Ensemble clustering for graphs: comparisons and applications
نویسندگان
چکیده
منابع مشابه
Informatik Ensemble and Constrained Clustering with Applications
The main focus of this thesis concerns the further developments in the areas of ensemble and constrained clustering. The goal of the proposed methods is to address clustering problems, in which the optimal clustering method is unknown. Additionally, by means of pairwise linkage constraints, it is possible to aggregate extra information to the clustering framework. Part I investigates the concep...
متن کاملWeighted Ensemble Clustering for Increasing the Accuracy of the Final Clustering
Clustering algorithms are highly dependent on different factors such as the number of clusters, the specific clustering algorithm, and the used distance measure. Inspired from ensemble classification, one approach to reduce the effect of these factors on the final clustering is ensemble clustering. Since weighting the base classifiers has been a successful idea in ensemble classification, in th...
متن کاملAn Ensemble Approach for Clustering Scale-Free Graphs
Several real-world networks of interest, such as social and biological networks, are modular in nature. Most of these networks also possess the scale-free property, which makes the task of detecting and isolating communities from these networks difficult. The application of traditional clustering algorithms on these networks has not yielded a great deal of success. In this paper, we apply an en...
متن کاملA new ensemble clustering method based on fuzzy cmeans clustering while maintaining diversity in ensemble
An ensemble clustering has been considered as one of the research approaches in data mining, pattern recognition, machine learning and artificial intelligence over the last decade. In clustering, the combination first produces several bases clustering, and then, for their aggregation, a function is used to create a final cluster that is as similar as possible to all the cluster bundles. The inp...
متن کاملAn ensemble-clustering-based distance metric and its applications
A distance metric learned from data reflects the actual similarity between objects better than the geometric distance. So, in this paper, we propose a new distance that is based on clustering. Because objects belonging to the same cluster usually share some common traits even though their geometric distance might be large. Thus, we perform several clustering runs to yield an ensemble of cluster...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Applied Network Science
سال: 2019
ISSN: 2364-8228
DOI: 10.1007/s41109-019-0162-z