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RNP-Miner: Repetitive Nonoverlapping Sequential Pattern Mining

IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2024年第9期36卷 4874-4889页

作者： Geng, Meng Wu, Youxi Li, Yan Liu, Jing Fournier-Viger, Philippe Zhu, Xingquan Wu, Xindong Hebei Univ Technol Sch Artificial Intelligence Tianjin 300400 Peoples R China Hebei Univ Technol Sch Econ & Management Tianjin 300400 Peoples R China Shenzhen Univ Shenzhen 518060 Peoples R China Florida Atlantic Univ Dept Comp & Elect Engn & Comp Sci Boca Raton FL 33431 USA Zhejiang Lab Res Ctr Knowledge Engn Hangzhou 311121 Peoples R China

Sequential pattern mining (SPM) is an important branch of knowledge discovery that aims to mine frequent sub-sequences (patterns) in a sequential database. Various SPM methods have been investigated, and most of them are classical SPM methods, since these methods only consider whether or not a given pattern occurs within a sequence. Classical SPM can only find the common features of sequences, but it ignores the number of occurrences of the pattern in each sequence, i.e., the degree of interest of specific users. To solve this problem, this paper addresses the issue of repetitive nonoverlapping sequential pattern (RNP) mining and proposes the RNP-Miner algorithm. To reduce the number of candidate patterns, RNP-Miner adopts an itemset pattern join strategy. To improve the efficiency of support calculation, RNP-Miner utilizes the candidate support calculation algorithm based on the position dictionary. To validate the performance of RNP-Miner, 10 competitive algorithms and 20 sequence databases were selected. The experimental results verify that RNP-Miner outperforms the other algorithms, and using RNPs can achieve a better clustering performance than raw data and classical frequent patterns.

关键词： Itemsets Databases Data mining Dictionaries clustering algorithms Behavioral sciences Indexes clustering performance itemset pattern join position dictionary repetitive sequential pattern sequential pattern mining

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Self-Organizing a Latent Hierarchy of Sketch Patterns for Controllable Sketch Synthesis

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024年第10期35卷 14506-14518页

作者： Zang, Sicong Tu, Shikui Xu, Lei Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai 200240 Peoples R China Guangdong Inst Intelligence Sci & Technol Zhuhai 519031 Guangdong Peoples R China

Encoding sketches as Gaussian mixture model (GMM)-distributed latent codes is an effective way to control sketch synthesis. Each Gaussian component represents a specific sketch pattern, and a code randomly sampled from the Gaussian can be decoded to synthesize a sketch with the target pattern. However, existing methods treat the Gaussians as individual clusters, which neglects the relationships between them. For example, the giraffe and horse sketches heading left are related to each other by their face orientation. The relationships between sketch patterns are important messages to reveal cognitive knowledge in sketch data. Thus, it is promising to learn accurate sketch representations by modeling the pattern relationships into a latent structure. In this article, we construct a tree-structured taxonomic hierarchy over the clusters of sketch codes. The clusters with the more specific descriptions of sketch patterns are placed at the lower levels, while the ones with the more general patterns are ranked at the higher levels. The clusters at the same rank relate to each other through the inheritance of features from common ancestors. We propose a hierarchical expectation-maximization (EM)-like algorithm to explicitly learn the hierarchy, jointly with the training of encoder-decoder network. Moreover, the learned latent hierarchy is utilized to regularize sketch codes with structural constraints. Experimental results show that our method significantly improves controllable synthesis performance and obtains effective sketch analogy results.

关键词： Codes Training clustering algorithms Task analysis Learning systems Feature extraction Faces Controllable sketch synthesis hierarchical latent representation latent code self-organization sketch analogy sketch representation learning

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Segmentation of 3D Anatomically Diffused Tissues in Magnetic Resonance Images Through Edge-Preserving Constrained Center-Free Fuzzy C-Means

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2024年第6期32卷 3444-3457页

作者： Guo, Qing Song, Hong Wang, Cong Fan, Jingfan Ai, Danni Gao, Yuanjin Yu, Xiaoling Yang, Jian Beijing Inst Technol Sch Comp Sci &Technol Beijing 100081 Peoples R China Beijing Inst Technol Sch Opt & Photon Beijing 100081 Peoples R China Northwestern Polytech Univ Sch Artificial Intelligence OPt & Elect iOPEN Xian 710072 Peoples R China Northwestern Polytech Univ Shenzhen Res & Dev Inst Shenzhen 51800 Peoples R China Beijing Childrens Hosp Dept Ultrasound Beijing 100081 Peoples R China Chinese Peoples Liberat Army Gen Hosp Dept Intervent Ultrasound Beijing 100081 Peoples R China

