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16th Conference of the International Federation of Classification Societies, IFCS 2019

作者： Tai, Xiao Hui Frisoli, Kayla University of California BerkeleyCA United States Carnegie Mellon University PittsburghPA United States

ISBN: (纸本)9783030601034

Minimax linkage was first introduced by Ao et al. (2004) in 2004, as an alternative to standard linkage methods used in hierarchical clustering. Minimax linkage relies on distances to a prototype for each cluster;this prototype can be thought of as a representative object in the cluster, hence improving the interpretability of clustering results. Bien and Tibshirani analyzed properties of this method in 2011 (Bien and Tibshirani 2011), popularizing the method within the statistics community. Additionally, they performed some comparisons of minimax linkage to standard linkage methods. In an effort to expand upon their work and evaluate minimax linkage more comprehensively, we follow the guidelines for neutral benchmark studies outlined in Van Mechelen et al. (2018), focusing on thorough method evaluation via multiple performance metrics on several well-described data sets. We also make all code and data publicly available through an R package, for full reproducibility. Similarly to Bien and Tibshirani (2011), we find that minimax linkage often produces the smallest distances to prototypes, meaning that objects in a cluster are tightly clustered around their prototype. This is true across a range of values for the total number of clusters (k). However, this is not universally true, and special attention should be paid to the case when k is the true known value. For true k, minimax linkage does not always perform the best in terms of all the evaluation metrics studied, including distance to prototype. © 2021, Springer Nature Switzerland AG.

关键词： clustering algorithms

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Evolutionary Parameter-Free clustering Algorithm 2

Evolutionary Parameter-Free Clustering Algorithm

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2nd IEEE International Conference on Pattern Recognition and Machine Learning, PRML 2021

作者： Ding, Zhiyong Xie, Haibin Li, Peng National University of Defense Technology College of Intelligence Science and Technology Changsha410073 China

ISBN: (纸本)9781665443838

The performance of the clustering algorithms depends mainly on the setting of artificial parameter values which is usually difficult in practical application. In addition, the dataset is usually incremental, and the clustering algorithm applied to the static dataset cannot develop with the change of the dataset. If new sample points are added, algorithm parameters need to be readjusted to cluster again, leading to a great time cost. This paper proposed an evolutionary parameter-free clustering algorithm (EPFC) for the above problems, which imitates the human clustering mechanism of objective things. EPFC algorithm takes the average distance between each sample and its nearest neighbour sample as the threshold value to judge whether the sample can be grouped into one cluster. The threshold value is adaptively updated without setting an artificially parameter value as the samples increase. A large number of experiments on benchmark datasets show that EPFC is effective on datasets with different characteristics, and the algorithm has strong robustness. © 2021 IEEE.

关键词： clustering algorithms

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Power Load Forecasting Model Based on Improved Parallel Fuzzy Kernel clustering Algorithm 10th

Power Load Forecasting Model Based on Improved Parallel Fuzz...

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10th International Conference on Frontier Computing, FC 2020

作者： Su, Huaquan Su, Liwei He, Chaoxun Liang, Yingwei Xie, Hanyang Information Center of Guangdong Power Grid Co. Ltd Guangzhou China Customer Service Center of Guangdong Power Grid Co. Ltd Guangzhou China Shanwei Power Supply Bureau of Guangdong Power Grid Co. Ltd Guangzhou China

ISBN: (纸本)9789811601149

Objective: In order to build a more accurate and effective power load forecasting model, this paper analyzes it. Methods: Based on the improved parallel fuzzy kernel clustering algorithm, this paper expounds the advantages of the algorithm, the calculation process, and the modeling method and verifies the application effect of the algorithm. Results: The improved parallel fuzzy kernel clustering algorithm is successfully obtained through the analysis and is built by using the algorithm. The application of the model in the verification is effective, which shows that the idea of this topic has certain reference value. Conclusion: The improved parallel fuzzy kernel clustering algorithm has more advantages than the traditional clustering algorithm and has good performance in the actual work, which is worth considering. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： clustering algorithms

