Compared to other clustering techniques, DBSCAN does not require you to explicitly specify how many data clusters to use, explains Dr. James McCaffrey of Microsoft Research in this full-code, ...

This article explains the concept of cosine similarity and how it is used as a metric for evaluation of data points in various applications. Cosine similarity measures the angle between two vectors to ...

Unsupervised learning is a class of machine learning that involves finding patterns in unlabeled data. And clustering is an unsupervised learning algorithm that finds patterns in unlabeled data by ...

The brain functional network extracted from the BOLD signals reveals the correlated activity of the different brain regions, which is hypothesized to underlie the integration of the information across ...

Stroke causes behavioral deficits in multiple cognitive domains and there is a growing interest in predicting patient performance from neuroimaging data using machine learning techniques. Here, we ...

The weighted k-nearest neighbors (k-NN) classification algorithm is a relatively simple technique to predict the class of an item based on two or more numeric predictor variables. For example, you ...

In this article we prove two characterizations of the Euclidean ball: (i) the only convex body in ℝ³ such that every normal plane bisects the volume (or surface area) is the Euclidean ball, (ii) the ...