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PPreDeConStream: A Parallel Version of PreDeConStream Algorithm

Reza Tashvighi, Alireza Bagheri
2016 International Journal of Computer Applications  
PreDeConStream is a density-based data stream clustering algorithm for clustering high-dimensional data streams.  ...  General Terms Data mining, data stream, parallel algorithms, clustering, micro cluster Keywords Clustering data stream algorithms, parallel algorithms, microcluster, density-based clustering, shared memory  ...  Clustering data streams must handle the following challenges: There are different algorithms for Density-based clustering data stream approach.  ... 
doi:10.5120/ijca2016912235 fatcat:6n5zdojbwrhwll2d5l324v5ioq

Load Forecasting Method Based on Improved Deep Learning in Cloud Computing Environment

Kai Zhang, Wei Guo, Jian Feng, Mei Liu, Yi-Zhang Jiang
2021 Scientific Programming  
Then, the density peak clustering algorithm based on spark is used to detect abnormal data in each partition, and the local clusters and abnormal points are merged.  ...  For the problems of low accuracy and low efficiency of most load forecasting methods, a load forecasting method based on improved deep learning in cloud computing environment is proposed.  ...  , the Spark parallel programming model is used to parallelize anomaly data detection algorithm to improve its efficiency. e parallel detection algorithm of density peak clustering anomaly data based on  ... 
doi:10.1155/2021/3250732 fatcat:h6v6d4locfgpraqgzzt6lc54ty

An improved high-density sub trajectory clustering algorithm

Xiaoming Liu, Luxi Dong, Chunlin Shang, Xiangda Wei
2020 IEEE Access  
INDEX TERMS Trajectory data, parallel boundary, high-density, sub trajectory clustering, entropy enter.  ...  According to above deficiency, an improved high-density sub-trajectory clustering algorithm (HTRACLUS_DL) is proposed under the practical application background of a traffic corridor identification.  ...  Liu et al.: Improved High-Density Sub Trajectory Clustering Algorithm Algorithm 1 Partitioning Method Based on Sub-Trace Parallel Edges Input: Any Traj = {q 1 , · · · , q n }.  ... 
doi:10.1109/access.2020.2974059 fatcat:uxn2setnvbbkbg4qg36vfpzsbq

Parallel seed selection method for overlapping community detection in social network

Belfin R V, Grace Mary Kanaga E
2018 Scalable Computing : Practice and Experience  
The algorithm in parallel finds out the superior seed set in the network and expands it in parallel to find out the community.  ...  The work shows amazing improvement in the runtime and also detects quality groups in the network.  ...  The input of the algorithm will be the unlabeled network, for example, social graph or collaboration networks or the network of web pages.  ... 
doi:10.12694/scpe.v19i4.1429 fatcat:zevumh6txve5fgrzl7www7gezq

Parallel clustering algorithms

Xiaobo Li, Zhixi Fang
1989 Parallel Computing  
Although the parallel clustering algorithms have been used for many applications, the clustering tasks are applied as preprocessing steps for parallelization of other algorithms too.  ...  Therefore, the applications of parallel clustering algorithms and the clustering algorithms for parallel computations are described in this paper.  ...  Parallel Density-based Clustering Algorithms DBSCAN is one of density-based clustering algorithms [26] , which can have arbitrary shape of clusters and efficient enough to be used for large spatial data  ... 
doi:10.1016/0167-8191(89)90036-7 fatcat:uly53om5cjgzvcfdft5mkikafe

Hybrid decomposition method in parallel molecular dynamics simulation based on SMP cluster architecture

Bing Wang, Jiwu Shu, Weimin Zheng, Jinzhao Wang, Min Chen
2005 Tsinghua Science and Technology  
The method also partitions particle pairs within each node using the force decomposition strategy to improve the load balance for each node.  ...  A hybrid decomposition method for molecular dynamics simulations was presented, using simultaneously spatial decomposition and force decomposition to fit the architecture of a cluster of symmetric multi-processor  ...  This paper describes a hybrid decomposition method for parallel MD simulations based on the SMP cluster architecture.  ... 
doi:10.1016/s1007-0214(05)70052-3 fatcat:otysyljfjnh6pem6yp3adiqn3y

Link quality and path based clustering in IEEE 802.15.4-2015 TSCH networks

Alexandros Mavromatis, Georgios Z. Papadopoulos, Xenofon Fafoutis, Angelos Goulianos, George Oikonomou, Periklis Chatzimisios, Theo Tryfonas
2017 2017 IEEE Symposium on Computers and Communications (ISCC)  
The proposed algorithm is based on the link quality among nodes as well as the network density.  ...  Algorithms based on clustering are being widely used in the networking field since they are able to deliver high efficiency, in matter to implement parallel processing, load balancing and fault tolerance  ... 
doi:10.1109/iscc.2017.8024625 dblp:conf/iscc/MavromatisPFGOC17 fatcat:3ghusoq76nhllageb2x66r5hq4

Research on Parallel Design of DBSCAN Clustering Algorithm in Spatial Data Mining