Anatomically diffused tissues (ADTs) refer to soft tissues containing many anatomical regions that are spatially dispersed and structurally irregular. In magnetic resonance images, ADTs exhibit blurred morphology and heterogeneous texture, making the accurate extraction of their 3D anatomy challenging. Center-free fuzzy C -means (FCM) can effectively partition nonlinear or nonspherical clusters, providing a promising scheme for ADT segmentation. It solves the uncertainty arising from unreliable center estimation by introducing a similarity criterion. However, the similarity criterion is sensitive to the number of target objects and their adjacent members in the images. Moreover, memberships of the existing algorithms are susceptible to losing real ADT details. To handle these issues, we propose an edge-preserving constrained center-free FCM algorithm for segmenting 3D ADTs in magnetic resonance images. To overcome the sensitivity of the similarity criterion, a novel object-to-cluster similarity measure is first proposed to utilize refined member-to-object adjacency. Specifically, the similarity measure focuses on members in the feature space, which share approximately homogeneous characteristics with each target object. Gradient-domain edge-preserving filtering is then combined with the improved similarity criterion to construct the novel objective function of center-free FCM. With the assistance of the designed image-driven edge-preserving regularization, the gradient information of clusters is constrained, eventually approaching that of ADTs in the guidance image. Experiments are conducted on two public brain datasets and one local intrahepatic vein dataset. The results demonstrate that the proposed algorithm is more effective for ADT segmentation than the state-of-the-art peers, exhibiting superior generalization capability.

关键词： Image segmentation Image edge detection clustering algorithms Three-dimensional displays Veins Linear programming Biological tissues Brain tissue segmentation center-free fuzzy C-means (FCM) edge-preserving filtering intrahepatic vein segmentation magnetic resonance (MR) image

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Online Neural Speaker Diarization With Target Speaker Tracking

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IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 2024年 32卷 5078-5091页

作者： Wang, Weiqing Li, Ming Duke Univ Dept Elect & Comp Engn Durham NC 27708 USA Duke Kunshan Univ Suzhou Municipal Key Lab Multimodal Intelligent Sy Kunshan 215306 Peoples R China

This paper proposes an online target speaker voice activity detection (TS-VAD) system for speaker diarization tasks that does not rely on prior knowledge from clustering-based diarization systems to obtain target speaker embeddings. By adapting conventional TS-VAD for real-time operation, our framework identifies speaker activities using self-generated embeddings, ensuring consistent performance and avoiding permutation inconsistencies during inference. In the inference phase, we employ a front-end model to extract frame-level speaker embeddings for each incoming signal block. Subsequently, we predict each speaker's detection state based on these frame-level embeddings and the previously estimated target speaker embeddings. The target speaker embeddings are then updated by aggregating the frame-level embeddings according to the current block's predictions. Our model predicts results block-by-block and iteratively updates target speaker embeddings until reaching the end of the signal. Experimental results demonstrate that the proposed method outperforms offline clustering-based diarization systems on the DIHARD III and AliMeeting datasets. Additionally, this approach is extended to multi-channel data, achieving comparable performance to state-of-the-art offline diarization systems.

关键词： Voice activity detection clustering algorithms Acoustics Real-time systems Vectors Speech enhancement Training Target tracking Low latency communication Automatic speech recognition Speaker diarization online speaker diarization target speaker voice activity detection

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The Core Cluster-Based Subspace Weighted clustering Ensemble

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WIRELESS COMMUNICATIONS & MOBILE COMPUTING 2022年第1期2022卷

作者： Huang, Xuan Qin, Fang Lin, Lin Univ Elect Sci & Technol China Chengdu Coll Chengdu 611731 Peoples R China Southwest Jiaotong Univ Sch Informat Sci & Technol Chengdu 610031 Peoples R China Dalian Univ Sci & Technol Sch Informat Sci & Technol Dalian 116052 Liaoning Peoples R China Chengdu Aeronaut Polytech Coll Informat Engn Chengdu 610100 Peoples R China

