Ensemble clustering in the belief functions framework
نویسندگان
چکیده
منابع مشابه
Ensemble clustering in the belief functions framework
In this paper, belief functions, defined on the lattice of intervals partitions of a set of objects, are investigated as a suitable framework for combining multiple clusterings. We first show how to represent clustering results as masses of evidence allocated to sets of partitions. Then a consensus belief function is obtained using a suitable combination rule. Tools for synthesizing the results...
متن کاملA New Clustering Ensemble Framework
A new criterion for clusters validation is proposed in the paper and based on the new cluster validation criterion a clustering ensmble framework is proposed. The main idea behind the framework is to extract the most stable clusters in terms of the defined criteria. Employing this new cluster validation criterion, the obtained ensemble is evaluated on some well-known and standard data sets. The...
متن کاملNovelty detection in the belief functions framework
The problem of testing whether an observation may be deemed to correspond to a given model is a difficult issue. Variants of the problem have been widely studied in Statistics and Pattern Recognition. We build a solution in the belief function framework and demonstrate its advantages over other approaches in situations where the available information is particularly scarce.
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 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 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: International Journal of Approximate Reasoning
سال: 2011
ISSN: 0888-613X
DOI: 10.1016/j.ijar.2010.04.007