproducibility of clusters by randomly perturbing the network. We show that clust.-perturb results are predictive of real-world cluster stability: poorly reproducible clusters as identified by *** are significantly less likely to be reclustered across experiments. We conclude that graph-based clustering amplifies noise in protein interaction networks, but quantifying the robustness of a cluster to network noise can separate stable protein complexes from spurious associations.

关键词： Biochemistry Bioinformatics Proteomics Interactome graph-based clustering Robust Noise

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