In recent years, the Internet of Things (IoT) technology has developed rapidly and is widely used in various fields. It is of great research significance to uncover underlying patterns and insights from the high-dimensional data of IoT, to excavate valuable information to guide people's production and life. clustering can explore the natural cluster structure of the data, which is conducive to further understanding of the data, and is an essential preprocessing step for data analysis. However, clustering is highly dependent on the data. In order to reduce the complexity of the model, reduce the computational cost, and obtain a more robust clustering solution, we combine subspace clustering and ensemble learning to propose a novel subspace weighted clustering ensemble framework for high-dimensional data. The proposed framework first combines random feature selection and unsupervised feature selection to generate a set of base subspaces. clustering is performed on each base subspace to achieve a set of subspace clustering solutions that generate a set of adaptive core clusters. The size of the core cluster is between the sample and the cluster. In the ensemble process, the core clusters are viewed as the basic unit, and the stability of the cluster is evaluated by measuring the distance between the core cluster pairs, and the similarity between the core clusters and the clusters in the base subspace, and then weighting the subspace clustering solution. Under this framework, we propose four subspace ensemble approaches based on core cluster to improve the accuracy of consensus clustering solutions. Extensive experiments are conducted on multiple real-world high-dimensional datasets, demonstrating that the proposed framework can process high-dimensional data for the IoT, and the proposed subspace clustering ensemble approaches are superior to the state-of-the-art clustering approaches.

关键词： clustering algorithms

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Tensorial Multi-View clustering via Low-Rank Constrained High-Order Graph Learning

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2022年第8期32卷 5307-5318页

作者： Jiang, Guangqi Peng, Jinjia Wang, Huibing Mi, Zetian Fu, Xianping Dalian Maritime Univ Coll Informat Sci & Technol Dalian 116026 Liaoning Peoples R China Hebei Univ Sch Cyber Secur & Comp Baoding 071002 Hebei Peoples R China

Multi-view clustering aims to partition multi-view data into different categories by optimally exploring the consistency and complementary information from multiple sources. However, most existing multi-view clustering algorithms heavily rely on the similarity graphs from respective views and fail to comprehend multiple views holistically. Moreover, due to the noise and redundancy maintained in the original data, the original errors of multiple similarity graphs will continue to accumulate in the process of constructing consistent graphs. These situations always lead to the limitation to effective fuse the essential information from multiple views, which always influences the clustering performance and cries out for reliable solutions. Based on the above considerations, we propose a novel method termed Tensorial Multi-view clustering (TMvC), which learns high-order graph by low-rank tensor constraint to uncover the essential information stored in multiple views. TMvC first learns the Laplacian graphs of all views and stacks them into a tensor which can be viewed as a high-order graph. With the high-order graph, consistency and complementary information from different views can be propagated smoothly across all views. Then, based on low-rank constraint, high-order graph is constrained in the horizontal and vertical directions to better uncover the inter-view and inter-class correlations between multi-view data, which is of vital importance for multi-view clustering. Extensive experiments on document and image datasets demonstrate that TMvC can achieve the state-of-the-art performance for multi-view clustering.

关键词： Tensors Correlation Task analysis Optimization clustering algorithms Redundancy Laplace equations High-order graph learning low-rank constraint multi-view clustering

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Multi-Manifold Optimization for Multi-View Subspace clustering

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2022年第8期33卷 3895-3907页

作者： Khan, Aparajita Maji, Pradipta Indian Stat Inst Machine Intelligence Unit Biomed Imaging & Bioinformat Lab Kolkata 700108 India

The meaningful patterns embedded in high-dimensional multi-view data sets typically tend to have a much more compact representation that often lies close to a low-dimensional manifold. Identification of hidden structures in such data mainly depends on the proper modeling of the geometry of low-dimensional manifolds. In this regard, this article presents a manifold optimization-based integrative clustering algorithm for multi-view data. To identify consensus clusters, the algorithm constructs a joint graph Laplacian that contains denoised cluster information of the individual views. It optimizes a joint clustering objective while reducing the disagreement between the cluster structures conveyed by the joint and individual views. The optimization is performed alternatively over k-means and Stiefel manifolds. The Stiefel manifold helps to model the nonlinearities and differential clusters within the individual views, whereas k-means manifold tries to elucidate the best-fit joint cluster structure of the data. A gradient-based movement is performed separately on the manifold of each view so that individual nonlinearity is preserved while looking for shared cluster information. The convergence of the proposed algorithm is established over the manifold and asymptotic convergence bound is obtained to quantify theoretically how fast the sequence of iterates generated by the algorithm converges to an optimal solution. The integrative clustering on benchmark and multi-omics cancer data sets demonstrates that the proposed algorithm outperforms state-of-the-art multi-view clustering approaches.

