scholarly journals GiniClust2: a cluster-aware, weighted ensemble clustering method for cell-type detection

2018 ◽  
Vol 19 (1) ◽  
Author(s):  
Daphne Tsoucas ◽  
Guo-Cheng Yuan
Keyword(s):  
Ensemble Clustering ◽  
Cell Type ◽  
Clustering Method

scholarly journals A cluster-aware, weighted ensemble clustering method for cell-type detection

2018 ◽  
Author(s):  
Daphne Tsoucas ◽  
Guo-Cheng Yuan
Keyword(s):  
Single Cell Analysis ◽  
Fano Factor ◽  
Cell Types ◽  
Large Datasets ◽  
Computational Method ◽  
Cell Analysis ◽  
Ensemble Clustering ◽  
Cell Type ◽  
Very Large Datasets ◽  
Common Cell

ABSTRACTSingle-cell analysis is a powerful tool for dissecting the cellular composition within a tissue or organ. However, it remains difficult to detect rare and common cell types at the same time. Here we present a new computational method, called GiniClust2, to overcome this challenge. GiniClust2 combines the strengths of two complementary approaches, using the Gini index and Fano factor, respectively, through a cluster-aware, weighted ensemble clustering technique. GiniClust2 successfully identifies both common and rare cell types in diverse datasets, outperforming existing methods. GiniClust2 is scalable to very large datasets.

scholarly journals A new ensemble clustering method based on fuzzy cmeans clustering while maintaining diversity in ensemble

2021 ◽  
Vol 17 (4) ◽  
pp. 103-122
Author(s):  
fatemeh najafi ◽  
hamid parvin ◽  
kamal mirzaei ◽  
samad nejatiyan ◽  
seyede vahideh rezaie ◽  
...  
Keyword(s):  
Ensemble Clustering ◽  
Clustering Method

scholarly journals Clustering in Aggregated User Profiles Across Multiple Social Networks

2017 ◽  
Vol 7 (6) ◽  
pp. 3692
Author(s):  
Charu Virmani ◽  
Anuradha Pillai ◽  
Dimple Juneja
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Similarity Measure ◽  
Online Social Networks ◽  
Vital Role ◽  
User Profiles ◽  
Ensemble Clustering ◽  
Clustering Method ◽  
A Value

A social network is indeed an abstraction of related groups interacting amongst themselves to develop relationships. However, toanalyze any relationships and psychology behind it, clustering plays a vital role. Clustering enhances the predictability and discoveryof like mindedness amongst users. This article’s goal exploits the technique of Ensemble K-means clusters to extract the entities and their corresponding interestsas per the skills and location by aggregating user profiles across the multiple online social networks. The proposed ensemble clustering utilizes known K-means algorithm to improve results for the aggregated user profiles across multiple social networks. The approach produces an ensemble similarity measure and provides 70% better results than taking a fixed value of K or guessing a value of K while not altering the clustering method. This paper states that good ensembles clusters can be spawned to envisage the discoverability of a user for a particular interest.

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