Application of the Ensemble Clustering Algorithm in Solving the Problem of Segmentation of Users Taking Into Account Their Loyalty

Author(s):  
Pyotr Bochkaryov ◽  
Anna I. Guseva
2019 ◽  
Author(s):  
William Wong ◽  
Naotsugu Tsuchiya

Evidence accumulation clustering (EAC) is an ensemble clustering algorithm that can cluster data for arbitrary shapes and numbers of clusters. Here, we present a variant of EAC in which we aimed to better cluster data with a large number of features, many of which may be uninformative. Our new method builds on the existing EAC algorithm by populating the clustering ensemble with clusterings based on combinations of fewer features than the original dataset at a time. Our method also calls for prewhitening the recombined data and weighting the influence of each individual clustering by an estimate of its informativeness. We provide code of an example implementation of the algorithm in Matlab and demonstrate its effectiveness compared to ordinary evidence accumulation clustering with synthetic data.


2008 ◽  
Vol 5 (4) ◽  
pp. 350-360 ◽  
Author(s):  
S.M. Hojnacki ◽  
G. Micela ◽  
S.M. LaLonde ◽  
E.D. Feigelson ◽  
J.H. Kastner

2011 ◽  
Vol 16 (1) ◽  
pp. 87-96 ◽  
Author(s):  
Harun Pirim ◽  
Dilip Gautam ◽  
Tanmay Bhowmik ◽  
Andy Perkins ◽  
Burak Ekşioglu ◽  
...  

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