Cluster ensembles have been shown to be better than any standard clustering algorithm at
improving accuracy and robustness across different data collections. This meta-learning …

Electrocardiography is the gold standard technique for detecting abnormal heart conditions.
Automatic detection of electrocardiogram (ECG) abnormalities helps clinicians analyze the …

Many successful algorithms for analyzing ECG signals leverage data-driven models that are
learned for each specific user. Unfortunately, a few algorithmic challenges are still to be …

Cancer subtype analysis, as an extension of cancer diagnosis, can be regarded as a
consensus clustering problem. This analysis is beneficial for providing patients with more …

Because of user movements and activities, heartbeats recorded from wearable devices
typically feature a large degree of variability in their morphology. Learning problems, which …

With the rise of smart-watches and other wearables, off-the-person electrocardiography is
gaining momentum as high-quality Lead 1 ECG signals can now be acquired from a …

The problem of monitoring a datastream and detecting whether the data generating process
changes from normal to novel and possibly anomalous conditions has relevant applications …

Because of user movements and activities, heartbeats recorded from wearable devices
typically feature a large degree of variability in their morphology. Learning problems, which …

U ovom radu prikazana je analiza i izdvajanje obeležja sa histopatoloških slika tumora
dojke kako bi se postiglo njihovo klasterovanje na benigne i maligne uzorke. Korišćeno je …

abstract Cluster ensembles have been shown to be better than any standard clustering
algorithm at improving accuracy and robustness across different data collections. This meta …