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14th IEEE International Conference on Data Mining (IEEE ICDM)

作者： He, Jinyuan Zhao, Gansen Zhang, Hao Lan Ramamohanarao, Kotagiri Pang, Chaoyi Sun Yat Sen Univ Sch Software Engn Guangzhou Guangdong Peoples R China Zhejiang Univ NIT Ningbo Zhejiang Peoples R China South China Normal Univ Sch Comp Guangzhou Guangdong Peoples R China Zhejiang Univ Ctr SCDM NIT Ningbo Zhejiang Peoples R China Univ Melbourne Melbourne Vic Australia CSIRO Australian E Hlth Res Ctr Canberra ACT Australia

ISBN: (纸本)9781479942749

clustering is an important analysis method commonly used in many areas, including data mining, image processing, statistics, biology, and machine learning. In this paper, we introduce a novel effective clustering method based on Euclidean Distance called Self - Increaseclustering(SIC) for detecting well-separated clusters that can be either convex or non convex sets. Unlike most of the prevalent clustering algorithms, SIC does not require any initial parameters such as the number of clusters produced. Instead, SIC can discover the clusters number automatically based on the distribution of input data and separate these clusters effectively. In each iteration of SIC, a new cluster containing one randomly selected object is created and then this cluster increases by merging itself with the other objects or clusters near-by if certain criterion is satisfied. We evaluate SIC both from theoretical as well as practical points of view, and the experimental results show that SIC works effectively and efficiently on different data sets.

关键词： clustering algorithm Data mining Well-separated Convex Non-convex

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Exploring Energy-Balancing Adaptive clustering algorithm (EBACA) in Wireless Sensor Networks (WSN)

Exploring Energy-Balancing Adaptive Clustering Algorithm (EB...

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International Conference on Information Technology and Computer Application Engineering (ITCAE)

作者： Li, Zhiyu Northwestern Polytech Univ Sch Mech Xian 710072 Peoples R China

ISBN: (纸本)9781315813288;9781138000797

In the node-randomly-distributed clustering algorithms in WSN, it is widely accepted that the excessively centralized cluster nodes can result in unbalanced energy consumption. Section 3 of the full paper explains in some detail the results of our exploration on how to improve network load balancing with EBACA algorithm. The core of section 3 consists of (1) EBACA algorithm selects the cluster head with three parameters: node remaining energy, distance to the Sink node, node density in the cluster;(2) the communication among the cluster heads is optimized, in which selecting the path with shortest sum of Euclidean distance between the cluster heads to transmit data can lead to the least communication energy consumption. Simulation results show that EBACA algorithm, compared with Low Energy Adaptive clustering Hierarchy (LEACH) algorithm, can effectively realize the network energy balancing and prolong the network life.

关键词： Wireless Sensor Networks clustering algorithm Energy Balancing Network Life

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A New Parallelism-Capable clustering algorithm for Wireless Sensor Networks 14

A New Parallelism-Capable Clustering Algorithm for Wireless ...

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14th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid)

作者： Boloorchi, Alireza T. Samadzadeh, M. H. Rahnavard, Nazanin Oklahoma State Univ Dept Comp Sci Stillwater OK 74078 USA Univ Cent Florida Dept Elect Engn & Comp Sci Orlando FL 32816 USA Oklahoma State Univ Elect & Comp Engn Dept Stillwater OK 74078 USA

ISBN: (纸本)9781479927845

In Wireless Sensor Networks (WSN), interactions of sensors to provide service to a specific task is a significant issue. Services provided by WSNs include collecting data from the environment and aggregating them to address queries, or processing the collected data and using the result to adjust environmental parameters such as temperature and moisture. In WSNs, a number of nodes may be simultaneously needed by some application, and these nodes usually need to interact with one another while running in parallel. It is desirable in WSNs that enough resources be assigned to the applications so that a typical application can acquire its needed resources as fast as possible. Distances among sensors assigned to an application typically constitute an important factor in saving communication energy. This paper concerns introducing a clustering algorithm to enhance the efficiency of resource assignment by reducing overall distances among cooperating sensors. In the proposed algorithm, clusters are formed with different sizes to assign just enough sensors to requesting application. Cluster sizes are determined based on an input file to the algorithm that contains the initial number of required clusters of each size. Several increasingly more inclusive versions of the proposed algorithm were studied that are reported in this paper. A performance analysis is provided that shows the proposed clustering algorithm outperforms the Kmeans clustering algorithm from different perspectives.