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Cooperative Beamforming for Wireless Fronthaul and Access Links in Ultra-Dense C-RANs With SWIPT: A First-Order Approach

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IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING 2021年第5期15卷 1242-1257页

作者： Tan, Fangqing Wu, Peiran Wu, Yik-Chung Xia, Minghua Sun Yat Sen Univ Sch Elect & Informat Technol Guangzhou 510006 Peoples R China Guilin Univ Elect Technol Key Lab Cognit Radio & Informat Proc Guilin 541004 Peoples R China Southern Marine Sci & Engn Guangdong Lab Zhuhai 519082 Peoples R China Univ Hong Kong Dept Elect & Elect Engn Hong Kong Peoples R China

This work studies multigroup multicasting transmission in cloud radio access networks (C-RANs) with simultaneous wireless information and power transfer where densely packed remote radio heads (RRHs) cooperatively provide information and energy services for information users (IUs) and energy users (EUs) respectively. To maximize the weighted sum rate (WSR) of information services while satisfying the energy harvesting levels at EUs an optimization of joint beamforming design for the fronthaul and access links is formulated which is however neither smooth nor convex and is indeed NP-hard. To tackle this difficulty the smooth and successive convex approximations are used to transform the original problem into a sequence of convex problems and two first-order algorithms are developed to find the initial feasible point and the nearly optimal solution respectively. Moreover an accelerated algorithm is designed to improve the convergence speed by exploiting both Nesterov and heavy-ball momentums. Numerical results demonstrate that the proposed first-order algorithms achieve almost the same WSR as that of traditional second-order approaches yet with much lower computational complexity and the proposed scheme outperforms state-of-the-art competing schemes in terms of WSR.

关键词： Signal processing algorithms Array signal processing Wireless communication Wireless sensor networks clustering algorithms Heuristic algorithms Energy harvesting Beamforming cloud radio access networks first-order algorithm simultaneous wireless information and power transfer weighted sum rate wireless fronthauling

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Sleep-Scheduling-Based Hierarchical Data Collection Algorithm for Gliders in Underwater Acoustic Sensor Networks

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IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY 2021年第9期70卷 9466-9479页

作者： Han, Guangjie Zhou, Zeren Zhang, Yu Martinez-Garcia, Miguel Peng, Yan Xie, Ling Hohai Univ Dept Informat & Commun Syst Changzhou 213022 Jiangsu Peoples R China Chinese Acad Sci Inst Acoust State Key Lab Acoust Beijing 100190 Peoples R China Loughborough Univ Dept Aeronaut & Automot Engn Loughborough LE11 3TU Leics England Shanghai Univ Sch Artificial Intelligence Shanghai 200000 Peoples R China Dalian Univ Technol Sch Software Dalian 116620 Liaoning Peoples R China

In recent years, underwater acoustic sensor networks (UASNs) have been widely investigated for ocean environmental monitoring, offshore exploration, and marine military. The core function of UASNs is to collect data for related operations. A number of factors make the monitoring challenging;ocean thermoclines may affect the communication of the underwater nodes and gliders, reducing their communication range at varying depth;moreover, the node movement caused by Ekman drifting effect can significantly interfere with the data transmissions. Thus, these factors are regarded essential towards characterizing the ocean environment. To address these challenges, a sleep-scheduling-based hierarchical data collection algorithm (SSHDCA) for underwater gliders is designed. The UASN is split into multiple virtual cubes, where the nodes in different virtual cubes sleep and work alternately to save energy. Then, the SSHDCA divides the network into a dynamic layer and a static layer. In the dynamic layer, a virtual-cube-based multi-hop method is leveraged to transmit data packets to the central area. In the static layer, an improved density-based clustering technique is applied to assign each node to an appropriate cluster, while the underwater gliders collect data from the cluster heads. Further, to reduce energy consumption, the SSHDCA compresses key and non-key data, reducing the size of the packets. Simulation results have shown that the proposed algorithm is effective in reducing the path length of the gliders and the average energy consumption of the nodes, while increasing the remaining operational life of the whole network.

