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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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Graph Contrastive Learning for Tracking Dynamic Communities in Temporal Networks

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2024年第5期8卷 3422-3435页

作者： Ai, Yun Xie, Xianghua Ma, Xiaoke Xidian Univ Sch Comp Sci & Technol Xian 710071 Peoples R China Xidian Univ Key Lab Smart Human Comp Interact & Wearable Tech Xian 710071 Peoples R China Swansea Univ Dept Comp Sci Swansea SA1 8EN W Glam Wales

Temporal networks are ubiquitous because complex systems in nature and society are evolving, and tracking dynamic communities is critical for revealing the mechanism of systems. Moreover, current algorithms utilize temporal smoothness framework to balance clustering accuracy at current time and clustering drift at historical time, which are criticized for failing to characterize the temporality of networks and determine its importance. To overcome these problems, we propose a novel algorithm by joining Non-negative matrix factorization and Contrastive learning for Dynamic Community detection (jNCDC). Specifically, jNCDC learns the features of vertices by projecting successive snapshots into a shared subspace to learn the low-dimensional representation of vertices with matrix factorization. Subsequently, it constructs an evolution graph to explicitly measure relations of vertices by representing vertices at current time with features at historical time, paving a way to characterize the dynamics of networks at the vertex-level. Finally, graph contrastive learning utilizes the roles of vertices to select positive and negative samples to further improve the quality of features. These procedures are seamlessly integrated into an overall objective function, and optimization rules are deduced. To the best of our knowledge, jNCDC is the first graph contrastive learning for dynamic community detection, that provides an alternative for the current temporal smoothness framework. Experimental results demonstrate that jNCDC is superior to the state-of-the-art approaches in terms of accuracy.

关键词： Heuristic algorithms clustering algorithms Self-supervised learning Representation learning Windows Knowledge engineering Visualization Dynamic community graph contrastive learning non-negative matrix factorization self-representation temporal networks

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Impact of Strategic Sampling and Supervision Policies on Semi-Supervised Learning

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE 2024年

作者： Roy, Shuvendu Etemad, Ali Queens Univ Dept ECE Kingston ON K7L 3N6 Canada Queens Univ Ingenu Lab Res Inst Kingston ON K7L 3N6 Canada

In semi-supervised representation learning frameworks, when the number of labelled data is very scarce, the quality and representativeness of these samples become increasingly important. Existing literature on semi-supervised learning randomly sample a limited number of data points for labelling. All these labelled samples are then used along with the unlabelled data throughout the training process. In this work, we ask two important questions in this context: 1) does it matter which samples are selected for labelling? 2) does it matter how the labelled samples are used throughout the training process along with the unlabelled data? To answer the first question, we explore a number of unsupervised methods for selecting specific subsets of data to label (without prior knowledge of their labels), with the goal of maximizing representativeness w.r.t. the unlabelled set. Then, for our second line of inquiry, we define a variety of different label injection strategies in the training process. Extensive experiments on four popular datasets, CIFAR-10, CIFAR-100, SVHN, and STL-10, show that unsupervised selection of samples that are more representative of the entire data improves performance by up to $\sim$2% over the existing semi-supervised frameworks such as MixMatch, ReMixMatch, FixMatch and others with random sample labelling. We show that this boost could even increase to 7.5% for very few-labelled scenarios. However, our study shows that gradually injecting the labels throughout the training procedure does not impact the performance considerably versus when all the existing labels are used throughout the entire training.

