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binary clustering for Deep Network Trained by Feature Growth

IEEE ACCESS 2021年 9卷 8354-8366页

作者： Yang, Xiaqing Shi, Jun Wang, Chen Zhou, Yuanyuan Zhou, Zenan Chen, Tingjun Wei, Shunjun Zhang, Xiaoling Univ Elect Sci & Technol China Sch Informat & Commun Engn Chengdu 611731 Peoples R China

clustering, a class of unsupervised learning methods, has been extensively studied and applied in machine learning. By designing the training process, we are able to impose the CNN with the ability of clustering. In this paper, we propose a binary clustering framework by implementing the deep network in the clustering process, instead of barely acting as a feature extractor. The proposed training strategy consists of five stages: feature elimination, feature seeding, feature germination, feature growing and feature grafting. Feature elimination works to eliminate the impact of random weights initialization and leave the network unbiased. Feature seeding, feature germination and feature growing altogether realize similarity generation, while feature grafting filters and strengthens the generated similarity. Because of the uncertainty of clustering, we take genres into consideration as evaluation metrics. Only performance analysis is considered while the first and the second samples selected during feature training belong to different genres. Compared with DEC and DEC-DA, our proposed training strategy is shown to achieve the state-of-the-art performance, with clustering accuracy of up to 0.996 for dataset fashion MNIST 4/5 -full and outperforms other methods in terms of stability.

关键词： Training Feature extraction Unsupervised learning Neural networks Task analysis clustering algorithms Standards binary clustering CNN feature growth training

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binary clustering with missing data

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Applied Stochastic Models and Data Analysis 1993年第1期9卷 59-59页

作者： Nadif, M. Govaert, G. 1L.R.I.M. Universit&eacute Metz Ile du Saulcy 57045 Metz France2UTC-URA CNRS 817 Compi&egrave gne France

A clustering method is presented for analysing multivariate binary data with missing values. When not all values are observed, Govaert3 has studied the relations between clustering methods and statistical models. The ... 详细信息

A clustering method is presented for analysing multivariate binary data with missing values. When not all values are observed, Govaert³ has studied the relations between clustering methods and statistical models. The author has shown how the identification of a mixture of Bernoulli distributions with the same parameter for all clusters and for all variables corresponds to a clustering criterion which uses L₁ distance characterizing the MNDBIN method (Marchetti⁸). He first generalized this model by selecting parameters which can depend on variables and finally by selecting parameters which can depend both on variables and on clusters. We use the previous models to derive a clustering method adapted to missing data. This method optimizes a criterion by a standard iterative partitioning algorithm which removes the necessity either to ignore objects or to substitute the missing data. We study several versions of this algorithm and, finally, a brief account is given of the application of this method to some simulated data.

关键词： Mixture Missing data Classification maximum likelihood binary clustering

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binary clustering of Color Images by Fuzzy Co-clustering with Non-Extensive Entropy Regularization 2

Binary Clustering of Color Images by Fuzzy Co-Clustering wit...

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2nd IEEE International Conference on Contemporary Computing and Informatics (IC3I)

作者： Susan, Seba Agarwal, Meetu Agarwal, Seetu Kartikeya, Anand Meena, Ritu Delhi Technol Univ Dept Comp Sci & Engn New Delhi India

ISBN: (纸本)9781509052561

This paper proposes semantically meaningful binary clustering of color images by a novel fuzzy co-clustering algorithm. The clustering objective function incorporates the non-extensive entropy with Gaussian gain for regularization purpose. The chromatic color components in the CIEL*A*B* color space form the feature space for clustering. The result is a very good differentiation of the colors in the scene as belonging to the foreground object and the background, which helps in scene understanding and information gathering. One direct application of our tool is salient or foreground object segmentation. Experimentation on images from a benchmark dataset and comparisons with the state of the art clustering and segmentation methods establish the efficiency of our approach.

关键词： Object Segmentation Color Image Segmentation binary clustering Non-extensive entropy with Gaussian gain Fuzzy clustering with non-extensive entropy regularization Fuzzy co-clustering

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BRB-KMeans: Enhancing binary Data clustering for binary Product Quantization 47

BRB-KMeans: Enhancing Binary Data Clustering for Binary Prod...

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47th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR)

作者： Lee, Suwon Choi, Sang-Min Gyeongsang Natl Univ Jinju South Korea

ISBN: (纸本)9798400704314

In binary Product Quantization (BPQ), where product quantization is applied to binary data, the traditional k-majority method is used for clustering, with centroids determined based on Hamming distance and majority vote for each bit. However, this approach often leads to a degradation in clustering quality, negatively impacting BPQ's performance. To address these challenges, we introduce binary-to-Real-and-Back K-Means (BRB-KMeans), a novel method that initially transforms binary data into real-valued vectors, performs k-means clustering on these vectors, and then converts the generated centroids back into binary data. This innovative approach significantly enhances clustering quality by leveraging the high clustering quality of k-means in the real-valued vector space, thereby facilitating future quantization for binary data. Through extensive experiments, we demonstrate that BRB-KMeans significantly enhances clustering quality and overall BPQ performance, notably outperforming traditional methods.