关键词： Manifolds clustering algorithms Optimization Cancer Symmetric matrices Laplace equations Data models Integrative clustering manifold optimization multi-view learning spectral clustering Stiefel manifold

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Adaptive Resonance Theory-Based Topological clustering With a Divisive Hierarchical Structure Capable of Continual Learning

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IEEE ACCESS 2022年 10卷 68042-68056页

作者： Masuyama, Naoki Amako, Narito Yamada, Yuna Nojima, Yusuke Ishibuchi, Hisao Osaka Metropolitan Univ Grad Sch Informat Sakai Osaka 5998531 Japan Osaka Prefecture Univ Grad Sch Engn Sakai Osaka 5998531 Japan Southern Univ Sci & Technol Dept Comp Sci & Engn Guangdong Prov Key Lab Brain Inspired Intelligent Shenzhen 518055 Peoples R China

Adaptive Resonance Theory (ART) is considered as an effective approach for realizing continual learning thanks to its ability to handle the plasticity-stability dilemma. In general, however, the clustering performance of ART-based algorithms strongly depends on the specification of a similarity threshold, i.e., a vigilance parameter, which is data-dependent and specified by hand. This paper proposes an ART-based topological clustering algorithm with a mechanism that automatically estimates a similarity threshold from the distribution of data points. In addition, for improving information extraction performance, a divisive hierarchical clustering algorithm capable of continual learning is proposed by introducing a hierarchical structure to the proposed algorithm. Experimental results demonstrate that the proposed algorithm has high clustering performance comparable with recently-proposed state-of-the-art hierarchical clustering algorithms.

关键词： clustering algorithms Kernel Subspace constraints Partitioning algorithms Data mining Big Data Training data Adaptive resonance theory topological clustering hierarchical clustering continual learning

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Viewpoint-Aware Progressive clustering for Unsupervised Vehicle Re-Identification

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2022年第8期23卷 11422-11435页

作者： Zheng, Aihua Sun, Xia Li, Chenglong Tang, Jin Anhui Univ Sch Artificial Intelligence Anhui Prov Key Lab Multimodal Cognit Computat Informat Mat & Intelligent Sensing Lab Anhui Prov Hefei 230601 Peoples R China Anhui Univ Sch Comp Sci & Technol Anhui Prov Key Lab Multimodal Cognit Computat Hefei 230601 Peoples R China

Vehicle re-identification (Re-ID) is an active task due to its importance in large-scale intelligent monitoring in smart cities. Despite the rapid progress in recent years, most existing methods handle vehicle Re-ID task in a supervised manner, which is both time and labor-consuming and limits their application to real-life scenarios. Recently, unsupervised person Re-ID methods achieve impressive performance by exploring domain adaption or clustering-based techniques. However, one cannot directly generalize these methods to vehicle Re-ID since vehicle images present huge appearance variations in different viewpoints. To handle this problem, we propose a novel viewpoint-aware clustering algorithm for unsupervised vehicle Re-ID. In particular, we first divide the entire feature space into different subspaces according to the predicted viewpoints and then perform a progressive clustering to mine the accurate relationship among samples. Comprehensive experiments against the state-of-the-art methods on two multi-viewpoint benchmark datasets VeRi-776 and VeRi-Wild validate the promising performance of the proposed method in both with and without domain adaption scenarios while handling unsupervised vehicle Re-ID.

关键词： Task analysis clustering algorithms Unsupervised learning Cameras Annotations Space vehicles Shape Viewpoint-aware progressive clustering vehicle Re-ID unsupervised learning

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Learning With Multiple Kernels

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IEEE ACCESS 2024年 12卷 56973-56980页

作者： Almahdawi, Mahdi A. Cabrera, Omar De La C. King Abdulaziz Univ Appl Coll Dept GRC Jeddah 21589 Saudi Arabia Kent State Univ Coll Arts & Sci Dept Math Sci Kent OH 44242 USA

Over the last decades, learning methods using kernels have become very popular. The main reason is that real data analysis often requires nonlinear methods to detect the dependencies that allow successful predictions of properties of interest. Gaussian kernels have been used in many studies such as learning algorithms and data analysis. Most of these studies have shown that the parameter chosen for a Gaussian kernel could have a huge impact on the desired results. Therefore, it is essential to understand this impact on a theoretical level. The main contribution of this paper is to study the effect of the Gaussian kernel bandwidth parameter on how well an empirical operator defined from data approximates its continuous counterpart. Some results in spectral approximations are provided as well as some examples.

关键词： Eigenvalues and eigenfunctions Bandwidth clustering algorithms Approximation algorithms Principal component analysis Data analysis Gaussian processes Support vector machines Gaussian kernel radial kernels kernel principal component analysis reproducing kernel support vector machine

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