Resampling Methods for Exploring Cluster Stability

TL;DR: A new algorithm to enhance clustering by any baseline method using bootstrap samples is proposed and a covering point set for each cluster, a concept kin to the confidence interval, is proposed to address the crucial question of whether any cluster is an intrinsic or spurious pattern.

TL;DR: In this paper , the problem of selecting individual clustering methods to produce an ensemble hierarchical clustering method is examined and a subset of optimal methods can reduce the complexity and increase the efficiency of ensemble clustering.

TL;DR: In this paper, Ahmad-Dey distance measure consistently outperformed Gower's distance on mixed and missing datasets, and advanced imputation methods like multiple imputation, which take into consideration the uncertainty inherent in imputation should be explored when clustering missing datasets.

TL;DR: In this article, the authors introduce the current state of science, existing cluster validation indices and propose a practical method to combine them to an individual composite index, using Multi Criteria Decision Analysis (mcda).

TL;DR: A défaut, tout contrevenant s'expose aux sanctions prévues par cette loi, nous déclinons toute responsabilité en la matière.

TL;DR: The main finding from the clustering analysis is that households who buy less fresh produce are those with children – an important insight with significant public health implications.

TL;DR: It is shown that the number of clusters can be reliably determined only when the type of clusters is known and all the components of the approach are carefully chosen, which leads to better results compared to existing stability-based methods.

TL;DR: In this thesis, two new additional random clustering algorithms have been proposed and the aggregation of clustering quality indexes has been examined with different types of simulated and real data sets.