Entropy-based consensus clustering for patient stratification

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...

Entropy-based Consensus Clustering for Patient Stratification Supplementary Information

A Novel Clustering Method for Patient Stratification

1! A Novel Clustering Method for Patient Stratification Hongfu Liu, Rui Zhao, Hongsheng Fang, Feixiong Cheng, Yun Fu & Yang-Yu Liu Department of Electrical and Computer Engineering, Northeastern University, Boston, Massachusetts 02115, USA. Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts 02115, USA. Chu Kochen Honors College, Zh...

Consensus 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...

Performance based stratification and clustering for benchmarking of container terminals

Benchmarking of container terminals is an important issue facing port management. Data envelopment analysis (DEA), which is a multi-factor productivity measurement tool is generally used in assessing the relative efficiency of homogenous units and setting benchmark for inefficient units. Evaluation of container terminals by DEA produces limited set of efficient units which are reference to inef...