Results 71 to 80 of about 9,019,139 (162)
An Affinity Propagation-Based Self-Adaptive Clustering Method for Wireless Sensor Networks
Sensors, 2019 A wireless sensor network (WSN) is an essential component of the Internet of Things (IoTs) for information exchange and communication between ubiquitous smart objects.
Jin Wang +4 more
doaj +1 more source
K-Medoid Clustering for Heterogeneous DataSets
Procedia Computer Science, 2015 AbstractRecent years have explored various clustering strategies to partition datasets comprising of heterogeneous domains or types such as categorical, numerical and binary. Clustering algorithms seek to identify homogeneous groups of objects based on the values of their attributes.
Harikumar, Sandhya, PV, Surya
openaire +1 more source
A New Social Life Cycle Assessment Framework: An AI‐Driven Optimization Approach and Application
Sustainable Development, EarlyView.ABSTRACT Social life cycle assessment (SLCA) has increasingly been acknowledged as a tool for assessing the societal impacts of services and products. However, the fragmented nature of current approaches makes it difficult to compare this concept and its practical applications.
Flavio Vassallo Mattos +3 more
wiley +1 more source
A Comparative Analysis of K – Means And K – Medoids for Smartphone Clustering
Jurnal SisfokomThe rapid growth of the smartphone industry has increased market complexity, making objective segmentation more challenging. The objective of this study is to conduct a comparative evaluation of the K - Means and K - Medoids methods in grouping ...
M. Arif Fadillah +2 more
doaj +1 more source
Robust Multi‐Source Batch Normalisation for Test‐Time Batch Adaptation
CAAI Transactions on Intelligence Technology, EarlyView.ABSTRACT Test‐Time Batch Adaptation (TTBA) aims to adapt a pre‐trained source model to small, unlabelled target batches at test time. The TTBA methods focus on adapting the pre‐trained model or the target data in a one‐to‐one alignment paradigm. However, these one‐to‐one alignment paradigms assume that the source domain may share the same knowledge ...
Xinlin Xiao +3 more
wiley +1 more source
Perbandingan Analisis Segmen Pelanggan Perusahaan Logistik Dengan Clustering K-Means dan K-Medoids [PDF]
Hasil analisis menunjukkan perbedaan distribusi dan karakteristik masing-masing cluster antara algoritma K-Means dan K-Medoids. K-Means menghasilkan cluster dengan variasi yang lebih besar, sedangkan K-Medoids menghasilkan cluster yang lebih terfokus ...
Tesalonika, Tesalonika
core +1 more source
Kernel $k$-Medoids as General Vector Quantization
2025 IEEE International Conference on Quantum Artificial Intelligence (QAI)Vector Quantization (VQ) is a widely used technique in machine learning and data compression, valued for its simplicity and interpretability. Among hard VQ methods, $k$-medoids clustering and Kernel Density Estimation (KDE) approaches represent two prominent yet seemingly unrelated paradigms -- one distance-based, the other rooted in probability ...
Thore Gerlach +2 more
openaire +2 more sources
Integrative Zoology, EarlyView.We performed 16S rDNA sequencing and metabolite profiling for three sympatric lizard species—Teratoscincus roborowskii, Phrynocephalus axillaris, and Eremias roborowskii—and compared their goblet cell and enzyme activities in the digestive tract. Our study suggests that the dietary niche may promote divergence or convergence of microbiota across host ...
Yi Yang, Ziyi Wang, Ruichen Wu
wiley +1 more source
Identification of groups in k-medoids clustering approach.
, 2014 Identification of groups in k-medoids clustering approach.
Ian S. McRae (396921) +5 more
core +1 more source
International Journal of Distributed Sensor Networks, 2018 Rapid growth of machine-type communications devices challenges the future network with a significant aggregated data traffic. Distributed source coding is a promising technique that compresses data sources and decreases required aggregated data ...
Wen Wang +3 more
doaj +1 more source