Holistic network virtualization and pervasive network intelligence for 6G
In this tutorial paper, we look into the evolution and prospect of network architecture and
propose a novel conceptual architecture for the 6th generation (6G) networks. The proposed …
propose a novel conceptual architecture for the 6th generation (6G) networks. The proposed …
Ensemble learning: A survey
O Sagi, L Rokach - Wiley interdisciplinary reviews: data mining …, 2018 - Wiley Online Library
Ensemble methods are considered the state‐of‐the art solution for many machine learning
challenges. Such methods improve the predictive performance of a single model by training …
challenges. Such methods improve the predictive performance of a single model by training …
From clustering to clustering ensemble selection: A review
Clustering, as an unsupervised learning, is aimed at discovering the natural groupings of a
set of patterns, points, or objects. In clustering algorithms, a significant problem is the …
set of patterns, points, or objects. In clustering algorithms, a significant problem is the …
A comprehensive survey of clustering algorithms
Data analysis is used as a common method in modern science research, which is across
communication science, computer science and biology science. Clustering, as the basic …
communication science, computer science and biology science. Clustering, as the basic …
[图书][B] Advances in K-means clustering: a data mining thinking
J Wu - 2012 - books.google.com
Nearly everyone knows K-means algorithm in the fields of data mining and business
intelligence. But the ever-emerging data with extremely complicated characteristics bring …
intelligence. But the ever-emerging data with extremely complicated characteristics bring …
A survey of clustering ensemble algorithms
S Vega-Pons, J Ruiz-Shulcloper - International Journal of Pattern …, 2011 - World Scientific
Cluster ensemble has proved to be a good alternative when facing cluster analysis
problems. It consists of generating a set of clusterings from the same dataset and combining …
problems. It consists of generating a set of clusterings from the same dataset and combining …
Clustering aggregation
We consider the following problem: given a set of clusterings, find a single clustering that
agrees as much as possible with the input clusterings. This problem, clustering aggregation …
agrees as much as possible with the input clusterings. This problem, clustering aggregation …
A survey of evolutionary algorithms for clustering
ER Hruschka, RJGB Campello… - IEEE Transactions on …, 2009 - ieeexplore.ieee.org
This paper presents a survey of evolutionary algorithms designed for clustering tasks. It tries
to reflect the profile of this area by focusing more on those subjects that have been given …
to reflect the profile of this area by focusing more on those subjects that have been given …
An evolutionary approach to multiobjective clustering
The framework of multiobjective optimization is used to tackle the unsupervised learning
problem, data clustering, following a formulation first proposed in the statistics literature. The …
problem, data clustering, following a formulation first proposed in the statistics literature. The …
Solving cluster ensemble problems by bipartite graph partitioning
XZ Fern, CE Brodley - Proceedings of the twenty-first international …, 2004 - dl.acm.org
A critical problem in cluster ensemble research is how to combine multiple clusterings to
yield a final superior clustering result. Leveraging advanced graph partitioning techniques …
yield a final superior clustering result. Leveraging advanced graph partitioning techniques …