关键词： binary clustering product quantization binary data binary vector

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Automatic Data clustering Framework Using Nature-Inspired binary Optimization Algorithms

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IEEE ACCESS 2021年 9卷 93703-93722页

作者： Merikhi, Behnaz Soleymani, M. R. Concordia Univ Dept Elect & Comp Engn Montreal PQ H3G 1M8 Canada

Cluster analysis using metaheuristic algorithms has earned increasing popularity over recent years due to the great success of these algorithms in finding high-quality clusters in complex real-world problems. This paper proposes a novel framework for automatic data clustering with the capability of generating clusters with approximately the same maximum distortion using nature-inspired binary optimization algorithms. The inherent problem with clustering using such algorithms is having a huge search space. Therefore, we have also proposed a binary encoding scheme for the particle representation to alleviate this problem. The proposed clustering solution requires no prior knowledge of the number of clusters and proceed with the process based on re-clustering, merging, and modifying the small clusters to compensate for the distortion gap between groups with different sizes. The proposed framework's performance has been evaluated over a wide range of synthetic, real-life, and higher dimensional datasets first by considering four different binary optimization algorithms for the optimizer module. Then, it has also been compared to multiple classical and new clustering solutions and two other automatic clustering techniques in continuous search space in terms of separation and compactness of the clusters by utilizing internal validity measures. The experimental results show the proposed solution is highly efficient in creating well-separated and compact clusters with approximately the same distortion in most datasets. Moreover, the application of the proposed framework to the correlated binary dataset is also reported as a case study. The presence of correlation in a dataset results from the similarity between data points in the same category, such as repeated measurements in remote sensing, crowdsourced multi-view video uploading, and augmented reality. Simplicity, customizability, and flexibility in adding extra conditions to the proposed solution and having a dynamic nu

关键词： clustering algorithms Optimization Partitioning algorithms Distortion Approximation algorithms Particle swarm optimization Heuristic algorithms Automatic data clustering binary search space binary clustering cluster-level constraint distortion dynamic clusters nature-inspired optimization algorithms

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Highly-Economized Multi-view binary Compression for Scalable Image clustering 15th

Highly-Economized Multi-view Binary Compression for Scalable...

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15th European Conference on Computer Vision (ECCV)

作者： Zhang, Zheng Liu, Li Qin, Jie Zhu, Fan Shen, Fumin Xu, Yong Shao, Ling Shen, Heng Tao Harbin Inst Technol Shenzhen Shenzhen Peoples R China Univ Queensland Brisbane Qld Australia Incept Inst Artificial Intelligence Abu Dhabi U Arab Emirates Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland Univ Elect Sci & Technol China Chengdu Peoples R China

ISBN: (纸本)9783030012588;9783030012571

How to economically cluster large-scale multi-view images is a long-standing problem in computer vision. To tackle this challenge, we introduce a novel approach named Highly-economized Scalable Image clustering (HSIC) that radically surpasses conventional image clustering methods via binary compression. We intuitively unify the binary representation learning and efficient binary cluster structure learning into a joint framework. In particular, common binary representations are learned by exploiting both sharable and individual information across multiple views to capture their underlying correlations. Meanwhile, cluster assignment with robust binary centroids is also performed via effective discrete optimization under l(21)-norm constraint. By this means, heavy continuous-valued Euclidean distance computations can be successfully reduced by efficient binary XOR operations during the clustering procedure. To our best knowledge, HSIC is the first binary clustering work specifically designed for scalable multi-view image clustering. Extensive experimental results on four large-scale image datasets show that HSIC consistently outperforms the state-of-the-art approaches, whilst significantly reducing computational time and memory footprint.

关键词： Large-scale image clustering binary code learning binary clustering Multi-view features

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Hybridization of Fuzzy and Hard Semi-Supervised clustering Algorithms Tuned with Ant Lion Optimizer Applied to Higgs Boson Search

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Computer Modeling in Engineering & Sciences 2020年第11期125卷 459-494页

作者： Soukaina Mjahed Khadija Bouzaachane Ahmad Taher Azar Salah El Hadaj Said Raghay Faculty of Sciences and Technology Department of Applied Mathematics and Computer SciencesCadi Ayyad UniversityMarrakech40000Morocco Robotics and Internet-of-Things Lab(RIOTU) Prince Sultan UniversityRiyadh12435Saudi Arabia Faculty of Computers and Artificial Intelligence Benha UniversityBenha13511Egypt

