Results 161 to 170 of about 4,729 (197)

MapReduce model for k-medoid clustering

2016 International Conference on Data Science and Engineering (ICDSE), 2016
Distributed 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, Shabana Shamsuddin Thaha   +1 more
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Rough K-medoids clustering using GAs

2009 8th IEEE International Conference on Cognitive Informatics, 2009
This 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

Advanced Materials Research, 2012
Because 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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A Parallel K-Medoids Algorithm for Clustering based on MapReduce

2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA), 2016
One 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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Summarizing approach for efficient search by k-medoids method

2015 10th Asian Control Conference (ASCC), 2015
In 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, Hungming Hung, Junzo Watada   +2 more
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Modification to K-Medoids and CLARA for Effective Document Clustering

2017
Document clustering plays an important role in several applications. K-Medoids and CLARA are among the most notable algorithms for clustering. These algorithms together with their relatives have been employed widely in clustering problems. In this paper we present a solution to improve the original K-Medoids and CLARA by making change in the way they ...
Phuong T. Nguyen 0001, Kai Eckert 0001, Azzurra Ragone, Tommaso Di Noia   +3 more
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Semi-Automatic Online Tagging with K-Medoid Clustering

International Journal of Software Engineering and Knowledge Engineering, 2014
Online 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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Data clustering using modified k-medoids algorithm

International Journal of Medical Engineering and Informatics, 2012
This paper proposes a modified k-medoids algorithm for data clustering. This algorithm is applied on seven different datasets including two gene expression datasets and two medical datasets. It improves initial medoids selection and employs updated medoids selection. Records in the datasets are divided into k groups.
T. Geetha, Michael Arock
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Kernel Based K-Medoids for Clustering Data with Uncertainty

2010
Uncertain data is ubiquitous in real-world applications due to various causes. In recent years, clustering uncertain data has been paid more attention by the research community, and the classical clustering algorithms based on partition, density and hierarchy have been extended to handle the uncertain data.
Baoguo Yang, Yang Zhang 0010
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A New and Efficient K-Medoid Algorithm for Spatial Clustering

2005
A new k-medoids algorithm is presented for spatial clustering in large applications. The new algorithm utilizes the TIN of medoids to facilitate local computation when searching for the optimal medoids. It is more efficient than most existing k-medoids methods while retaining the exact the same clustering quality of the basic k-medoids algorithm.
Qiaoping Zhang, Isabelle Couloigner
openaire   +1 more source