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7th IEEE Student Conference on Electric Machines and Systems, SCEMS 2024

作者： Qiu, Shuang Gao, Qingzhong Wang, Yujiao Zhao, Qihang Shenyang Institute of Engineering Graduate Department Liaoning Shenyang China New Energy College Shenyang Institute of Engineering Liaoning Shenyang China

ISBN: (纸本)9798350366945

Identifying high-value customers and providing quality services are essential for the transformation and advancement of power grid enterprises. This paper highlights the strategies necessary for achieving this transformation. Utilizing the load data collected by the measurement system of the power supply company, we analyze the load characteristics of users in typical industries through cluster analysis, a data mining technique. The hierarchical clustering algorithm can effectively extract the local characteristics of the data, determine the power supply mode according to the nature of users' electricity consumption, determine their electricity price strategy according to the types of electricity consumption, and dynamically adjust and implement the optimal service strategy according to the behavior of users. © 2024 IEEE.

关键词： clustering analysis data mining hierarchical clustering algorithm smart grid

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PIFHC: The Probabilistic Intuitionistic Fuzzy hierarchical clustering algorithm

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APPLIED SOFT COMPUTING 2022年第0期120卷

作者： Varshney, Ayush K. Muhuri, Pranab K. Lohani, Q. M. Danish South Asian Univ Dept Comp Sci New Delhi 110021 India

hierarchical clustering techniques help in building a tree-like structure called dendrogram from the data points which can be used to find the closest related data objects. This paper presents a novel hierarchical clustering technique which considers intuitionistic fuzzy sets to deal with the uncertainty present in the data. Instead of using traditional hamming distance or Euclidean distance measure to find the distance between the data points, it employs the probabilistic Euclidean distance measure to propose a novel clustering approach which we term as 'Probabilistic Intuitionistic Fuzzy hierarchical clustering (PIFHC) algorithm'. The proposed PIFHC algorithm considers probabilistic weights from the data to measure the distances between the data points. clustering results over UCI datasets show that our proposed PIFHC algorithm gives better cluster accuracies than its existing counterparts. PIFHC efficiently provides improvements of 1%-3.5% in the clustering accuracy compared to other fuzzy hierarchical clustering algorithms for most of the datasets. We further provide experimental results with the real-world car dataset and the Listeria monocytogenes dataset for mouse susceptibility to demonstrate the practical efficacy of the proposed algorithm. For Listeria datasets as well, proposed PIFHC records 1.7% improvement against the state-of-the-art methods The dendrograms formed by the proposed PIFHC algorithm exhibits high cophenetic correlation coefficient with an improvement of 0.75% over others. We provide various AGNES methods to update the distance between merged clusters in the proposed PIFHC algorithm. (C) 2022 Elsevier B.V. All rights reserved.

关键词： hierarchical clustering Intuitionistic fuzzy sets Fuzzy clustering Probabilistic Euclidean distance measure Probabilistic weights Probabilistic intuitionistic fuzzy hierarchical clustering algorithm

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ON EXPONENTIALLY CONSISTENCY OF LINKAGE-BASED hierarchical clustering algorithm USING KOLMOGROV-SMIRNOV DISTANCE

ON EXPONENTIALLY CONSISTENCY OF LINKAGE-BASED HIERARCHICAL C...

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IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Wang, Tiexing Liu, Yang Chen, Biao Syracuse Univ Dept EECS Syracuse NY 13244 USA

ISBN: (纸本)9781509066315

This paper focuses on performance analysis of linkage-based hierarchical agglomerative clustering algorithms for sequence clustering using the Kolmogrov-Smirnov distance. Data sequences are assumed to be generated from unknown continuous distributions. The goal is to group the data sequences whose underlying generative distributions belong to one cluster without a priori knowledge of both the underlying distributions as well as the number of clusters. Upper bounds on the clustering error probability are derived. The upper bounds help establish the fact that the error probability decays exponentially fast as the sequence length goes to infinity and the obtained error exponent bound has a simple form. Tighter upper bounds on the error probability of single-linkage and complete-linkage algorithms are derived by taking advantage of the simplified metric updating for these two special cases. Simulation results are provided to validate the analysis.

