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Ayla Sayli, Ahmet Dursun Alkan, Merve Aydin
2016 Brodogradnja  
We also use the Elbow method to estimate the true number of clusters for K-Means algorithm.  ...  functions of heave and pitch motions and of absolute vertical acceleration, by our in-house software application based on K-Means algorithm from data mining.  ...  Determination of relational classification among hull form parameters Ayla Sayli and ship motions performance for a set of small vessels Ahmet Dursun Alkan, Merve Aydın  ... 
doi:10.21278/brod67401 fatcat:3foconesurhyjilvwziltocd7m

Application of PCA-K-means++ combination model to construction of light vehicle driving conditions in intelligent traffic

Shuqing Guo, Kangkai Wu, Guoqing Zhang
2020 Journal of Measurements in Engineering  
With the application of PCA-K-means and PCA-K-means++ clustering algorithm, a driving condition curve with a duration of 1200s is constructed before its effectiveness and accuracy being compared and analyzed  ...  The results show that the error rate of driving condition between sample data and driving condition constructed by PCA-K-mean++ clustering algorithm is less than 6 % and the error rate of average speed  ...  reduced to 𝐾 unrelated principal components: 𝑓 = 𝐿 𝑧 . (9) K-means++ clustering method K-means++ algorithm theory Aiming at big data clustering, K-means++ algorithm is the optimization of random  ... 
doi:10.21595/jme.2020.21433 fatcat:csa7ymxlunf6jmnur6ya32iija

Makingk-means even faster [chapter]

Greg Hamerly
2010 Proceedings of the 2010 SIAM International Conference on Data Mining  
The k-means algorithm is widely used for clustering, compressing, and summarizing vector data.  ...  Our algorithm uses one novel lower bound for point-center distances, which allows it to eliminate the innermost k-means loop 80% of the time or more in our experiments.  ...  Related work Many people have worked on accelerating clustering algorithms.  ... 
doi:10.1137/1.9781611972801.12 dblp:conf/sdm/Hamerly10 fatcat:5d3ulgfynfb2bgvorezkm6szw4

Faster Mean-shift: GPU-accelerated Embedding-clustering for Cell Segmentation and Tracking [article]

Mengyang Zhao, Aadarsh Jha, Quan Liu, Bryan A. Millis, Anita Mahadevan-Jansen, Le Lu, Bennett A. Landman, Matthew J.Tyskac, Yuankai Huo
2020 arXiv   pre-print
Different from previous GPU-accelerated fast mean-shift algorithms, a new online seed optimization policy (OSOP) is introduced to adaptively determine the minimal number of seeds, accelerate computation  ...  In this study, we propose a novel Faster Mean-shift algorithm, which tackles the computational bottleneck of embedding based cell segmentation and tracking.  ...  Our research is closely related to holistic instance segmentation and tracking [16] , as well as GPU accelerated clustering algorithms [21] , [22] .  ... 
arXiv:2007.14283v1 fatcat:5nmkl2sbfzeddmrcy6cn5xpkzu

AccD: A Compiler-based Framework for Accelerating Distance-related Algorithms on CPU-FPGA Platforms [article]

Yuke Wang, Boyuan Feng, Gushu Li, Lei Deng, Yuan Xie, Yufei Ding
2019 arXiv   pre-print
As a promising solution to boost the performance of distance-related algorithms (e.g., K-means and KNN), FPGA-based acceleration attracts lots of attention, but also comes with numerous challenges.  ...  In this work, we propose AccD, a compiler-based framework for accelerating distance-related algorithms on CPU-FPGA platforms.  ...  For example, K-means updates the cluster centers by averaging the positions of the points inside.  ... 
arXiv:1908.11781v1 fatcat:qomhzsfycfcgtbki3paqmok6f4

A Multi-Clustering Algorithm to Solve Driving Cycle Prediction Problems Based on Unbalanced Data Sets: A Chinese Case Study

Yuewei Wu, Wutong Zhang, Long Zhang, Yuanyuan Qiao, Jie Yang, Cheng Cheng
2020 Sensors  
clustering algorithms on unbalanced data sets.  ...  If the ordinary clustering algorithm is directly applied to the unbalanced data, it will have a huge impact on the performance to build driving cycle maps, and the parameters of the map will deviate considerable  ...  The algorithm framework integrates k-means-type, K-means++, DBSCAN and other clustering algorithms, and uses stacking to strengthen each individual model.  ... 
doi:10.3390/s20092448 pmid:32344855 pmcid:PMC7248886 fatcat:lqytnyceore6hgxvphf5gvufzu

Evolutionary Clustering Algorithms for Relational Data

Amit Banerjee, Issam Abu-Mahfouz
2018 Procedia Computer Science  
Relational data clustering has received lot less attention than vector data clustering and the use of evolutionary techniques to optimize clustering parameters is even rare.  ...  We extend an earlier work where a relational data version of DBSCAN was presented and an evolutionary framework was proposed for optimizing clustering parameters.  ...  Another set of techniques is based on the k-mediods approach more suited to relational data because mediods unlike means (such as in k-means or Fuzzy c-Means) are actual entities in the dataset that can  ... 
doi:10.1016/j.procs.2018.10.319 fatcat:wfa6s77qazftfhgmhe3gofmqdy