关键词： Training Semisupervised learning Labeling Entropy clustering algorithms Unsupervised learning Perturbation methods Minimization Computational intelligence Predictive models Semi-supervised learning clustering representation learning

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Parametric Pipelined k-Means Implementation for Hyperspectral Processing on Spacecraft Embedded FPGA

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2024年 17卷 15927-15941页

作者： Morcillo, Borja Bascones, Daniel Gonzalez, Carlos Mendias, Jose M. Mozos, Daniel Univ Complutense Madrid Dept Comp Architecture & Automat Madrid 28035 Spain

k-means stands out as one of the most common clustering algorithms, widely employed for classification in hyperspectral imaging. In this context, large amounts of data are gathered by sensors that are embedded into satellites with strict constraints in terms of power consumption, weight, physical space or radiation tolerance. Since communication bandwidth is also limited, data processing must be performed on board. However, meeting all those constraints also entails a significant tradeoff with computing performance. The aim of this work is clustering hyperspectral images in real time. Custom hardware has been designed with the objective of reducing overhead and maximizing performance, by exploiting several acceleration techniques. The implementation targets a space-grade Xilinx Kintex FPGA, which features low power consumption and is shielded against radiation. The design has a deep pipelined architecture, able to process all bands of each hyperspectral pixel in parallel. In consequence, it attains a throughput of 100 M hyperspectral pixels per second, even with a discrete use of FPGA resources. In addition, it is also fully parametric, with on-the-fly adaptation to different kinds of images and clustering configurations. Compared to previous implementations, ours takes advantage of a fully RTL design that avoids CPU bottlenecks and HLS design overheads. It also has a fixed throughput regardless of image or clustering properties, while having lower FPGA resource usage than performancewise equivalent implementations.

关键词： Hyperspectral imaging Field programmable gate arrays Hardware clustering algorithms Power demand Performance evaluation Satellites clustering methods multidimensional signal processing reconfigurable architectures satellite applications

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Experimental analysis of clustering based models and proposal of a novel evaluation metric for static video summarization

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MULTIMEDIA TOOLS AND APPLICATIONS 2022年第1期83卷 3259-3284页

作者： Gupta, Deeksha Sharma, Akashdeep KaurnAff, Pavit GuptanAff, Ritika Panjab Univ Mehr Chand Mahajan DAV Coll Women Chandigarh India Panjab Univ Univ Inst Engn & Technol Chandigarh India

Video summarization deals with the identification of relevant and important frames from a video for facilitating efficient storage, browsing and indexing of the videos. Automatic video summarization is a challenging task due to the varied genre, structure and domain of videos. Amongst several video summarization mechanisms, clustering-based approaches have become popular because of their independence from the expensive and time-consuming task of collecting user annotated summaries. The work aims to investigate and compare the behaviour of different nature clustering algorithms and frame descriptors for video summarization tasks. The scope of the presented study is twofold. Firstly, 30 clustering-based video summarization models are implemented and analysed. Secondly, a novel quantitative performance metric, CUS-F (Comparison of User Studies F-Score) is proposed to gauge the quality of generated summaries via a straightforward and concise score value. For comparative evaluation, all experiments are performed on a benchmarking static video summarization dataset - OpenVideo (OV) dataset. The study discovered that DBSCAN clustering shows the best performance when used with local features. The experiments also identifie K-means as a robust clustering method and colour as the most consistent frame descriptor for summarization tasks. In addition, the study demonstrates the effectiveness of the proposed evaluation metric CUS-F in obtaining the assessment of automatic key-frame based summaries by considering both true positive and false positive keyframes.

关键词： Static video summarization Keyframe extraction Video abstraction clustering algorithms Evaluation metric

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Affinity Propagation and Chaotic Lion Swarm Optimization Based clustering for Wireless Sensor Networks

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IEEE ACCESS 2022年 10卷 71545-71556页

作者： Hu Huang-Shui Guo Yu-Xin Wang Chu-Hang Gao Dong Changchun Univ Technol Coll Comp Sci & Engn Changchun 130012 Peoples R China Changchun Normal Univ Coll Comp Sci & Technol Changchun 130026 Peoples R China