关键词： Data collection Energy consumption clustering algorithms Delays Signal processing algorithms Routing protocols Routing Underwater gliders sleep scheduling intelligent data collection intelligent signal processing artificial intelligence

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Robust Label Rectifying With Consistent Contrastive-Learning for Domain Adaptive Person Re-Identification

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IEEE TRANSACTIONS ON MULTIMEDIA 2021年 24卷 3229-3239页

作者： Song, Xulin Jin, Zhong Nanjing Univ Sci & Technol Sch Comp Sci & Engn Minist Educ Key Lab Intelligent Percept & Syst High Dimens In Nanjing 210094 Peoples R China

Domain adaptive person re-identification (Re-ID) is challenging due to the domain gap between the source and target domains. Existing methods have recently shown great promise by training models with contrastive learning and assigning pseudo labels by clustering, in which a memory bank is utilized to keep features for contrast. However, two main problems lead to sub-optimal generalization ability for existing methods. First, there is no constraint on the updating for memory kept features in existing methods, resulting in inaccurate contrastive learning. Second, the inevitable noisy labels during clustering are usually ignored. To alleviate these problems, we propose a Label Rectifying with Consistent Contrastive-learning (LRCC) framework with two strategies. (1) The consistent contrastive-learning (CC) strategy works with a memory bank which stores the source domain class centroids and all the target domain image features. With the CC strategy, the contrast is conducted across the source and target domains simultaneously. More specifically, we design and maintain consistent clustering during model iteration, thus the classes of memory kept target features are invariable in one epoch. (2) The label rectifying (LR) strategy introduces an auxiliary classifier into the LRCC framework. Thus the pseudo labels are rectified by minimizing the prediction variance between the primary classifier and the auxiliary classifier. To verify the effectiveness of LRCC, we conduct experiments on three public person Re-ID datasets under the domain adaptive setting, DukeMTMC-reID, Market-1501, and MSMT17. The experimental results demonstrate that the proposed LRCC can obtain reliable pseudo labels and achieves state-of-the-art adaptation performance.

关键词： Noise measurement Training Feature extraction Uncertainty Reliability Estimation clustering algorithms Contrastive learning domain adaptation label rectifying person Re-ID

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A Novel Imbalanced Ensemble Learning in Software Defect Predication

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IEEE ACCESS 2021年 9卷 86855-86868页

作者： Zheng, Jianming Wang, Xingqi Wei, Dan Chen, Bin Shao, Yanli Hangzhou Dianzi Univ Sch Comp Sci Key Lab Complex Syst Modeling & Simulat Hangzhou 310018 Peoples R China

With the availability of high-speed Internet and the advent of Internet of Things devices, modern software systems are growing in both size and complexity. Software defect prediction (SDP) guarantees the high quality of such complex systems. However, the characteristics of imbalanced distribution of defect data sets have led to the deviation and loss of accuracy of most software defect prediction methods. This paper presents two novel approaches for learning from imbalanced data sets to produce a higher predictive accuracy over the minority class. These two methods differ in whether the oversampling and the misclassification cost information are utilized during training stage, and they are good at different aspects of imbalanced classification, one for dealing with highly imbalanced data sets and the other for moderately imbalanced data sets. Comparing with other state-of-the-art imbalance learning algorithms on imbalanced datasets, the experimental results show that these two methods have achieved excellent results in terms of G-mean and AUC measures, and more accurately identified the defective modules to reduce the cost of detection system.

关键词： Software Software algorithms Prediction algorithms clustering algorithms Heuristic algorithms Boosting Support vector machines Class imbalance cost-sensitive learning ensemble learning oversampling software defect predication

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Rock Mass Discontinuity Extraction Method Based on Multiresolution Supervoxel Segmentation of Point Cloud

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2021年 14卷 8436-8446页

作者： Sun, Wenxiao Wang, Jian Yang, Yikun Jin, Fengxiang Shandong Univ Sci & Technol Coll Geodesy & Geomat Qingdao 266590 Peoples R China Beijing Normal Univ Coll Global Change & Earth Syst Sci Beijing 100875 Peoples R China Shandong Jianzhu Univ Coll Surveying & Geoinformat Jinan 250101 Peoples R China

