Parallel Implementation of Nonparametric Clustering Algorithm HCA-MS on GPU Using CUDA

2019 ◽  
pp. 189-196
Author(s):  
S. A. Rylov
Keyword(s):  
Clustering Algorithm ◽  
Parallel Implementation ◽  
Nonparametric Clustering

Parallel implementation of fuzzy minimals clustering algorithm

2016 ◽  
Vol 48 ◽  
pp. 35-41 ◽  
Author(s):  
Isabel Timón ◽  
Jesús Soto ◽  
Horacio Pérez-Sánchez ◽  
José M. Cecilia
Keyword(s):  
Clustering Algorithm ◽  
Parallel Implementation

scholarly journals Parallel Cleaning Algorithm for Similar Duplicate Chinese Data Based on BERT

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Biqiu Li ◽  
Jiabin Wang ◽  
Xueli Liu
Keyword(s):  
Data Mining ◽  
Large Scale ◽  
Clustering Algorithm ◽  
Parallel Implementation ◽  
Data Cleaning ◽  
Position Vector ◽  
Data Sets ◽  
Implementation Scheme ◽  
Mining Work ◽  
Context Features

Data is an important source of knowledge discovery, but the existence of similar duplicate data not only increases the redundancy of the database but also affects the subsequent data mining work. Cleaning similar duplicate data is helpful to improve work efficiency. Based on the complexity of the Chinese language and the bottleneck of the single machine system to large-scale data computing performance, this paper proposes a Chinese data cleaning method that combines the BERT model and a k-means clustering algorithm and gives a parallel implementation scheme of the algorithm. In the process of text to vector, the position vector is introduced to obtain the context features of words, and the vector is dynamically adjusted according to the semantics so that the polysemous words can obtain different vector representations in different contexts. At the same time, the parallel implementation of the process is designed based on Hadoop. After that, k-means clustering algorithm is used to cluster similar duplicate data to achieve the purpose of cleaning. Experimental results on a variety of data sets show that the parallel cleaning algorithm proposed in this paper not only has good speedup and scalability but also improves the precision and recall of similar duplicate data cleaning, which will be of great significance for subsequent data mining.

GPUDePiCt: A Parallel Implementation of a Clustering Algorithm for Computing Degenerate Primers on Graphics Processing Units

2015 ◽  
Vol 12 (2) ◽  
pp. 445-454 ◽  
Author(s):  
Trevor Cickovski ◽  
Tiffany Flor ◽  
Galen Irving-Sachs ◽  
Philip Novikov ◽  
James Parda ◽  
...  
Keyword(s):  
Graphics Processing Units ◽  
Clustering Algorithm ◽  
Parallel Implementation ◽  
Degenerate Primers ◽  
Graphics Processing

A Nonparametric Clustering Algorithm with a Quantile-Based Likelihood Estimator

2014 ◽  
Vol 26 (9) ◽  
pp. 2074-2101 ◽  
Author(s):  
Hideitsu Hino ◽  
Noboru Murata
Keyword(s):  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Conditional Entropy ◽  
Tuning Parameter ◽  
Clustering Methods ◽  
Sampling Weights ◽  
Data Set ◽  
Information Theoretic ◽  
Nonparametric Clustering ◽  
Conditional Information

Clustering is a representative of unsupervised learning and one of the important approaches in exploratory data analysis. By its very nature, clustering without strong assumption on data distribution is desirable. Information-theoretic clustering is a class of clustering methods that optimize information-theoretic quantities such as entropy and mutual information. These quantities can be estimated in a nonparametric manner, and information-theoretic clustering algorithms are capable of capturing various intrinsic data structures. It is also possible to estimate information-theoretic quantities using a data set with sampling weight for each datum. Assuming the data set is sampled from a certain cluster and assigning different sampling weights depending on the clusters, the cluster-conditional information-theoretic quantities are estimated. In this letter, a simple iterative clustering algorithm is proposed based on a nonparametric estimator of the log likelihood for weighted data sets. The clustering algorithm is also derived from the principle of conditional entropy minimization with maximum entropy regularization. The proposed algorithm does not contain a tuning parameter. The algorithm is experimentally shown to be comparable to or outperform conventional nonparametric clustering methods.

scholarly journals Parallel Implementation of Improved K-Means Based on a Cloud Platform

2019 ◽  
Vol 48 (4) ◽  
pp. 673-681
Author(s):  
Shufen Zhang ◽  
Zhiyu Liu ◽  
Xuebin Chen ◽  
Changyin Luo
Keyword(s):  
Large Scale ◽  
Clustering Algorithm ◽  
Programming Model ◽  
Parallel Implementation ◽  
Clustering Algorithms ◽  
Data Set ◽  
Large Scale Data ◽  
Sample Density ◽  
Scale Data ◽  
Selection Of

In order to solve the problem of traditional K-Means clustering algorithm in dealing with large-scale data set, a Hadoop K-Means (referred to HKM) clustering algorithm is proposed. Firstly, according to the sample density, the algorithm eliminates the effects of noise points in the data set. Secondly, it optimizes the selection of the initial center point using the thought of the max-min distance. Finally, it uses a MapReduce programming model to realize the parallelization. Experimental results show that the proposed algorithm not only has high accuracy and stability in clustering results, but can also solve the problems of scalability encountered by traditional clustering algorithms in dealing with large scale data.

Class: A nonparametric clustering algorithm

1976 ◽  
Vol 8 (3) ◽  
pp. 107-114 ◽  
Author(s):  
Frederick R. Fromm ◽  
Richard A. Northouse
Keyword(s):  
Clustering Algorithm ◽  
Class A ◽  
Nonparametric Clustering
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