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Parallel K-Medoids++ Spatial Clustering Algorithm Based on MapReduce [article]

Xia Yue, Wang Man, Jun Yue, Guangcao Liu
2016 arXiv   pre-print
In order to improve the efficiency of spatial clustering for large scale data, many researchers proposed several efficient clustering algorithms in parallel.  ...  Comparative Experiments conducted over different dataset and different number of nodes indicate that the proposed K-Medoids spatial clustering algorithm provides better efficiency than traditional K-Medoids  ...  Methodology Initialization algorithm of K-Medoids++ The K-Medoids algorithm finds cluster medoids by minimizing the intra-class discrepancy.  ... 
arXiv:1608.06861v1 fatcat:qa7bh2fuhzfqbntp3ogdqfec5u

Comparative Study between Parallel K-Means and Parallel K-Medoids with Message Passing Interface (MPI)

Fhira Nhita
2017 International Journal on Information and Communication Technology (IJoICT)  
K-Means and K-Medoids is one of clustering algorithms that mostly used because it's easy implementation, efficient, and also present good results.  ...  This research analyzed the result from K-Means and K-Medoids algorithm and time performance using High Performance Computing (HPC) Cluster to parallelize K-Means and K-Medoids algorithms and using Message  ...  ACKNOWLEDGMENT This work was supported in part by Telkom University research grant.  ... 
doi:10.21108/ijoict.2016.22.86 fatcat:6t434qaqezf3xiyo7s3kcaksou

An Improved Version of K-medoid Algorithm using CRO

Amjad Hudaib, Mohammad Khanafseh, Ola Surakhi
2018 Modern Applied Science  
K-medoid is a variant of k-mean that use an actual point in the cluster to represent it instead of the mean in the k-mean algorithm to get the outliers and reduce noise in the cluster.  ...  The performance of the new algorithm is evaluated by comparing its results with five clustering algorithms, k-mean, k-medoid, DB/rand/1/bin, CRO based clustering algorithm and hybrid CRO-k-mean by using  ...  Selecting the medoid of the cluster can be improved by using an optimization method.  ... 
doi:10.5539/mas.v12n2p116 fatcat:it6aq4ovyfanxpggfta4uialtq

An improved fuzzy k-medoids clustering algorithm with optimized number of clusters

Akhtar Sabzi, Yaghoub Farjami, Morteza ZiHayat
2011 2011 11th International Conference on Hybrid Intelligent Systems (HIS)  
In this paper an improved version of fuzzy k-medoids algorithm has been proposed.  ...  However, the determined numbers of cluster as an input and the impact of initial value of cluster centers on clusters' quality are the two major challenges of this algorithm.  ...  Improved Fuzzy -medoids The proposed algorithm gets inspired from agglomerative algorithms.  ... 
doi:10.1109/his.2011.6122106 dblp:conf/his/SabziFZ11 fatcat:6rh7lprc4rcujecspf54ckngq4

An Efficient Density based Improved K- Medoids Clustering algorithm

Raghuvira Pratap, K Suvarna, J Rama, Dr.K Nageswara
2011 International Journal of Advanced Computer Science and Applications  
In this paper, an efficient density based k-medoids clustering algorithm has been proposed to overcome the drawbacks of DBSCAN and kmedoids clustering algorithms.  ...  The result will be an improved version of kmedoids clustering algorithm.  ...  Clustering DBSCAN K-Medoids Improved K- Medoids Accuracy of Classification DBSCAN K-Medoids Improved K-Medoids (IJACSA) International Journal of Advanced Computer Science and Applications  ... 
doi:10.14569/ijacsa.2011.020607 fatcat:jjftf4aonbbfviwjxko537pahm

Research on HCKM Algorithm Based on Parallel Clustering

Min ZHANG, Zhao-jie ZANG
2017 DEStech Transactions on Computer Science and Engineering  
Since the traditional K-medoids algorithm, which is sensitive to the initial cluster center, still exists many limitations in handling large datasets.  ...  According to the experimental results, the feasibility of algorithm has been proved in the changes of running time or speedup.  ...  HCKM Algorithm Research Traditional K-medoids algorithm required to choose the initial center by the researchers.  ... 
doi:10.12783/dtcse/aics2016/8193 fatcat:rk3rwktaqreandj4vj2bvvhubi

Energy Efficient Clustering Technique for VANET [chapter]

Iswarya B, Radha B
2021 Advances in Parallel Computing  
This article proposed a clustering-based optimization technique called Energy Efficient Clustering Technique (EECT) with the AODV protocol's K-Medoids clustering algorithm in order to cluster vehicle nodes  ...  Efficient nodes are recognized from each cluster with the goal of energy-efficient communication, to optimize the parameter as minimum energy consumption in VANET.  ...  Additionally, the Medoid cycle rehash the K-medoid algorithm for distance calculation of a K-medoid algorithm gets 200 knots initially.  ... 
doi:10.3233/apc210129 fatcat:lwsgjgumdjhytk5vvibc4enhaq

Parallelization of Partitioning Around Medoids (PAM) in K-Medoids Clustering on GPU

Adhi Prahara, Dewi Pramudi Ismi, Ahmad Azhari
2020 Knowledge Engineering and Data Science  
Due to the high computational complexity, the efficiency of k-medoids clustering becomes a major concern in the k-medoids algorithm improvement.  ...  Researchers have been working on attempts to improve the performance of k-medoids clustering [11] [12] .  ...  Acknowledgment This research is supported by The Indonesian Ministry of Research, Technology and Higher Education (RISTEK-DIKTI) research grant no. PEKERTI-056/SP3/LPP-UAD/IV/2017.  ... 
doi:10.17977/um018v3i12020p40-49 fatcat:cvk62qr3gfgojpkib7pfnzh74y

