Clustering Algorithms Evaluation

7 papers with code • 10 benchmarks • 14 datasets

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Most implemented papers

Git: Clustering Based on Graph of Intensity Topology

gaozhangyang/git 4 Oct 2021

\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

zhuye88/CDF-TS 5 Oct 2018

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.

An Internal Validity Index Based on Density-Involved Distance

hulianyu/CVDD 22 Mar 2019

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

hclon/Learning-Styles-prediction 12 Oct 2019

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

ShuyueG/CVI_using_DSI 2 Sep 2020

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.

Autoencoder Based Iterative Modeling and Multivariate Time-Series Subsequence Clustering Algorithm

jokonu/abimca 9 Sep 2022

No meaningful and coherent measurement data which could be used for training a CbM model would emerge.