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DETERMINATION OF RELATIONAL CLASSIFICATION AMONG HULL FORM PARAMETERS AND SHIP MOTIONS PERFORMANCE FOR A SET OF SMALL VESSELS
2016
Brodogradnja
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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
2020
Journal of Measurements in Engineering
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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]
2010
Proceedings of the 2010 SIAM International Conference on Data Mining
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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]
2020
arXiv
pre-print
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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]
2019
arXiv
pre-print
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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
2020
Sensors
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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
2018
Procedia Computer Science
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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
2011
Bioinformatics
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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
2021
World Electric Vehicle Journal
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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
2015
International Journal of Emerging Technologies in Learning (iJET)
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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]
2018
arXiv
pre-print
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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
2012
Pattern Recognition
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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
2022
Sensors
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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
DRIVING STYLE ANALYSIS AND DRIVER CLASSIFICATION USING OBD DATA OF A HYBRID ELECTRIC VEHICLE
2020
Transport Problems
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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
2021
Converter
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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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