关键词： Wireless Sensor Networks clustering algorithm Clusterhead Kmeans Parallelism

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Application of corporate image integrating Zigbee technology in brand APP interface design using IoT technology

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023年第5期45卷 8317-8333页

作者： Gu, Xinxin Shandong Management Univ Sch Human Jinan Shandong Peoples R China

In modern social APP interface design, how to effectively improve the corporate image and create the connotation of corporate culture is a significant key problem. With the emergence of APP, a growing number of people use them, increasing communication energy usage and slowing network operation. To improve app compatibility and speed, it is necessary to combine it with the most advanced and dependable technology, such as ZigBee, which is regarded as the best solution for wireless sensor networks. The ZigBee protocol is primarily used to incorporate working and data transmission in wireless sensor networks that are based on ZigBee technology. As a result, incorporating ZigBee technology into APP interface design in the Internet of Things (IoT) domain can significantly improve brand APP interface design's network operation efficiency. This paper presents a novel approach to enhance the performance and corporate image of brand mobile applications (APPs) by integrating ZigBee technology. The primary objective is to improve the operating efficiency and user experience of the brand APPs. The study involves a comparison between 10 brand APPs that have not integrated ZigBee technology and 10 brand APPs that have adopted ZigBee technology. The experimental results indicate that the operating efficiency of the brand APPs incorporating ZigBee technology is 97%, while the efficiency of the brand APPs without ZigBee technology is 85%, resulting in a notable difference of 12%. To assess the effectiveness of ZigBee technology integration, the study conducted experiments with 100 users, randomly assigned to interact with both types of brand APPs. The user feedback and observations revealed that brand APPs integrated with ZigBee technology exhibit significantly higher operating efficiency, contributing to a 12% improvement over their counterparts lacking ZigBee integration. Moreover, 90 out of 100 users reported a preference for the brand APPs integrated with ZigBee technology due t

关键词： APP interface ZigBee technology Internet of Things clustering algorithm LEACH algorithm Internet

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Using Hybrid Wavelet Approach and Neural Network algorithm to Forecast Distribution Feeders

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JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2023年第3期18卷 1587-1600页

作者： Bagheri, Mehdi Zadehbagheri, Mahmoud Kiani, Mohammad Javad Zamani, Iman Nejatian, Samad Islamic Azad Univ Dept Elect Engn Yasuj Branch Yasuj Iran Shahed Univ Elect & Elect Engn Dept Tehran Iran

In this paper, using an algorithm based on the combination of data based on neural network virology and bacterial nutrition algorithm, improves the performance of the neural network prediction method. Also, the selection of two types of downstream and upstream filters in the wavelet transformation increases the predictive efficacy of neurological prediction. Based on the results, the optimized clustered neural network method has a more favorable response than the other methods. By selecting the appropriate filter and multichannel processing method, the maximum error percentage has improved by 15%. However, compared to the neural network prediction method, the proposed method has more computational volume due to the use of wavelet transform and also three times the use of neural prediction. Due to the large number of layers and used neurons, the neural network method has a much higher computational volume than the linear prediction method, where the linear prediction method has a higher error than the proposed method depending on the data used for training.

关键词： Forecasting Neural network clustering algorithm Wavelet transforms Power distribution

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A hybrid data-mining framework for train rescheduling strategy pattern discovery

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Transportation Safety and Environment 2024年第1期6卷 56-67页

作者： Ruirui Chen Xuekai Ge Ping Huang Chao Wen School of Transportation&Logistics Southwest Jiaotong UniversityChengdu 611756China Department of IT Operational Technology CN RailWaterloo N2V2R1Canada

This study presents a hybrid data-mining framework based on feature selection algorithms and clustering methods to perform the pattern discovery of high-speed railway train rescheduling strategies(RSs).The proposed model is composed of two *** the first state,decision tree,random forest,gradient boosting decision tree(GBDT)and extreme gradient boosting(XGBoost)models are used to investigate the importance of *** features that have a high influence on RSs are first *** the second state,a K-means clustering method is used to uncover the interdependences between RSs and the influencing features,based on the results in the first *** proposed method can determine the quantitative relationships between RSs and influencing *** results clearly show the influences of the factors on RSs,the possibilities of different train operation RSs under different situations,as well as some key time periods and key trains that the controllers should pay more attention *** research in this paper can help train traffic controllers better understand the train operation patterns and provides direction for optimizing rail traffic RSs.

关键词： high-speed railway rescheduling strategy pattern discovery feature identification clustering algorithm

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An intelligent routing algorithm for energy prediction of 6G-powered wireless sensor networks

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ALEXANDRIA ENGINEERING JOURNAL 2023年 76卷 35-49页

作者： Zhang, Tantong Henan Univ Anim Husb & Econ Sch Software Dept Informat Engn Zhengzhou 450053 Peoples R China

With the rapid development of the Internet and computers, large-scale network applications have become a research hotspot in the communication industry, engineering and other fields. Building a new wireless sensor network and intelligent communication transmission system can promote the further upgrading and improvement of the capability industry, which is also a subject that the current network academia and communication academia are trying to explore. Based on the above background environment, this paper proposes the research on energy prediction and intelligent routing algorithm of wireless sensor networks in the development of sixth generation (6G) networks. A node coverage and scheduling algorithm based on energy prediction is proposed for the overlap and non-uniformity of regions in network transmission. Secondly, the problem of energy efficient utilization in wireless sensor networks is solved, and the clustering algorithm is used to predict the use process. The working state model of network sensor is established, and the energy parameters are added to the actual prediction calculation as influencing factors. Finally, in the intelligent routing mechanism, clustering mechanism and particle swarm optimization algorithm are used to improve the path selection, so as to improve the fault tolerance of wireless network transmission in the face of large amounts of dynamic data. The research results show that the optimization and intelligent routing algorithm based on energy prediction for wireless sensor networks can prolong the service life cycle and ensure the network transmission efficiency and coverage in a long work cycle. It can ensure the energy supply of the sensor and prolong the service time through the balanced node scheduling.& COPY;2023 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University This is an open access article under the CC BY-NC-ND license (http://***/ licenses/by-nc-nd/4.0/).

