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Colour image segmentation using K – Medoids Clustering  [PDF]
Amit Yerpude,Dr. Sipi Dubey
International Journal of Computer Technology and Applications , 2012,
Abstract: K – medoids clustering is used as a tool for clustering color space based on the distance criterion. This paper presents a color image segmentation method which divides colour space into clusters. Through this paper, using various colour images, we will try to prove that K – Medoids converges to approximate the optimal solution based on this criteria theoretically as well as experimentally. Here we will also compare the efficiency of available algorithm for segmentation of gray as well as noisy images.
Document Clustering using K-Medoids  [PDF]
Monica Jha
Computer Science , 2015,
Abstract: People are always in search of matters for which they are prone to use internet, but again it has huge assemblage of data due to which it becomes difficult for the reader to get the most accurate data. To make it easier for people to gather accurate data, similar information has to be clustered at one place. There are many algorithms used for clustering of relevant information in one platform. In this paper, K-Medoids clustering algorithm has been employed for formation of clusters which is further used for document summarization.
An Efficient Density based Improved K- Medoids Clustering algorithm
Masthan Mohammed
International Journal of Computer and Distributed System , 2012,
Abstract: Mining knowledge from large amounts of spatial data is known as spatial data mining. It becomes a highly demanding field because huge amounts of spatial data have been collected in various applications ranging from geo-spatial data to bio-medical knowledge. The database can be clustered in many ways depending on the clustering algorithm employed, parameter settings used, and other factors. Multiple clustering can be combined so that the final partitioning of data provides better clustering. In this paper, an efficient density based k-medoids clustering algorithm has been proposed to overcome the drawbacks of DBSCAN and kmedoids clustering algorithms. Clustering is the process of classifying objects into different groups by partitioning sets of data into a series of subsets called clusters. Clustering has taken its roots from algorithms like k-medoids and k-medoids. However conventional k-medoids clustering algorithm suffers from many limitations. Firstly, it needs to have prior knowledge about the number of cluster parameter k. Secondly, it also initially needs to make random selection of k representative objects and if these initial k medoids are not selected properly then natural cluster may not be obtained. Thirdly, it is also sensitive to the order of input dataset.
K-medoids clustering algorithm method based on differential evolution
一种基于差分演化的K-medoids聚类算法

MENG Ying,LUO Ke,LIU Jian-hua,SHI Shuangc,
孟颖
,罗可,刘建华,石爽c

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.
Document Clustering using K-Means and K-Medoids  [PDF]
Rakesh Chandra Balabantaray,Chandrali Sarma,Monica Jha
Computer Science , 2015,
Abstract: With the huge upsurge of information in day-to-days life, it has become difficult to assemble relevant information in nick of time. But people, always are in dearth of time, they need everything quick. Hence clustering was introduced to gather the relevant information in a cluster. There are several algorithms for clustering information out of which in this paper, we accomplish K-means and K-Medoids clustering algorithm and a comparison is carried out to find which algorithm is best for clustering. On the best clusters formed, document summarization is executed based on sentence weight to focus on key point of the whole document, which makes it easier for people to ascertain the information they want and thus read only those documents which is relevant in their point of view.
Active Distance-Based Clustering using K-medoids  [PDF]
Mehrdad Ghadiri,Amin Aghaee,Mahdieh Soleymani Baghshah
Computer Science , 2015,
Abstract: k-medoids algorithm is a partitional, centroid-based clustering algorithm which uses pairwise distances of data points and tries to directly decompose the dataset with $n$ points into a set of $k$ disjoint clusters. However, k-medoids itself requires all distances between data points that are not so easy to get in many applications. In this paper, we introduce a new method which requires only a small proportion of the whole set of distances and makes an effort to estimate an upper-bound for unknown distances using the inquired ones. This algorithm makes use of the triangle inequality to calculate an upper-bound estimation of the unknown distances. Our method is built upon a recursive approach to cluster objects and to choose some points actively from each bunch of data and acquire the distances between these prominent points from oracle. Experimental results show that the proposed method using only a small subset of the distances can find proper clustering on many real-world and synthetic datasets.
k-medoids clustering algorithm based on CF tree
一种基于CF树的k-medoids聚类算法*

CAO Dan-yang,YANG Bing-ru,LI Guang-yuan,LIU Ying-hua,
曹丹阳
,杨炳儒,李广原,刘英华

计算机应用研究 , 2011,
Abstract: k-medoids algorithm has better robustness when the dataset exist noise and outlier points.However,computational cost of k-medoids algorithm is higher on big datasets.CF tree is a common structure in Birch algorithm,and it has better sca-lability on the clustering of big datasets.But CF tree has poor clustering results on the non-spherical data.Therefore,this paper presented a k-medoids algorithm based on CF tree on the basis of the two algorithms.First,the improved algorithm constructed CF tree by the data ...
A New Clustering Algorithm Based on GA and K-medoids Algorithm
基于遗传算法和k-medoids算法的聚类新算法*

Hao Zhangang,Wang Zhengou,
郝占刚
,王正欧

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.
Efficiency of k-Means and K-Medoids Algorithms for Clustering Arbitrary Data Points  [PDF]
Dr. T. VELMURUGAN
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.
Hybrid Personalized Recommender System Using Fast K-medoids Clustering Algorithm  [cached]
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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