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Ball K-Medoids: Faster and Exacter
2021Cluster analysis can be viewed as a result of the natural evolution of the vast amount of data from daily life, and can discover invisible feature information to contribute to the analysis. K-means algorithm is one of the wide data clustering methods in a variety of real-world applications thanks to its simpleness.
Qiao Peng +5 more
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Parallelization of K-medoid clustering algorithm
2013 5th International Conference on Information and Communication Technology for the Muslim World (ICT4M), 2013This paper presents an approach for paralleling K-medoid clustering algorithm. The K-medoid algorithm will be divided into tasks, which will be mapped into multiprocessor system. The control structure for the way of expressing the tasks in parallel form and the communication model that satisfied the mechanism for interaction between these tasks is ...
W. Aljoby, K. Alenezi
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Rough K-medoids clustering using GAs
2009 8th IEEE International Conference on Cognitive Informatics, 2009This paper proposes a medoid based variation of rough K-means algorithm. The variation can be especially useful for a more efficient evolutionary implementation of rough clustering. Experimentation with the rough K-means algorithm has shown that it provides a reasonable set of lower and upper bounds for a given dataset. However, rough K-means algorithm
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An improved k-medoids clustering algorithm
2010 The 2nd International Conference on Computer and Automation Engineering (ICCAE), 2010In this paper, we present an improved k-medoids clustering algorithm based on CF-Tree. The algorithm based on the clustering features of BIRCH algorithm, the concept of k-medoids algorithm has been improved. We preserve all the training sample data in an CF-Tree, then use k-medoids method to cluster the CF in leaf nodes of CF-Tree.
null Danyang Cao, null Bingru Yang
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On the k-Medoids Model for Semi-supervised Clustering
2019Clustering is an automated and powerful technique for data analysis. It aims to divide a given set of data points into clusters which are homogeneous and/or well separated. A major challenge with clustering is to define an appropriate clustering criterion that can express a good separation of data into homogeneous groups such that the obtained ...
Rodrigo Randel +3 more
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An Improved K-Medoids Clustering Algorithm
Advanced Materials Research, 2012Because of the traditional K-medoids clustering algorithm the initial clustering center sensitive, the global search ability is poor, easily trapped into local optimal and slow convergent speed; therefore, this article proposes an improved K-medoids clustering algorithm. Differential evolution is a kind of heuristic global search technology population,
Ying Meng, Ke Luo, Jian Hua Liu
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MapReduce model for k-medoid clustering
2016 International Conference on Data Science and Engineering (ICDSE), 2016Distributed and Parallel computing are best alternatives for scalable clustering of huge amount of data with moderate to high dimensions, together with improved speed up. In this paper we address the problem of k-medoid clustering using MapReduce framework for distributed computing on commodity machines to evaluate its efficacy.
Sandhya Harikumar +1 more
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Summarizing approach for efficient search by k-medoids method
2015 10th Asian Control Conference (ASCC), 2015In past days, although we have focused on to collect required data, we can get required information since many data are storage and disclosed. Therefore, it has become a new task to search efficiently required information. Nowadays, the search engine such as Google, Bing and Baidu help us to search information in the internet.
Yoshiyuki Yabuuchi +2 more
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Semi-Automatic Online Tagging with K-Medoid Clustering
International Journal of Software Engineering and Knowledge Engineering, 2014Online tagging is crucial for the acquisition and organization of web knowledge. We present TYG (Tag-as-You-Go) in this paper, a web browser extension for online tagging of personal knowledge on standard web pages. We investigate an approach to combine a K-Medoid-style clustering algorithm with the user input to achieve semi-automatic web page ...
He Hu 0001, Xiaoyong Du 0001
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A Parallel K-Medoids Algorithm for Clustering based on MapReduce
2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA), 2016One of the most important machine learning techniques include clustering of data into different clusters or categories. There are several decent algorithms and techniques that exist to perform clustering on small to medium scale data. In the era of Big Data and with applications being large-scale and data-intensive in nature, there is a significant ...
M. Omair Shafiq, Eric Torunski
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