关键词： 6G network Energy prediction Wireless sensor network Intelligent routing algorithm clustering algorithm

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Raga Identification using clustering algorithm 3

Raga Identification using Clustering Algorithm

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3rd International Conference on Communications and Signal Processing (ICCSP)

作者： Sheba, L. Loretta Maria Paul Revathy, A. Saranathan Coll Engn Dept ECE Panjappur 620012 Thiruchirappall India

ISBN: (纸本)9781479933587

The main objective of this paper is to evaluate the performance of raga identification using clustering algorithm and to compare the accuracy of the raga identification system which uses Mel Frequency Cepstral Coefficients (MFCC) as feature and the system which uses pitch information along with MFCC features. The goal of Raga identification is to identify the raga independent of training data. The training and testing phase are done for direct film song (vocal with background music) for 10 ragas. In training phase 10 film songs for each raga based on singers is taken as input. The input songs are made to undergo frame blocking. The Mel Frequency Cepstral Coefficients (MFCC) are extracted for each frames of the input signal. Similarly training is also done with MFCC and Pitch features (by Cepstrum method). The raga model is developed by K-means clustering algorithm for each raga. In clustering method, the cluster centroids are obtained for cluster size of 256 and stored. One model is created for each raga. In the testing phase Minimum mean of distances is computed for each model. Raga is classified based on selection of the model which produces minimum of average. The main motive behind Raga identification is that it can be used as a good basis for music information retrieval of any Carnatic music songs or Film songs.

关键词： Raga Identification clustering algorithm MFCC and Pitch

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Uncertainty modeling and applications for operating data-driven inverse design

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JOURNAL OF ENGINEERING DESIGN 2023年第2期34卷 81-110页

作者： Li, Shijiang Hou, Liang Chen, Zebo Wang, Shaojie Bu, Xiangjian Xiamen Univ Dept Mech & Elect Engn Xiamen Peoples R China Xiamen Univ Dept Mech & Elect Engn Xiamen 361102 Fujian Peoples R China

Operating data-driven inverse design (DID) provides updated knowledge for the forward design process. It forms a closed-loop of design enhancements in which uncertainties from different sources, such as the model and environment, have a significant impact on the design and optimisation results. This paper proposes a method for analysing DID uncertainty based on Bayes' theorem. First, the sources of uncertainty in the forward and inverse design processes are analysed, and the a priori information is defined in the inverse design process. The inverse relationship prediction model is based on correlation information and the a posteriori information. A clustering method is proposed to extract information on different working conditions from the operating data to provide a basis for mass-personalised design. Then, the a posteriori information and working conditions information are fed back to the forward design process for design optimisation, forming a closed-loop interaction process of forward and inverse design. Finally, the feasibility and effectiveness of the above methods are illustrated with a case study on the inverse optimisation of the structural characteristic parameters of a bicycle, and the clustering identification of environmental working condition load parameters for mass personalisation.

关键词： Inverse design operating data uncertainty clustering algorithm mass personalisation

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The Mining of Urban Hotspots Based on Multi-Source Location Data Fusion

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2023年第2期35卷 2061-2077页

作者： Cai, Li Wang, Haoyu Sha, Cong Jiang, Fang Zhang, Yihan Zhou, Wei Yunnan Univ Sch Software Kunming 650091 Yunnan Peoples R China

Urban hotspots reflect the degree of residents' travel gathering. The study of urban hotspots has important values for urban infrastructure planning, public security and other aspects. In existing researches, single-source location data and density-based clustering algorithms are used to mine hotspots. Due to the one-sidedness of using the single-source data, the mining of hotspots based on multi-source location data fusion has become a hot topic. Multi-source location data fusion requires a quantity balance between the data set to be fused, because several famous clustering algorithms cannot handle multi-source imbalanced data set. To solve this problem, we propose a novel framework to mine urban hotspots. First, we construct a data imputation model for the sparse data set so that reducing the difference in quantity between two types of data set. Then, a clustering algorithm for imbalanced data set is proposed, and a novel evaluation metric is designed to verify the effectiveness of clustering results. The experiment uses real data set including POI data, check-in data and GPS trajectory data. The results show that the proposed method discovers more than 90% of urban hotspots formed by fused imbalanced data set, and it is more accurate and efficient than the state-of-the-art algorithms.

关键词： clustering algorithms Data mining Tensors Trajectory Global Positioning System Data models Partitioning algorithms Urban hotspots location data multi-source data imbalanced data clustering algorithm

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