By grouping nodes with similar attributes into clusters, the energy efficiency and lifespan of wireless sensor networks(WSNs) can be improved effectively. However, the number of clusters needs to be set in advance, and the optimal cluster heads (CHs) are difficult to determine, which will undoubtedly reduce the network's overall performance. Therefore, a new clustering method using Affinity Propagation and Chaotic Lion swarm Optimization is proposed in this paper to form optimal clusters, which is called APCLO. In APCLO, Affinity Propagation (AP) is used to construct a cluster topology, forming the initial clustering according to the remaining energies of the nodes and the similarity of the distances between nodes. Moreover, the initial CHs are also determined simultaneously by AP. In order to eliminate the outliers from the initial CHs, Chaotic Lion Optimization(CLO) is presented to find the best CHs, in which a fitness function is set according to residual energy and distance to the BS, and chaotic map is used to speed up the convergence of CLO. Simulation results show that the APCLO protocol is superior to the comparison protocols in terms of energy consumption, network throughput, convergence speed, and lifetime. For network lifespan, it increases by 20.1%, 11.2%, and 13.5% respectively.

关键词： clustering algorithms Protocols Optimization Wireless sensor networks Particle swarm optimization Energy consumption clustering methods Wireless sensor networks affinity propagation chaotic lion swarm optimization cluster head selection minimizing energy consumption

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Unsupervised Adaptive Multi-Object Tracking-by-clustering Algorithm With a Bio-Inspired System

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IEEE ACCESS 2022年 10卷 24895-24908页

作者： Guillen-Garcia, Julio Palacios-Alonso, Daniel Cabello, Enrique Conde, Cristina Univ Rey Juan Carlos Escuela Tecn Super Ingn Informat Madrid 28933 Spain

A problem of current interest is how to emulate nature by acquiring information in a neuromorphic-like fashion;namely, by using configurable hardware and electronic systems to emulate the information gathering and processing strategies of biological systems. In this paper, we introduce BioCAMSHIFT, an algorithm for a bio-inspired system that acquires information via a neuromorphic process and uses it to track multiple objects. The system consists of a silicon retina that simulates the behavior of the human eye together with a communication system that uses an Address-Event Representation protocol to transmit information in a way analogous to that of biological neural systems. An unsupervised procedure, based on the CAMSHIFT algorithm, is then used for multi-object tracking. It takes advantage of the retina's high event rate to adapt to the changing sizes of the objects in its field of view. The proposed system has been experimentally validated using a data set from Freeway 210 in Pasadena, California, demonstrating a significantly better improvement in terms of multi-vehicle detection and tracking performance over the current state of the art.

关键词： Retina Silicon Robot sensing systems clustering algorithms Neurons Protocols Neuromorphics BioCAMSHIFT address-event representation (AER) CAMSHIFT bio-inspired system bioinformatics silicon retina devices clustering algorithms neuromorphic system jAER

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Rate-Aware Fuzzy clustering and Stable Sensor Association for Load Balancing in WSNs

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IEEE INTERNET OF THINGS JOURNAL 2022年第5期9卷 3559-3573页

作者： Zhong, Liang Ge, Ming-Feng Zhang, Shizhong Liu, Yong China Univ Geosci Sch Mech Engn & Elect Informat Wuhan 430072 Peoples R China

In wireless sensor networks (WSNs), topology control is of great significance for energy efficiency. While various clustering algorithms have been proposed to handle the topology control of WSNs, the joint optimization of fairness, Quality of Service (QoS), load balancing, and congestion management has received much less attention. Additionally, the constrained resources and processing capacity of sensor nodes complicate the topology control problem. This article proposes a three-layer framework based on joint rate-aware fuzzy clustering and stable sensor association that considers various factors of sensor energy efficiency. First, a rate-aware fuzzy clustering method is proposed for initial clustering, and energy-aware cluster head (CH) selection is applied considering the total energy consumption and the residual energy of the potential CHs to improve the energy efficiency of WSNs. After that, the payoff functions of the CHs and the cluster members are constructed for sensor association with resource capacity limitations and QoS constraints. The optimal sensor association is obtained to maximize the payoffs. Furthermore, a low-complexity suboptimal sensor association approach is proposed to reduce the complexity with a tolerable performance gap. Finally, a congestion factor is introduced to balance the load of CHs, and the Gale-Shapley (GS) algorithm is used to avoid unstable sensor association caused by the processing capacity constraints. The simulation results show that the proposed algorithms can effectively improve the resource utilization and the energy efficiency of the WSNs.