Improvement of the Fast Clustering Algorithm Improved by K-Means in the Big Data

Ting Xie, Ruihua Liu, Zhengyuan Wei
2020 Applied Mathematics and Nonlinear Sciences  
In this paper, we consider clustering on feature space to solve the low efficiency caused in the Big Data clustering by K-means.  ...  Clustering as a fundamental unsupervised learning is considered an important method of data analysis, and K-means is demonstrably the most popular clustering algorithm.  ...  Acknowledgements The work described in this paper was supported by the Science and Technology Research Program of Chongqing Municipal Education Commission (Grant No. KJ1709207).  ... 
doi:10.2478/amns.2020.1.00001 fatcat:p4bw22zddbapfnx5nk2zfjhjuu

Exploration of Various Clustering Algorithms for Text Mining

Neha Garg, R.K. Gupta
2018 International Journal of Education and Management Engineering  
Searching of documents can be made more efficient and effective if documents are clustered on the premise of their contents.  ...  Further, author has likewise examined the key challenges of clustering algorithms being used for effective clustering of documents.  ...  If the documents are clustered on the basis of BIRCH algorithm by taking the concept of multiple thresholds as defined by the author, it may improve the efficiency of the algorithm.  ... 
doi:10.5815/ijeme.2018.04.02 fatcat:q56doszilngmberfregiabubhy

Analysis of clustering techniques in VLSI cell partitioning

V. Leela, R. Manikandan
2014 Contemporary Engineerng Sciences  
The ranked K-medoid algorithm has overcome the local optimum problem and has showed the improved result when it is compared with K-mean and K-medoid clustering algorithm.  ...  The proposed ranked k-medoid clustering algorithm method reduces the size of the interconnections distance and also speed up the large scale partitioning problems without any reduction of partitioning  ...  Proposed Algorithm Ranked K-medoid Algorithm The Ranked k-medoid clustering is similar to k-medoid algorithm aims at partitioning a set of objects into k clusters in which each objects belongs to the  ... 
doi:10.12988/ces.2014.429 fatcat:7hdsk3hcz5azrkntoyae4l2h3e

Performance Analysis of Improved Clustering Algorithm on Real and Synthetic Data

Anand Khandare, A. S. Alvi
2017 International Journal of Computer Network and Information Security  
Also, the k predicted by the improved algorithm is compared with optimal k suggested by elbow method. It is found that both values of k are almost similar.  ...  This paper is a study and implementation of an improved clustering algorithm which automatically predicts the value of k and uses a new technique to take initial means.  ...  The working of k-medoids is given as follows: Input: Data, k Output: k-clusters 1. Read data sets and choose k initial medoids. 2. Find the distance between data objects and initial medoids. 3.  ... 
doi:10.5815/ijcnis.2017.10.07 fatcat:yulyfixez5evvp4cywf2j3lbui

A Novel Spatial Clustering with Obstacles Constraints Based on Genetic Algorithms and K-Medoids

Xueping Zhang, Jiayao Wang, Fang Wu, Zhongshan Fan, Xiaoqing Li
2006 Sixth International Conference on Intelligent Systems Design and Applications  
In this paper, we discuss the problem of spatial clustering with obstacles constraints and propose a novel spatial clustering method based on Genetic Algorithms (GAs) and K-Medoids, called GKSCOC, which  ...  The GKSCOC algorithm can not only give attention to higher local constringency speed and stronger global optimum search, but also get down to the obstacles constraints and practicalities of spatial clustering  ...  Acknowledgments This work is partially supported by the Natural Sciences  ... 
doi:10.1109/isda.2006.75 dblp:conf/isda/ZhangWWFL06 fatcat:ve5melllhje5llud4u3pwogzhq

Robust Face Recognition by Applying Partitioning Around Medoids Over Eigen Faces and Fisher Faces

Aruna Bhat
2014 International Journal on Computational Science & Applications  
This paper presents a method to integrate the technique of Partitioning Around Medoids with Eigen Faces and Fisher Faces to improve the efficiency of face recognition considerably.  ...  However the cost and time efficiency play a crucial role in implementing any methodology in real world.  ...  ACKNOWLEDGEMENTS The author would like to thank the faculty and the staff at Electrical Engineering Department,Indian Institute of Technology, Delhi for theirvalued guidance and help they have graciously  ... 
doi:10.5121/ijcsa.2014.4304 fatcat:jqg276xpxreshib5nwhz6ktgka

A Parallel Architecture for the Partitioning Around Medoids (PAM) Algorithm for Scalable Multi-Core Processor Implementation with Applications in Healthcare

Hassan Mushtaq, Sajid Gul Khawaja, Muhammad Usman Akram, Amanullah Yasin, Muhammad Muzammal, Shehzad Khalid, Shoab Ahmad Khan
2018 Sensors  
The Partitioning Around Medoids (PAM) algorithm belongs to the partitioning-based methods of clustering widely used for objects categorization, image analysis, bioinformatics and data compression, but  ...  Experiments show that the computational complexity of the PAM algorithm is reduced exponentially as we increase the number of cores working in parallel.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/s18124129 pmid:30477277 fatcat:rkwzqira6jfelf43lmlogt2ujq
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