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Search Results: 1 - 10 of 20877 matches for " K-medoids algorithm "
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A State of Art Analysis of Telecommunication Data by k-Means and k-Medoids Clustering Algorithms  [PDF]
T. Velmurugan
Journal of Computer and Communications (JCC) , 2018, DOI: 10.4236/jcc.2018.61019
Cluster analysis is one of the major data analysis methods widely used for many practical applications in emerging areas of data mining. A good clustering method will produce high quality clusters with high intra-cluster similarity and low inter-cluster similarity. Clustering techniques are applied in different domains to predict future trends of available data and its uses for the real world. This research work is carried out to find the performance of two of the most delegated, partition based clustering algorithms namely k-Means and k-Medoids. A state of art analysis of these two algorithms is implemented and performance is analyzed based on their clustering result quality by means of its execution time and other components. Telecommunication data is the source data for this analysis. The connection oriented broadband data is given as input to find the clustering quality of the algorithms. Distance between the server locations and their connection is considered for clustering. Execution time for each algorithm is analyzed and the results are compared with one another. Results found in comparison study are satisfactory for the chosen application.
A New Text Clustering Method Based on KGA
ZhanGang Hao
Journal of Software , 2012, DOI: 10.4304/jsw.7.5.1094-1098
Abstract: Text clustering is one of the key research areas in data mining. K-medoids is a classical partitioning algorithm, which can better solve the isolated point problem, but it often converges to local optimization. In this paper, we put forward a new genetic algorithm called KGA algorithm by putting k-medoids into the genetic algorithm, then we form a local Optimal Solution with multiple initial species group, strategy for crossover within a species group and crossover among species groups, using the mutation threshold to control mutation. This algorithm will increase the diversity of species group and enhance the optimization capability of genetic algorithm, thus improve the accuracy of clustering and the capacity of acquiring isolated points.
A New Clustering Algorithm Based on GA and K-medoids Algorithm

Hao Zhangang,Wang Zhengou,

现代图书情报技术 , 2006,
Abstract: This paper presents a new clustering algorithm based on GA(Genetic Algorithm) and k-medoids algorithm.The new algorithm can not only improve the precision of clustering but also recognize isolated points.At the same time,the new algorithm may expedite the convergence of GA and save the time cost for integration with the k-medoids algorithm in GA.
Research on parallel K-Medoids algorithm based on multi-core platform

LI Jing-bin,YANG Liu,HUA Bei,

计算机应用研究 , 2011,
Abstract: Analyzed the nice parallelism of K-Medoids and redesigned it to make suitable for multi-core platform. Implemented the parallel algorithm by OpenMP at last. The experimental results show that the new algorithm suits for multi-core condition very well and obtains good speedup and running efficiency on two-core and four-core machines both.
Efficient K-medoids clustering algorithm

计算机应用研究 , 2010,
Abstract: Due to the disadvantages of sensitivity to the initial selection of the medoids and poor performance in large data set processing in the K-medoids clustering algorithm, this paper proposed an improved K-medoids algorithm based on a fine-tuned of initial medoids and an incremental candidate set of medoids. The proposed algorithm optimized initial medoids by fine-tu-ning and reduced computational complexity of medoids substitution through expanding medoids candidate set gradually. Expenrimental results demonstrate the effectiveness of this algorithm,which can improve clustering quality and significantly shorten the time in calculation compared with the traditional K-medoids algorithm.
Efficiency of k-Means and K-Medoids Algorithms for Clustering Arbitrary Data Points
International Journal of Computer Technology and Applications , 2012,
Abstract: There are number of techniques proposed byseveral researchers to analyze the performance ofclustering algorithms in data mining. All thesetechniques are not suggesting good results for thechosen data sets and for the algorithms in particular.Some of the clustering algorithms are suit for somekind of input data. This research work usesarbitrarily distributed input data points to evaluatethe clustering quality and performance of two of thepartition based clustering algorithms namely k-Means and k-Medoids. To evaluate the clusteringquality, the distance between two data points aretaken for analysis. The computational time iscalculated for each algorithm in order to measure theperformance of the algorithms. The experimentalresults show that the k-Means algorithm yields thebest results compared with k-Medoids algorithm.
IC-kmedoids: A Clustering Algorithm for RNA Secondary Structure Prediction

