Results 11 to 20 of about 9,019,139 (162)
A hybrid heuristic for the k-medoids clustering problem [PDF]
Proceedings of the 14th annual conference on Genetic and evolutionary computation, 2012 Clustering is an important tool for data analysis, since it allows the exploration of datasets with no or very little prior information. Its main goal is to group a set of data based on their similarity (dissimilarity). A well known mathematical formulation for clustering is the k-medoids problem.
Mariá Cristina Vasconcelos Nascimento +2 more
openaire +4 more sources
Fast $K$-Medoids With the $l_{1}$-Norm [PDF]
IEEE Transactions on Artificial Intelligence, 2023 K-medoids clustering is one of the most popular techniques in exploratory data analysis. The most commonly used algorithms to deal with this problem are quadratic on the number of instances, n, and usually the quality of the obtained solutions strongly depends upon their initialization phase.
Marco Capó +2 more
openaire +3 more sources
IMPLEMENTATION OF K-MEDOIDS AND K-PROTOTYPES CLUSTERING FOR EARLY DETECTION OF HYPERTENSION DISEASE [PDF]
BarekengHypertension is a serious concern because of its significant impact on public health, especially in the context of lifestyle changes and specific health conditions. One method for grouping patients based on complex clinical data is the Clustering method.
Hardianti Hafid, Selvi Annisa
doaj +2 more sources
Optimasi Centroid Awal Algoritma K-Medoids Menggunakan Particle Swarm Optimization Untuk Segmentasi Customer [PDF]
Techno.ComCustomer segmentation is an important strategy in a company, it affects good customer relationships which will result in increased profits. Grouping customers in data mining can use several algorithms, but K-Medoids is the right choice because it can ...
Danang Bagus Wijaya +2 more
doaj +2 more sources
Local multiple orientations estimation using k-medoids [PDF]
2010 IEEE International Conference on Image Processing, 2010 Estimation of local multiple orientations plays an important role in many image processing and computer vision tasks. It has been shown that the detection of orientations in an image patch corresponds to fitting multiple axes to its Fourier transform.
Zhanghui Kuang +2 more
openaire +5 more sources
OneBatchPAM: A Fast and Frugal K-Medoids Algorithm
Proceedings of the AAAI Conference on Artificial IntelligenceThis paper proposes a novel k-medoids approximation algorithm to handle large-scale datasets with reasonable computational time and memory complexity. We develop a local-search algorithm that iteratively improves the medoid selection based on the estimation of the k-medoids objective. A single batch of size m
Antoine de Mathelin +4 more
openaire +4 more sources
JISKA (Jurnal Informatika Sunan Kalijaga), 2022 Universities as educational institutions have very large amounts of academic data which may not be used properly. The data needs to be analyzed to produce information that can map the distribution of students.
Qomariyah, Maria Ulfah Siregar
doaj +2 more sources
Perbandingan Algoritma K-Means dan K-Medoids untuk Pengelompokan Daerah Produksi Kakao [PDF]
, 2022 Cocoa is one of the leading commodities from the plantation sector, even cocoa production is considered capable of increasing the country's foreign exchange.
Hermatyar, Arudji +3 more
core +1 more source
Barekeng, 2023 Cluster analysis is a technique for grouping objects in a database based on their similar characteristics. The grouping results are said to be good if each cluster is homogeneous, and can be validated using the silhouette coefficient test.
Taufiq Akbar +2 more
doaj +1 more source
CoRR, 2016 We run experiments showing that algorithm clarans (Ng et al., 2005) finds better K-medoids solutions than the Voronoi iteration algorithm. This finding, along with the similarity between the Voronoi iteration algorithm and Lloyd's K-means algorithm, suggests that clarans may be an effective K-means initializer.
Newling, James, Fleuret, Francois
openaire +4 more sources