Correction to: A novel adaptive clustering ensemble method
نویسندگان
چکیده
منابع مشابه
A 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 Method for Clustering
Combination strategies in classification are a popular way of overcoming instabilities in classification algorithms. A direct application of ideas such as “voting” to cluster analysis problems is not possible, as no a priori class information for the patterns is available. We present a methodology for combining ensembles of partitions obtained by clustering, discuss the properties of such combi...
متن کاملApplying Cluster Ensemble to Adaptive Tree Structured Clustering
Adaptive tree structured clustering (ATSC) is our proposed divisive hierarchical clustering method that recursively divides a data set into 2 subsets using self-organizing feature map (SOM). In each partition, the data set is quantized by SOM and the quantized data is divided using agglomerative hierarchical clustering. ATSC can divide data sets regardless of data size in feasible time. On the ...
متن کاملA novel dual energy CT-based attenuation correction method in PET/CT systems: A phantom study
In present PET/CT scanners, PET attenuation correction is performed by relying on the information given by CT scan. In the CT-based attenuation correction methods, dual-energy technique (DECT) is the most accurate approach, which has been limited due to the increasing patient dose. In this feasibility study, we have introduced a new method that can implement dual-en...
متن کاملFrom Ensemble Clustering to Multi-View Clustering
Multi-View Clustering (MVC) aims to find the cluster structure shared by multiple views of a particular dataset. Existing MVC methods mainly integrate the raw data from different views, while ignoring the high-level information. Thus, their performance may degrade due to the conflict between heterogeneous features and the noises existing in each individual view. To overcome this problem, we pro...
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 Machine Learning and Cybernetics
سال: 2018
ISSN: 1868-8071,1868-808X
DOI: 10.1007/s13042-018-0807-8