关键词： Kolmogorov-Smirnov distance clustering exponential consistency probability of error hierarchical clustering algorithm

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OPE-HCA: an optimal probabilistic estimation approach for hierarchical clustering algorithm

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NEURAL COMPUTING & APPLICATIONS 2019年第7期31卷 2095-2105页

作者： Fan, Jiancong Shandong Univ Sci & Technol State Key Lab Min Disaster Prevent & Control Cofo Qingdao 266590 Shandong Peoples R China Shandong Univ Sci & Technol Minist Sci & Technol Qingdao 266590 Shandong Peoples R China Shandong Univ Sci & Technol Coll Informat Sci & Engn Qingdao 266590 Shandong Peoples R China Nanjing Univ State Key Lab Novel Software Technol Nanjing 210023 Jiangsu Peoples R China

The Survival of the Fittest is a principle which selects the superior and eliminates the inferior in the nature. This principle has been used in many fields, especially in optimization problem-solving. clustering in data mining community endeavors to discover unknown representations or patterns hidden in datasets. hierarchical clustering algorithm (HCA) is a method of cluster analysis which searches the optimal distribution of clusters by a hierarchical structure. Strategies for hierarchical clustering generally have two types: agglomerative with a bottom-up procedure and divisive with a top-down procedure. However, most of the clustering approaches have two disadvantages: the use of distance-based measurement and the difficulty of the clusters integration. In this paper, we propose an optimal probabilistic estimation (OPE) approach by exploiting the Survival of the Fittest principle. We devise a hierarchical clustering algorithm (HCA) based on OPE, also called OPE-HCA. The OPE-HCA combines optimization with probability and agglomerative HCA. Experimental results show that the OPE-HCA has the ability of searching and discovering patterns at different description levels and can also obtain better performance than many clustering algorithms according to NMI and clustering accuracy measures.

关键词： clustering hierarchical clustering algorithm Data mining Probabilistic estimation

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Component analysis of ancient glass products based on hierarchical analysis clustering algorithm

Component analysis of ancient glass products based on hierar...

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作者： Yusi Feng Hongkai Chen Xin Zheng Taiyuan University of Technology

First, the attachment is pre-processed, and after the abnormal data are removed side by side, the bar chart is drawn to preliminarily analyze the relationship that lead-barium glass is easier to weathering than high potassium glass. Then the chi-square test is carried out to find that whether the weathering of the glass cultural relics surface is related to the glass type of the cultural relics, but there is no obvious relationship with the decoration and color of the cultural relics. Secondly, the statistical model of one-way ANOVA was established, and the difference analysis of various chemical components was conducted before and after the two types of glass weathering. The chemical components that passed the difference test had significant statistical rules. Finally, multiple linear regression equations are constructed to predict the chemical composition content before weathering. The 14 chemical components of glass were regarded as 14 indicators, and the principal component analysis method was used to calculate the principal component contribution rate and the cumulative contribution rate, and then the two principal components were determined. Then the principal component analysis was used to cluster the indicators, and the hierarchical clustering algorithm was used to generate lineage maps. According to the elbow principle, the number of subcategories of high potassium glass is 3, and the number of subcategories of lead-barium glass is 4, so as to divide the chemical composition of glass. The cluster group scatter plot is then plotted and the subclass results are highly reasonable. Then a stepwise regression model is established to analyze the classification sensitivity and obtain several indicators with high sensitivity, which can be used for more targeted protection and restoration of unearthed cultural relics.

关键词： Chi-square Test The Multiple Linear Regression Equation hierarchical clustering algorithm

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A New hierarchical clustering algorithm

A New Hierarchical Clustering Algorithm

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ICIIBMS 2015 Int C Intelligent Inform BioMedical

作者： Nazari, Zahra Kang, Dongshik Asharif, M. Reza Sung, Yulwan Ogawa, Seiji Univ Ryukyus Grad Sch Engn & Sci Nishihara Okinawa Japan Tohoku Fukushi Univ Kansei Fukushi Res Ctr Sendai Miyagi Japan

ISBN: (纸本)9781479985623

The purpose of data clustering algorithm is to form clusters (groups) of data points such that there is high intra-cluster and low inter-cluster similarity. There are different types of clustering methods such as hierarchical, partitioning, grid and density based. hierarchical clustering is a method of cluster analysis which seeks to build a hierarchy of clusters. A hierarchical clustering method can be thought of as a set of ordinary (flat) clustering methods organized in a tree structure. These methods construct the clusters by recursively partitioning the objects in either a top-down or bottom-up fashion. In this paper we present a new hierarchical clustering algorithm using Euclidean distance. To validate this method we have performed some experiments with low dimensional artificial datasets and high dimensional fMRI dataset. Finally the result of our method is compared to some of existing clustering methods.

关键词： clustering algorithm hierarchical clustering algorithm agglomerative hierarchical clustering fMRI data clustering

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An improvement of Chinese text hierarchical clustering algorithm 2nd

An improvement of Chinese text hierarchical clustering algor...

