61 datasets found

Clickstream data is one of the various emerging data types that demands particular attention because there is a notable paucity of statistical learning approaches currently...

Object Cluster Hierarchy (OCH) is a novel variant of Hierarchical Clustering that increasingly gains more interest in the field of Machine Learning.

The dataset is used for clustering simulation output distributions using the regularized Wasserstein distance.

The Image Segmentation dataset is used to evaluate the performance of the ensemble average rule.

The dataset used in this paper for Dominant Set Clustering.

The dataset used in this paper for Dominant Set Clustering.

The dataset used in this paper for Dominant Set Clustering.

The dataset used in this paper for Dominant Set Clustering.

Scene-15 is a dataset of indoor and outdoor environment images, which consists of 4485 images in total and is divided into 15 categories. It is described by 3 features.

Mnist has a training set of 10000 examples, and the first four categories(0-3) of it, i.e., the first 4000 samples form a subset called Mnist4. It is divided into 4 categories...

The dataset used for out-of-distribution detection through soft clustering with non-negative kernel regression.

The dataset used in this paper is a collection of 9600 incomplete data sets, each with 10 variables and 25-200 individuals, with varying levels of missing values.

The MNIST dataset is used to demonstrate the clustering capability of the proposed group-sparse autoencoder.

Breast cancer dataset where mammograms have been labeled independently by three doctors. Ground-truth has been obtained through a biopsy, not available to the algorithm nor the...

Deep embeddings answer one simple question: How similar are two images? Learning these embeddings is the bedrock of verification, zero-shot learning, and visual search.

Clustering is a fundamental task in data analysis. Recently, deep clustering, which derives inspiration primarily from deep learning approaches, achieves state-of-the-art...

The shuttle dataset is used for anomaly clustering and visualization. It contains 9 numerical attributes and 7 classes.

The dataset is used to evaluate the performance of clustering algorithms.

The proposed k-meansNet is used for clustering and has explicit interpretability in structure.

Autoencoders with Intrinsic Dimension Constraints for Learning Low Dimensional Image Representations