Results 131 to 140 of about 9,019,139 (162)

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
openaire   +5 more sources

Some of the next articles are maybe not open access.

Related searches:

Convex fuzzy k-medoids clustering

Fuzzy Sets and Systems, 2020
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Daniel Nobre Pinheiro, Daniel Aloise, Simon J. Blanchard   +2 more
openaire   +2 more sources

A genetic k-medoids clustering algorithm

Journal of Heuristics, 2006
We propose a hybrid genetic algorithm for k-medoids clustering. A novel heuristic operator is designed and integrated with the genetic algorithm to fine-tune the search. Further, variable length individuals that encode different number of medoids (clusters) are used for evolution with a modified Davies-Bouldin index as a measure of the fitness of the ...
Weiguo Sheng 0001, Xiaohui Liu 0001
openaire   +2 more sources

Video summarization by k-medoid clustering

Proceedings of the 2006 ACM symposium on Applied computing, 2006
In this paper, we propose a video summarization algorithm by multiple extractions of key frames in each shot. This algorithm is based on the k-medoid clustering algorithms to find the best representative frame for each video shot. This algorithm, which is applicable to all types of descriptors, consists of extracting key frames by similarity clustering
Youssef Hadi, Fedwa Essannouni, Rachid Oulad Haj Thami   +2 more
openaire   +1 more source

Variance enhanced K-medoid clustering

Expert Systems with Applications, 2011
This 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

Clustering Uncertain Data Via K-Medoids

2008
Uncertain 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, Giovanni Ponti, Andrea Tagarelli   +2 more
openaire   +4 more sources

K-Medoids Clustering and Fuzzy Sets for Isolation Forest

2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2021
Capturing 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, Adam Kiersztyn, Witold Pedrycz, Marcin Badurowicz, Dariusz Czerwinski, Jerzy Montusiewicz   +5 more
openaire   +1 more source

Privacy preserving K-Medoids clustering

2007 IEEE International Conference on Systems, Man and Cybernetics, 2007
Privacy 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   +2 more sources

The Application of K-Medoids and PAM to the Clustering of Rules

2004
Earlier 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., Richards, Graeme, Rayward-Smith, Vic J.   +2 more
openaire   +3 more sources

Multiple Cartesian K-Medoids for a Fine Quantization

2016 IEEE 22nd International Conference on Parallel and Distributed Systems (ICPADS), 2016
K-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, Lihua Tian, Shanmin Pang, Chen Li   +3 more
openaire   +1 more source