关键词： Wireless sensor networks Resource management clustering algorithms Topology Energy consumption Quality of service Load management Energy efficiency resource utilization resource-constrained networks stable sensor association

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Incremental Density-Based clustering on Multicore Processors

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2022年第3期44卷 1338-1356页

作者： Mai, Son T. Jacobsen, Jon Amer-Yahia, Sihem Spence, Ivor Nhat-Phuong Tran Assent, Ira Quoc Viet Hung Nguyen Queens Univ Belfast Belfast BT7 1NN Antrim North Ireland Queens Univ Belfast Sch Elect Elect Engn & Comp Sci Artificial Intelligence Res Theme Belfast BT7 1NN Antrim North Ireland Univ Grenoble Alpes F-38400 St Martin Dheres France Griffith Univ Brisbane Qld 4222 Australia Aarhus Univ DK-8000 Aarhus Denmark Aarhus Univ Comp Sci DK-8000 Aarhus Denmark

The density-based clustering algorithm is a fundamental data clustering technique with many real-world applications. However, when the database is frequently changed, how to effectively update clustering results rather than reclustering from scratch remains a challenging task. In this work, we introduce IncAnyDBC, a unique parallel incremental data clustering approach to deal with this problem. First, IncAnyDBC can process changes in bulks rather than batches like state-of-the-art methods for reducing update overheads. Second, it keeps an underlying cluster structure called the object node graph during the clustering process and uses it as a basis for incrementally updating clusters wrt. inserted or deleted objects in the database by propagating changes around affected nodes only. In additional, IncAnyDBC actively and iteratively examines the graph and chooses only a small set of most meaningful objects to produce exact clustering results of DBSCAN or to approximate results under arbitrary time constraints. This makes it more efficient than other existing methods. Third, by processing objects in blocks, IncAnyDBC can be efficiently parallelized on multicore CPUs, thus creating a work-efficient method. It runs much faster than existing techniques using one thread while still scaling well with multiple threads. Experiments are conducted on various large real datasets for demonstrating the performance of IncAnyDBC.

关键词： clustering algorithms Multicore processing Databases Instruction sets Electronic mail Time factors clustering methods Density-based clustering anytime clustering incremental clustering active clustering multicore CPUs

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Fault Detection of Electrical Yield Meter Based on Electrical Resistance Tomography

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IEEE ACCESS 2024年 12卷 158103-158109页

作者： Fu, Keyi Yue, Shihong Luo, Donghua Liu, Liping Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China Shandong Commun & Media Coll Jinan 250200 Peoples R China

As an advanced detection technique, the electrical yield meter (EYM) based on electrical resistance tomography has increasingly been applied in various industry fields. But EYM often works under high temperature differences, strong vibrations, high friction, and great corrosions, thus the faults are random and inevitable. How to effectively detect and find EYM faults remains unresolved, while the measurements of EYM failures/faults can provide a way to distinguish the faults themselves in practice. In this paper, an experimental platform is firstly built to simulate and construct the actual EYM working process, and thereby different types of EYM faults are reconstructed to obtain a set of sufficient normal and abnormal measurements. After performing the principal component analysis to capture the key features hidden in measurements, all faults are divided to six clusters by a typical c-means clustering algorithm. Therefore, a fault detection method is created along different measuring conditions and flowing patterns. Experimental evaluation in practical engineering shows that the proposed method can quickly and effectively distinguish fault types through actual measurements. Consequently, the proposed fault detection method is feasible and effective to ensure the relief and stable EYM measurement in practice.

关键词： Electrodes Data acquisition clustering algorithms Feature extraction Meters Electrical yield meter sensor fault detection measurement principal component analysis c-means clustering principal component analysis c-means clustering

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