Fuzheng Liu Examines Transformer-XL Modeling of E-Commerce User Behavior and Real-Time Behavioral Analysis

As e-commerce platforms generate ever-longer streams of user-behavior data, machine-learning methods are increasingly examined for their ability to model how customer interests form and shift over ...
BMJ Global Health

Clustering of countries based on national essential medicines lists: cross-sectional study

Introduction National essential medicines lists (NEMLs) guide medicine selection and procurement and are key tools for ...
The Manila Times

Data Scientist Ke Zhang’s Research Explores Homomorphic Encryption for Privacy-Preserving Marketing

A privacy-preserving marketing framework applies homomorphic encryption to perform machine learning on encrypted consumer data. By combining secure clustering with efficient computation, the study ...
Nature

What is the expectation maximization algorithm?

Probabilistic models, such as hidden Markov models or Bayesian networks, are commonly used to model biological data. Much of their popularity can be attributed to the existence of efficient and robust ...
IEEE

A possibilistic fuzzy c-means clustering algorithm

Abstract: In 1997, we proposed the fuzzy-possibilistic c-means (FPCM) model and algorithm that generated both membership and typicality values when clustering unlabeled data. FPCM constrains the ...
IEEE

Robust k-Means-Type Clustering for Noisy Data

Abstract: Data clustering is a fundamental machine learning task that seeks to categorize a dataset into homogeneous groups. However, real data usually contain noise, which poses significant ...
GitHub

A robust and fast clustering method for amplicon-based studies.

The purpose of swarm is to provide a novel clustering algorithm that handles massive sets of amplicons. Results of traditional clustering algorithms are strongly input-order dependent, and rely on an ...