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Contrastive hierarchical clustering
Deep clustering has been dominated by flat models, which split a dataset into a predefined
number of groups. Although recent methods achieve an extremely high similarity with the …
number of groups. Although recent methods achieve an extremely high similarity with the …
Pharmacoprint: A combination of a pharmacophore fingerprint and artificial intelligence as a tool for computer-aided drug design
Structural fingerprints and pharmacophore modeling are methodologies that have been
used for at least 2 decades in various fields of cheminformatics, from similarity searching to …
used for at least 2 decades in various fields of cheminformatics, from similarity searching to …
Clustered distribution of natural product leads of drugs in the chemical space as influenced by the privileged target-sites
Some natural product leads of drugs (NPLDs) have been found to congregate in the
chemical space. The extent, detailed patterns and mechanisms of this congregation …
chemical space. The extent, detailed patterns and mechanisms of this congregation …
Determining the optimum number of clusters in hierarchical clustering using Pseudo-F
Poverty refers to the condition where a person cannot meet the basic necessities based on
the minimum living standards. Statistics Indonesia proxied an increase in the poverty rate in …
the minimum living standards. Statistics Indonesia proxied an increase in the poverty rate in …
General split gaussian cross–entropy clustering
Robust mixture models approaches, which use non-normal distributions have recently been
upgraded to accommodate asymmetric data. In this article we propose a new method based …
upgraded to accommodate asymmetric data. In this article we propose a new method based …
The choice of an appropriate information dissimilarity measure for hierarchical clustering of river streamflow time series, based on calculated Lyapunov exponent and …
The purpose of this paper was to choose an appropriate information dissimilarity measure
for hierarchical clustering of daily streamflow discharge data, from twelve gauging stations …
for hierarchical clustering of daily streamflow discharge data, from twelve gauging stations …
Average information content maximization—a new approach for fingerprint hybridization and reduction
Fingerprints, bit representations of compound chemical structure, have been widely used in
cheminformatics for many years. Although fingerprints with the highest resolution display …
cheminformatics for many years. Although fingerprints with the highest resolution display …
Fast entropy clustering of sparse high dimensional binary data
We introduce Sparse Entropy Clustering (SEC) which uses minimum entropy criterion to split
high dimensional binary vectors into groups. The idea is based on the analogy between …
high dimensional binary vectors into groups. The idea is based on the analogy between …
CFam: a chemical families database based on iterative selection of functional seeds and seed-directed compound clustering
Similarity-based clustering and classification of compounds enable the search of drug leads
and the structural and chemogenomic studies for facilitating chemical, biomedical …
and the structural and chemogenomic studies for facilitating chemical, biomedical …
Customer profiling and purchase decision influencers: an empirical case study from the management consulting industry
V Kivistö - 2024 - lutpub.lut.fi
Customer profiling and assessing the influencers of business-to-business purchase
decisions can clarify who a company's core customers are and how sales and marketing can …
decisions can clarify who a company's core customers are and how sales and marketing can …