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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.
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.
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.
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.
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.
Novel text categorization by amalgamation of augmented k-nearest neighborhood classification and k-medoids clustering  [PDF]
RamachandraRao Kurada,Dr. K Karteeka Pavan
Computer Science , 2013, DOI: 10.5121/ijcsity.2013.1406
Abstract: Machine learning for text classification is the underpinning of document cataloging, news filtering, document steering and exemplification. In text mining realm, effective feature selection is significant to make the learning task more accurate and competent. One of the traditional lazy text classifier k-Nearest Neighborhood (kNN) has a major pitfall in calculating the similarity between all the objects in training and testing sets, there by leads to exaggeration of both computational complexity of the algorithm and massive consumption of main memory. To diminish these shortcomings in viewpoint of a data-mining practitioner an amalgamative technique is proposed in this paper using a novel restructured version of kNN called AugmentedkNN(AkNN) and k-Medoids(kMdd) clustering.The proposed work comprises preprocesses on the initial training set by imposing attribute feature selection for reduction of high dimensionality, also it detects and excludes the high-fliers samples in the initial training set and restructures a constrictedtraining set. The kMdd clustering algorithm generates the cluster centers (as interior objects) for each category and restructures the constricted training set with centroids. This technique is amalgamated with AkNNclassifier that was prearranged with text mining similarity measures. Eventually, significantweights and ranks were assigned to each object in the new training set based upon their accessory towards the object in testing set. Experiments conducted on Reuters-21578 a UCI benchmark text mining data set, and comparisons with traditional kNNclassifier designates the referredmethod yieldspreeminentrecitalin both clustering and classification.
Efficient K-medoids clustering algorithm
一种高效的K-medoids聚类算法

夏宁霞,苏一丹,覃希
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.
Document Clustering with K-tree  [PDF]
Christopher M. De Vries,Shlomo Geva
Computer Science , 2010, DOI: 10.1007/978-3-642-03761-0_43
Abstract: This paper describes the approach taken to the XML Mining track at INEX 2008 by a group at the Queensland University of Technology. We introduce the K-tree clustering algorithm in an Information Retrieval context by adapting it for document clustering. Many large scale problems exist in document clustering. K-tree scales well with large inputs due to its low complexity. It offers promising results both in terms of efficiency and quality. Document classification was completed using Support Vector Machines.
Evidential relational clustering using medoids  [PDF]
Kuang Zhou,Arnaud Martin,Quan Pan,Zhun-Ga Liu
Computer Science , 2015,
Abstract: In real clustering applications, proximity data, in which only pairwise similarities or dissimilarities are known, is more general than object data, in which each pattern is described explicitly by a list of attributes. Medoid-based clustering algorithms, which assume the prototypes of classes are objects, are of great value for partitioning relational data sets. In this paper a new prototype-based clustering method, named Evidential C-Medoids (ECMdd), which is an extension of Fuzzy C-Medoids (FCMdd) on the theoretical framework of belief functions is proposed. In ECMdd, medoids are utilized as the prototypes to represent the detected classes, including specific classes and imprecise classes. Specific classes are for the data which are distinctly far from the prototypes of other classes, while imprecise classes accept the objects that may be close to the prototypes of more than one class. This soft decision mechanism could make the clustering results more cautious and reduce the misclassification rates. Experiments in synthetic and real data sets are used to illustrate the performance of ECMdd. The results show that ECMdd could capture well the uncertainty in the internal data structure. Moreover, it is more robust to the initializations compared with FCMdd.
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.
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