Gong-jian ZHOU
2018 DEStech Transactions on Engineering and Technology Research  
The experiment shows that the improved DBSCAN parallel algorithm has better acceleration ratio and extensibility.  ...  To solve the above problems, this paper proposes a parallel grid clustering algorithm and two cluster merging strategies of DBSCAN based on Spark platform, will find the Eps neighborhood to narrow the  ...  DBSCAN Algorithm DBSCAN (Density-Based Spatial Clustering of Applications with Noise) is an algorithm proposed by Ester Martin et al. to spatially cluster data object density as a similarity index [2]  ... 
doi:10.12783/dtetr/ecar2018/26370 fatcat:y6vv77gvr5drvipddlxmnau5nm

Data Mining Algorithm for Cloud Network Information Based on Artificial Intelligence Decision Mechanism

Yuan Huang, Zhe Cheng, Qianyu Zhou, Yuxing Xiang, Ruixiao Zhao
2020 IEEE Access  
This improves the robustness of the algorithm and the adaptability of the algorithm to the shape and structure of the data, so that the parallel and scalable clustering algorithm can more effectively perform  ...  Based on Spark programming model, this paper designs the parallel extension of fuzzy c-means.  ...  This makes the clustering result unsatisfactory when the shape of the cluster is irregular or the size is very different. 2) DENSITY-BASED CLUSTERING ALGORITHM The density-based clustering method uses  ... 
doi:10.1109/access.2020.2981632 fatcat:auwfsg4mvzgjrd6gyutt2ye3q4

Fast and Parallel RankClus Algorithm based on Dynamic Rank Score Tracking

Kotaro Yamazaki, Shohei Matsugu, Hiroaki Shiokawa, Hiroyuki Kitagawa
2020 Journal of Information Processing  
For further improving the efficiency, we also present a parallel implementation of our proposed algorithm by using thread-based parallelization on a modern manycore CPU.  ...  The RankClus framework accurately performs clustering for bi-type information networks using ranking-based graph clustering techniques.  ...  Density-based graph clustering is an extension of DBSCAN [7] , which is a traditional density-based clustering algorithm for multi-dimensional data objects.  ... 
doi:10.2197/ipsjjip.28.453 fatcat:u536ieu6j5dzbcxpbry26h3mqy

Research on the Parallelization of the DBSCAN Clustering Algorithm for Spatial Data Mining Based on the Spark Platform

Fang Huang, Qiang Zhu, Ji Zhou, Jian Tao, Xiaocheng Zhou, Du Jin, Xicheng Tan, Lizhe Wang
2017 Remote Sensing  
Xu et al. proposed a network-based fast parallel-DBSCAN (PDBSCAN) algorithm for use on cluster systems with master-slave architectures.  ...  Density-based spatial clustering of applications with noise (DBSCAN) is a density-based clustering algorithm that has the characteristics of being able to discover clusters of any shape, effectively distinguishing  ...  Introduction Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is an algorithm proposed by Ester et al. for clustering analyses based on the density method in 1996 [1] .  ... 
doi:10.3390/rs9121301 fatcat:mtrnqvus4bdedi7arzdvijkoue

Improving the reference network in wide-area Persistent Scatterer Interferometry for non-urban areas

Kanika Goel, Nico Adam, Robert Shau, Fernando Rodriguez-Gonzalez
2016 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)  
This paper presents algorithms for improving the reference network in wide-area PSI, with a focus on non-urban areas.  ...  In rural regions, low density of PSs leads to separate clusters during reference network construction.  ...  For this purpose, use a selected parallel inversion method based on LDL decomposition. EXPERIMENTAL RESULTS The proposed algorithms are tested on three ERS-1/2 stacks of Germany.  ... 
doi:10.1109/igarss.2016.7729370 dblp:conf/igarss/GoelASG16 fatcat:ltisaaiqxrh6joqf5cyhc7moim

Big data clustering with varied density based on MapReduce

Safanaz Heidari, Mahmood Alborzi, Reza Radfar, Mohammad Ali Afsharkazemi, Ali Rajabzadeh Ghatari
2019 Journal of Big Data  
is a prevalent method of density-based clustering algorithms, the most important feature of which is the ability to detect arbitrary shapes and varied clusters and noise data.  ...  In this paper, we have attempted to introduce a new algorithm for clustering big data with varied density using a Hadoop platform running MapReduce.  ...  Clustering methods are divided into five categories: partially based, hierarchical, density-based, model-based, and network-based [9, 10] .  ... 
doi:10.1186/s40537-019-0236-x fatcat:7dknmmz2ejdr5nobdwo5zccfnu

A Survey of Parallel Clustering Algorithms Based on Spark

Wen Xiao, Juan Hu
2020 Scientific Programming  
Spark is one of the most popular parallel processing platforms for big data, and many researchers have proposed many parallel clustering algorithms based on Spark.  ...  In this paper, the existing parallel clustering algorithms based on Spark are classified and summarized, the parallel design framework of each kind of algorithms is discussed, and after comparing different  ...  Figure 5 :Figure 6 56 Core point p and q are density-connected. : e framework of a parallel density clustering algorithm based on Spark.  ... 
doi:10.1155/2020/8884926 fatcat:24pcgfyhuffl3mrwxtf4kjeenu

Scalable Clustering Algorithms for Big data: A Review

Mahmoud A. Mahdi, Khalid M. Hosny, Ibrahim Elhenawy
2021 IEEE Access  
. • The most commonly used method for handling stream data in the studies was density-based algorithms.  ...  These advantages allow many researchers to easily applied and improved their algorithms in parallel processing systems.  ... 
doi:10.1109/access.2021.3084057 fatcat:wwjmq557fbbfjgr4w3makbjkg4
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