Clustering Algorithms Evaluation
8 papers with code • 10 benchmarks • 13 datasets
Benchmarks
These leaderboards are used to track progress in Clustering Algorithms Evaluation
Datasets
MNIST
Fashion-MNIST
JAFFE
ionosphere
pathbased
iris
97 synthetic datasets
Satimage
Most implemented papers
Git: Clustering Based on Graph of Intensity Topology
\textbf{A}ccuracy, \textbf{R}obustness to noises and scales, \textbf{I}nterpretability, \textbf{S}peed, and \textbf{E}asy to use (ARISE) are crucial requirements of a good clustering algorithm.
CDF Transform-and-Shift: An effective way to deal with datasets of inhomogeneous cluster densities
To match the implicit assumption, we propose to transform a given dataset such that the transformed clusters have approximately the same density while all regions of locally low density become globally low density -- homogenising cluster density while preserving the cluster structure of the dataset.
Clubmark: a Parallel Isolation Framework for Benchmarking and Profiling Clustering Algorithms on NUMA Architectures
There is a great diversity of clustering and community detection algorithms, which are key components of many data analysis and exploration systems.
An Internal Validity Index Based on Density-Involved Distance
One reason is that the measure of cluster separation does not consider the impact of outliers and neighborhood clusters.
A predictive model for the identification of learning styles in MOOC environments
Massive online open course (MOOC) platform generates a large amount of data, which provides many opportunities for studying the behaviors of learners.
An Internal Cluster Validity Index Using a Distance-based Separability Measure
And, to have more CVIs is crucial because there is no universal CVI that can be used to measure all datasets, and no specific method for selecting a proper CVI for clusters without true labels.
The Area Under the ROC Curve as a Measure of Clustering Quality
In particular, we elaborate on the use of AUC as an internal/relative measure of clustering quality, which we refer to as Area Under the Curve for Clustering (AUCC).
Autoencoder Based Iterative Modeling and Multivariate Time-Series Subsequence Clustering Algorithm
jokonu/abimca
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No meaningful and coherent measurement data which could be used for training a CbM model would emerge.
