Results 151 to 160 of about 4,729 (197)
Some of the next articles are maybe not open access.
Variance enhanced K-medoid clustering
Expert Systems with Applications, 2011This paper proposes new variance enhanced clustering methods to improve the popular K-medoid algorithm by adapting variance information in data clustering. Since measuring similarity between data objects is simpler than mapping data objects to data points in feature space, these pairwise similarity based clustering algorithms can greatly reduce the ...
Por-Shen Lai, Hsin-Chia Fu
openaire +1 more source
Multiple Cartesian K-Medoids for a Fine Quantization
2016 IEEE 22nd International Conference on Parallel and Distributed Systems (ICPADS), 2016K-means is a widely used method for the process of vector quantization in image retrieval, and its results will directly affect the subsequent retrieval quality. Although k-means is popular in image retrieval, it has some obvious disadvantages, such as randomness and sensitivity to outliers.
Wei Zhang +3 more
openaire +1 more source
Privacy preserving K-Medoids clustering
2007 IEEE International Conference on Systems, Man and Cybernetics, 2007Privacy is an important issue in the collaborative data mining since privacy concerns may prevent the parties from directly sharing the data and some types of information about the data. How multiple parties collaboratively conduct data mining without breaching data privacy presents a challenge.
openaire +1 more source
K-Medoids Clustering and Fuzzy Sets for Isolation Forest
2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2021Capturing anomalies in data is one of the most important problems in modern data analysis. In recent years, scientists have developed many interesting approaches. One of the leading is the Isolation Forest method, which is based on searching a forest of binary trees.
Pawel Karczmarek +5 more
openaire +1 more source
Clustering Uncertain Data Via K-Medoids
2008Uncertain data are usually represented in terms of an uncertainty region over which a probability density function (pdf) is defined. In the context of uncertain data management, there has been a growing interest in clustering uncertain data. In particular, the classic K-means clustering algorithm has been recently adapted to handle uncertain data ...
Francesco Gullo +2 more
openaire +3 more sources
The Application of K-Medoids and PAM to the Clustering of Rules
2004Earlier research has resulted in the production of an ‘all-rules’ algorithm for data-mining that produces all conjunctive rules of above given confidence and coverage thresholds. While this is a useful tool, it may produce a large number of rules. This paper describes the application of two clustering algorithms to these rules, in order to identify ...
Reynolds, Alan P. +2 more
openaire +2 more sources
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
openaire +1 more source
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
openaire +1 more source
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
openaire +1 more source
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
openaire +2 more sources