ARTÍCULO
TITULO

Incremental Clustering and Dynamic Information Retrieval

Moses Charikar    
Chandra Chekuri    
Tomas Feder    
Rajeev Motwani     

Resumen

No disponible

 Artículos similares

       
 
Yaming Wang, Zhengheng Xu, Wenqing Huang, Yonghua Han and Mingfeng Jiang    
Traditional approaches to modeling and processing discrete pixels are mainly based on image features or model optimization. These methods often result in excessive shrinkage or expansion of the restored pixel region, inhibiting accurate recovery of the t... ver más
Revista: Algorithms

 
Athanasios Davvetas, Iraklis A. Klampanos, Spiros Skiadopoulos and Vangelis Karkaletsis    
Evidence transfer for clustering is a deep learning method that manipulates the latent representations of an autoencoder according to external categorical evidence with the effect of improving a clustering outcome. Evidence transfer?s application on clus... ver más
Revista: Informatics

 
Jian Zhang, Yaozong Pan, Ruili Wang, Yuqiang Fang and Haitao Yang    
Decentralized partially observable Markov decision processes (Dec-POMDPs) are general multi-agent models for planning under uncertainty, but are intractable to solve. Doubly exponential growth of the search space as the horizon increases makes a brute-fo... ver más
Revista: Applied Sciences

 
Nitin patidar,Kushboo patidar    
The management and analysis of big data has been recognized as one of the majority significant promising requirements in recent years. This is because of the pure volume and growing complexity of data creature created or composed. Existing clustering alg... ver más

 
Belete Berhanu, Yilma Seleshi, Solomon S. Demisse and Assefa M. Melesse    
The spatiotemporal variability of a stream flow due to the complex interaction of catchment attributes and rainfall induce complexity in hydrology. Researchers have been trying to address this complexity with a number of approaches; river flow regime is ... ver más
Revista: Water