CAMPAIGN: an open-source library of GPU-accelerated data clustering algorithms

K. J. Kohlhoff, M. H. Sosnick, W. T. Hsu, V. S. Pande, R. B. Altman
2011 Bioinformatics  
Results: CAMPAIGN is a library of data clustering algorithms and tools, written in 'C for CUDA' for Nvidia GPUs.  ...  The library provides up to two orders of magnitude speed-up over respective CPU-based clustering algorithms and is intended as an open-source resource.  ...  ACKNOWLEDGEMENTS The authors are grateful to Imran Haque and the staff at the Simbios NIH Center for Biomedical Computation for helpful suggestions. Conflict of Interest: none declared.  ... 
doi:10.1093/bioinformatics/btr386 pmid:21712246 pmcid:PMC3150041 fatcat:urce3o7mnbfktjpisawbcr25hm

Driving Style Recognition Model Based on NEV High-Frequency Big Data and Joint Distribution Feature Parameters

Lina Xia, Zejun Kang
2021 World Electric Vehicle Journal  
Among them, energy consumption and driving range are particularly concerning, and are closely related to the driving style of the driver.  ...  algorithm, and build a driving style recognition model through a neural network algorithm.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/wevj12030142 fatcat:ygb44emya5exldoykfajtk25vm

Cloud Computing and Its Application in Big Data Processing of Distance Higher Education

Guolei Zhang, Jia Li, Li Hao
2015 International Journal of Emerging Technologies in Learning (iJET)  
In this paper, we study the parallel K-means clustering algorithm based on cloud computing platform Hadoop, and give the design and strategy of the algorithm.  ...  However, the traditional K-means clustering algorithm has the characteristics of randomness, uncertainty, high time complexity, and it does not meet the requirements of large data processing.  ...  Parallel K-mean algorithm A massively parallel and scalable implementation of a parallel k-means algorithm was developed for cluster analysis of very large datasets (Figure 3 ).  ... 
doi:10.3991/ijet.v10i8.5280 fatcat:t6jr5mrwwjahnmjhe4qy3hq6v4

Using Multi-Core HW/SW Co-design Architecture for Accelerating K-means Clustering Algorithm [article]

Hadi Mardani Kamali
2018 arXiv   pre-print
However, due to large size (volume) of Big-Data, and large dimensionality of its data points, even the application of a simple k-mean clustering may become extremely time and resource demanding.  ...  K-mean clustering is an essential tool for many big data applications including data mining, predictive analysis, forecasting studies, and machine learning.  ...  The execution time of k-clustering algorithms could be improved by means of both SW and HW.  ... 
arXiv:1807.09250v1 fatcat:wbvre7jsbbgxtjlcilfwfvpqv4

Partitioning hard clustering algorithms based on multiple dissimilarity matrices

Francisco de A.T. de Carvalho, Yves Lechevallier, Filipe M. de Melo
2012 Pattern Recognition  
This paper introduces hard clustering algorithms that are able to partition objects taking into account simultaneously their relational descriptions given by multiple dissimilarity matrices.  ...  These relevance weights change at each algorithm iteration and can either be the same for all clusters or different from one cluster to another.  ...  The clustering algorithm has been performed simultaneously on these 3 relational data tables (position, velocity and acceleration of the radar waveforms) to obtain a partition in K = {1, . . . , 10}.  ... 
doi:10.1016/j.patcog.2011.05.016 fatcat:fmrnc5eotzgwdepehqfqtsm7vu

Towards Convolutional Neural Network Acceleration and Compression Based on Simonk-Means

Mingjie Wei, Yunping Zhao, Xiaowen Chen, Chen Li, Jianzhuang Lu
2022 Sensors  
First, we propose an extension algorithm named Simonk-means based on simple k-means. We use Simonk-means to cluster trained weights in convolutional layers and fully connected layers.  ...  Finally, we provide the hardware implementation of the compressed CNN accelerator.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/s22114298 pmid:35684919 fatcat:slfbgzp7hrfaxgp4mxr3un7wpu


2020 Transport Problems  
The driver's categorization was based on a statistical analysis of input signals and mean tractive force (MTF) by clustering.  ...  Relations between the type of driver, driving dynamics, and fuel consumption were studied.  ...  K-means clustering is the most commonly used unsupervised machine learning algorithm for partitioning a given data set into a set of k groups (i.e. k clusters), where k represents the number of groups  ... 
doi:10.21307/tp-2020-050 fatcat:qlw7rgvhujfilkhbi46sta6lre

A New K-Means Clustering Algorithm for Customer Classification in Precision Marketing

Xinwu Li, Xiaoling Du
2021 Converter  
Theoriginal K-means algorithm is improved and applied in customer clustering in precision marketing.  ...  Firstly, integrates K-means algorithm with particle swarm optimization according to analyzing the source of the K-means calculation limitations; Secondly, improves the improved algorithm in its operation  ...  Acknowledgements Theresearch is funded by the The 12th Five Year Plan of Social Sciences in Jiangxi Province( hosted by Ye Hankun) and education department of Jiangxi Province (GJJ150458).  ... 
doi:10.17762/converter.227 fatcat:ovbi4ss7d5cxfichsb6ptidnc4
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