Spectral clustering with distinction and consensus learning on multiple views data
نویسندگان
چکیده
منابع مشابه
Entropy-based Consensus for Distributed Data Clustering
The increasingly larger scale of available data and the more restrictive concerns on their privacy are some of the challenging aspects of data mining today. In this paper, Entropy-based Consensus on Cluster Centers (EC3) is introduced for clustering in distributed systems with a consideration for confidentiality of data; i.e. it is the negotiations among local cluster centers that are used in t...
متن کاملMultiple Non-Redundant Spectral Clustering Views
Many clustering algorithms only find one clustering solution. However, data can often be grouped and interpreted in many different ways. This is particularly true in the high-dimensional setting where different subspaces reveal different possible groupings of the data. Instead of committing to one clustering solution, here we introduce a novel method that can provide several non-redundant clust...
متن کاملExtending Consensus Clustering to Explore Multiple Clustering Views
Consensus clustering has emerged as an important extension of the classical clustering problem. Given a set of input clusterings of a given dataset, consensus clustering aims to find a single final clustering which is a better fit in some sense than the existing clusterings. There is a significant drawback in generating a single consensus clustering since different input clusterings could diffe...
متن کاملSpectral Clustering with Two Views
In this paper we develop an algorithm for spectral clustering in the multi-view setting where there are two independent subsets of dimensions, each of which could be used for clustering (or classification). The canonical examples of this are simultaneous input from two sensory modalitites, where input from each sensory modality is considered a view, as well as web pages where the text on the pa...
متن کاملConsensus Clustering + Meta Clustering = Multiple Consensus Clustering
Consensus clustering and meta clustering are two important extensions of the classical clustering problem. Given a set of input clusterings of a given dataset, consensus clustering aims to find a single final clustering which is a better fit in some sense than the existing clusterings, and meta clustering aims to group similar input clusterings together so that users only need to examine a smal...
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: PLOS ONE
سال: 2018
ISSN: 1932-6203
DOI: 10.1371/journal.pone.0208494