This paper focuses on the unsupervised detection of the Higgs boson particle using the most informative features and variables which characterize the“Higgs machine learning challenge 2014”data *** unsupervised detection goes in this paper analysis through 4 steps:(1)selection of the most informative features from the considered data;(2)definition of the number of clusters based on the elbow *** experimental results showed that the optimal number of clusters that group the considered data in an unsupervised manner corresponds to 2 clusters;(3)proposition of a new approach for hybridization of both hard and fuzzy clustering tuned with Ant Lion Optimization(ALO);(4)comparison with some existing metaheuristic optimizations such as Genetic Algorithm(GA)and Particle Swarm Optimization(PSO).By employing a multi-angle analysis based on the cluster validation indices,the confusion matrix,the efficiencies and purities rates,the average cost variation,the computational time and the Sammon mapping visualization,the results highlight the effectiveness of the improved Gustafson-Kessel algorithm optimized withALO(ALOGK)to validate the proposed *** if the paper gives a complete clustering analysis,its novel contribution concerns only the Steps(1)and(3)considered *** first contribution lies in the method used for Step(1)to select the most informative features and *** used the t-Statistic technique to rank ***,a feature mapping is applied using Self-Organizing Map(SOM)to identify the level of correlation between ***,Particle Swarm Optimization(PSO),a metaheuristic optimization technique,is used to reduce the data set *** second contribution of thiswork concern the third step,where each one of the clustering algorithms as K-means(KM),Global K-means(GlobalKM),Partitioning AroundMedoids(PAM),Fuzzy C-means(FCM),Gustafson-Kessel(GK)and Gath-Geva(GG)is optimized and tuned with ALO.

关键词： Ant lion optimization binary clustering clustering algorithms Higgs boson feature extraction dimensionality reduction elbow criterion genetic algorithm particle swarm optimization

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A comparison of two methods for fitting the INDCLUS model

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JOURNAL OF CLASSIFICATION 2005年第2期22卷 273-285页

作者： Ten Berge, JMF Kiers, HAL Univ Groningen Heijmans Inst Psychol Res NL-9712 TS Groningen Netherlands

Chaturvedi and Carroll have proposed the SINDCLUS method for fitting the INDCLUS model. It is based on splitting the two appearances of the cluster matrix in the least squares fit function and relying on convergence to a solution where both cluster matrices coincide. Kiers has proposed an alternative method which preserves equality of the cluster matrices throughout. This paper shows that the latter method is generally to be preferred. However, because the method has a serious local minimum problem, alternative approaches should be contemplated.

关键词： binary clustering binary least squares fitting proximity matrices

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IhybCNV: An intra-hybrid approach for CNV detection from next-generation sequencing data

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DIGITAL SIGNAL PROCESSING 2022年 121卷 103304-103304页

作者： Xie, Kun Liu, Kang Alvi, Haque A. K. Ji, Wenyue Wang, Shuzhen Chang, Liang Yuan, Xiguo Xidian Univ Sch Comp Sci & Technol Xian 710071 Peoples R China Southwestern Univ Finance & Econ Sch Stat Chengdu 610074 Peoples R China Guilin Univ Elect Technol Sch Comp Sci & Informat Secur Guilin 541004 Peoples R China Xidian Univ Hangzhou Inst Technol Hangzhou 311200 Peoples R China

Copy number variation (CNV) is a common phenomenon in the human genome, accounting for a large proportion of structural variations. Accurate detection of CNVs has been regarded as a regular approach for studying cancer mechanisms and evolution and seeking targeted drugs for cancer diagnosis. Next-generation sequencing (NGS) data has provided an unprecedented opportunity for this task. Currently, a large number of methods have been developed for CNV detection by using NGS data. Each of them has its own viewpoint on the characteristics of CNVs associated with the features of NGS data. Due to the intrinsic complexity of CNVs, new methods with a comprehensive viewpoint on the characteristics of CNVs combined with NGS data are required for the detection of various forms of CNVs. In this paper, we propose an intra-hybrid approach, IhybCNV, for the detection of CNVs from NGS data. The central idea of the IhybCNV is that it regards CNVs as outlier events from various viewpoints on the read depth (RD) profile to be analyzed. More precisely, the IhybCNV integrates five individual detectors to calculate five types of outlier scores for each genome segment and then combines the five types of outlier scores into an anomaly score via locally selective combination in parallel outlier ensembles (LSCP). With the anomaly scores of the genome segments, the IhybCNV determines abnormal segments (CNVs) or normal segments by designing a binary clustering model (BCM). The performance of the IhybCNV is evaluated by carrying out simulation studies and real data applications and is compared with five peer methods. The experimental results demonstrate that the IhybCNV obtains a higher F1-score value than the other methods. Thus, the IhybCNV could be expected as a supplementary to current methods and might become a promising tool for the detection of genome mutations. (C) 2021 Elsevier Inc. All rights reserved.

关键词： Copy number variations Next-generation sequencing Intra-hybrid approach Outlier score binary clustering

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