- , 2015, DOI: 10.7507/1001-5515.20150018
Abstract: 采用自由能方法预测RNA二级结构时, 如何精确有效地从次优结构中筛选出真实的二级结构成为RNA结构预测中的关键。采用聚类技术对次优结构集合进行分析, 可有效地提高预测结果的精度。本文利用RBP分数矩阵, 提出一种基于增量中心候选集的改进k-medoids算法。它将随机选择初始中心并进行首次划分后以中心候选集逐一扩展的方式进行中心轮换, 以降低算法的复杂度。实验表明, 该算法能取得更高的CH值, 且能有效地缩短计算时间。
Due to the minimum free energy model, it is very important to predict the RNA secondary structure accurately and efficiently from the suboptimal foldings. Using clustering techniques in analyzing the suboptimal structures could effectively improve the prediction accuracy. An improved k-medoids cluster method is proposed to make this a better accuracy with the RBP score and the incremental candidate set of medoids matrix in this paper. The algorithm optimizes initial medoids through an expanding medoids candidate sets gradually.The predicted results indicated this algorithm could get a higher value of CH and significantly shorten the time for calculating clustering RNA folding structures.
K-medoids clustering algorithm method based on differential evolution

MENG Ying,LUO Ke,LIU Jian-hua,SHI Shuangc,

计算机应用研究 , 2012,
Abstract: The traditional K-medoids clustering algorithm, because on the initial clustering center sensitive, the global search ability is poor, easily trapped into local optimal, slow convergent speed, and so on. Therefore, this paper proposed a kind of K-medoids clustering algorithm based on differential evolution. Differential evolution was a kind of heuristic global search technology population, had strong robustness. It combined with the global optimization ability of differential evolution using K-medoids clustering algorithm, effectively overcame K-medoids clustering algorithm, shortend convergence time, improved clustering quality. Finally, the simulation result shows that the algorithm is verified stability and robustness.
Travel behavior analysis using genetic clustering algorithm

XIANYU Jian-chuan,JUAN Zhi-cai,

计算机应用研究 , 2009,
Abstract: Based on the good performance of K-medoids clustering algorithm for categorical data and the nice self-organization, self-adaptation and self-learning of genetic algorithm, this paper aimed to develop a methodology for the clustering of activity patterns with a genetic algorithm based clustering method. The proposed method used integer coded chromosome. The dissimilarity measure between two activity patterns was defined as the total number of mismatches of activity types at a corresponding time index and the fitness function was defined as the sum of dissimilarities of all objects to their nearest medoids. The results for different sizes of data sets and for different parameter settings were compared and based on this recommended parameter settings were provided. It is demonstrated that the algorithm is good at preventing premature convergence, decreasing the sensitivity to outliers and that it is fast converging and is a good solution for categorical data clustering analysis.
Hybrid Personalized Recommender System Using Fast K-medoids Clustering Algorithm
Subhash K. Shinde,Uday V. Kulkarni
Journal of Advances in Information Technology , 2011, DOI: 10.4304/jait.2.3.152-158
Abstract: Recommender systems attempt to predict items in which a user might be interested, given some information about the user’s and items’ profiles. This paper proposes a fast k-medoids clustering algorithm which is used for Hybrid Personalized Recommender System (FKMHPRS). The proposed system works in two phases. In the first phase, opinions from the users are collected in the form of user-item rating matrix. They are clustered offline using fast k-medoids into predetermined number clusters and stored in a database for future recommendation. In the second phase, clusters are used as the neighborhoods, the prediction rating for the active users on items are computed by either weighted sum or simple weighted average. This helps to get more effective and quality recommendations for the active users. The experimental results using Iris dataset show that the proposed fast k-medoids performs better than k-medoids and k-mean algorithms. The performance of FKMHPRS is evaluated using Jester database available on website of California University, Berkeley and compared with web personalized recommender system (WPRS). The results obtained empirically demonstrate that the proposed FKMHPRS performs superiorly.
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