Discontinuity sets play an essential and pivotal role in the deformation monitoring and stability analysis of the rock mass, but there are still many challenges for accurately and rapidly extracting discontinuity. In this study, an extraction and characterization method of discontinuity sets based on point cloud supervoxel segmentation was proposed, which consists of four parts: 1) a multiresolution supervoxel segmentation (MRSS) algorithm was developed to classify unstructured point cloud into multiresolution facets and discrete points;2) to extract the individual discontinuity, the single supervoxel that having spatial connectivity, similar planarity, and parallelism was clustered;3) the orientation of individual discontinuity was calculated, respectively, based on the plane fitting parameters;and 4) for comprehensively analyzing the stability of rock mass, the improved K-means clustering algorithm is utilized to constructing the discontinuity sets that having similar orientation information. The novel method has been successfully tested on two practical cases (a rock cut and a side slope point cloud captured by the terrestrial laser scanner). A comparison with existing methods shows that the deviation of the discontinuity orientation for rock cut is less than 1 degrees, and the time efficiency is increased by 2.6 times. In addition, the orientation variation of the seven principle discontinuity in the five temporal side slope point cloud is relatively small, the dip direction and angle are within 2 degrees and 1 degrees, respectively. We can conclude that the proposed method can efficiently obtain the full extent of every individual discontinuity from rock mass surface point cloud and accurately analyze their orientation information.

关键词： Three-dimensional displays Rocks Feature extraction Spatial resolution Data mining clustering algorithms Classification algorithms Discontinuity extraction K-means clustering multiresolution supervoxel segmentation (MRSS) point cloud

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Network lifetime enhancement in wireless sensor networks using energy aware clustering with fuzzy system

Network lifetime enhancement in wireless sensor networks usi...

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2020 AICTE Sponsored National E-Conference on Recent Advances in Smart System Automation, Computing and Communication, RASCC 2020

作者： Kumaran, R. Senthil Suganya, P. Department of Electronics and Communication Engineering Ifet College of Engineering Villupuram India

The lifetime of the network states that the failure time of the initial network nodes or if the building blocks of WSN modules in the network runs out of power. The successive exchange of messages leads to power loss and burdening of sensor nodes. It reduces the network lifetime. This paper presents the energy aware clustering algorithm along with fuzzy to contemplate the residual energy. In cluster based wireless sensor networks, the cluster head gathers all kind of information from each node and has the responsibility of passing of the collected data to the various destination nodes. It diminishes the message overhead, delay, increasing packet delivery ratio and enhancing the lifetime of network. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： clustering algorithms

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Fair-Capacitated clustering 14

Fair-Capacitated Clustering

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14th International Conference on Educational Data Mining, EDM 2023

作者： Le Quy, Tai Roy, Arjun Friege, Gunnar Ntoutsi, Eirini Leibniz University Hannover Hannover Germany Freie Universität Berlin Germany Berlin Germany

ISBN: (纸本)9781733673624

Traditionally, clustering algorithms focus on partitioning the data into groups of similar instances. The similarity objective, however, is not sufficient in applications where a fair-representation of the groups in terms of protected attributes like gender or race, is required for each cluster. Moreover, in many applications, to make the clusters useful for the end-user, a balanced cardinality among the clusters is required. Our motivation comes from the education domain where studies indicate that students might learn better in diverse student groups and of course groups of similar cardinality are more practical e.g., for group assignments. To this end, we introduce the fair-capacitated clustering problem that partitions the data into clusters of similar instances while ensuring cluster fairness and balancing cluster cardinalities. We propose a two-step solution to the problem: i) we rely on fairlets to generate minimal sets that satisfy the fair constraint and ii) we propose two approaches, namely hierarchical clustering and partitioning-based clustering, to obtain the fair-capacitated clustering. Our experiments on three educational datasets show that our approaches deliver well-balanced clusters in terms of both fairness and cardinality while maintaining a good clustering quality. © EDM *** rights reserved.

关键词： clustering algorithms

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