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2nd International Conference on Computer, Intelligent Computing and Education Technology (CICET)

作者： Liu, X. L. Chen, Z. G. Zeng, B. Zhu, Q. X. Chen, T. Univ Elect Sci & Technol China Secure Commun Key Lab Chengdu 610054 Peoples R China

ISBN: (纸本)9781315685892;9781138028005

hierarchical clustering algorithm has low computational efficiency and error accumulation problem in iterative clustering process. To deal with the problems, we propose an improvement of hierarchical clustering algorithm based on GAAC (Group-average Agglomerative clustering), and the improved algorithm is applied to Chinese text clustering. The results of our experimentation show that the improved algorithm have been improved greatly in computational efficiency and the quality of clustering results.

关键词： hierarchical clustering algorithm Text clustering Chinese text

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A New hierarchical clustering algorithm

A New Hierarchical Clustering Algorithm

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International Conference on Intelligent Informatics and Biomedical Sciences

作者： Zahra Nazari Dongshik Kang M. Reza Asharif Yulwan Sung Seiji Ogawa Graduate School of Engineering & Science University of the Ryukyus Kansei Fukushi Research Center Tohoku Fukushi University

ISBN: (纸本)9781479985630

关键词： clustering algorithm hierarchical clustering algorithm Agglomerative hierarchical clustering FMRI data clustering

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Damage identification of steel-ECC composite deck incorporating piezoelectric impedance methodology and hierarchical clustering analysis

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ARCHIVES OF CIVIL AND MECHANICAL ENGINEERING 2024年第3期24卷 187-187页

作者： Sun, Rui Lu, Youfu Liu, Gang Di, Jin Zhang, Zhigang Qin, Fengjiang Chongqing Univ Sch Civil Engn Chongqing 400045 Peoples R China Yunnan Key Lab Water Resources & Hydropower Engn S Kunming 650032 Peoples R China Shandong Hispeed Grp Co Ltd 9999 Jingshi Rd Jinan 250000 Shandong Peoples R China

As a highly ductile concrete, engineered cementitious composites (ECC) can be used as pavement to form a lightweight composite bridge deck system. However, the structural damage introduced by fatigue load in operation might lead to the degradation of structural performance. In this paper, piezoelectric sensors and hierarchical clustering algorithm are used to identify structural damage of steel-ECC composite deck. First, three steel-ECC composite decks were tested under four-point loading, and the electrical impedance signals were measured. The root mean square deviation (RMSD) was extracted to quantify the structural damage severities and locations. Then the frequency interval is divided into nine sub-frequency range to employ the sensitivity analysis. On this basis, a hierarchical clustering algorithm was introduced to analyze the impedance signal to identify the damage of steel-ECC composite deck. The results show that the development of the structural damage can be continuously monitored using impedance methodology and hierarchical clustering algorithm even in the case of small unlabeled datasets.

关键词： Damage identification Steel-ECC composite deck Electromechanical impedance Piezoelectric sensor hierarchical clustering algorithm

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Power allocation method based on modified social network search algorithm

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APPLIED INTELLIGENCE 2024年第24期54卷 12851-12884页

作者： Gao, Hongyuan Li, Huishuang Lin, Yun Ma, Jingya Harbin Engn Univ Coll Informat & Commun Engn Harbin 150001 Peoples R China

With the increase of communication devices and demands, the problems of high power consumption, tight spectrum resources, and low energy efficiency in the two-layer heterogeneous network are the popular topics, which need to be solved urgently. For the purpose of solving these problems in a two-layer heterogeneous network consisting of femtocell base stations in randomly distributed a macrocell base station, which can also be called the Macrocell/Femtocell two-layer heterogeneous network, the hierarchical clustering algorithm is firstly used to cluster femtocell base stations in accordance with a distance threshold, the spectrum partitioning mechanism and non-orthogonal multiple access technique are combined to obtain spectrum allocation schemes for different users. Then, the modified social network search algorithm is used to simulate the power allocation problem in the two-layer heterogeneous network with system energy efficiency as the objective function. By comparing with the previous algorithms, the proposed algorithm's superior performance is verified on the test functions. The results show that the proposed method can effectively improve spectrum utilization and reduce interference. The modified social network search algorithm is more robust and widely applicable regarding energy and computational efficiency.

关键词： Power allocation method Macrocell Femtocell two-layer heterogeneous network hierarchical clustering algorithm Spectrum partitioning mechanism Non-orthogonal multiple access technique Modified social network search algorithm

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