hj-n/btw-dataset-internal-measures

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MIT License
Created over 2 years ago
Updated about 2 years ago

hj-n/btw-dataset-internal-measures

hj-n

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The implementation of between dataset internal measures

cpp_4_undergraduates

Learning references : cpp programming & fundamental CS tools for undergraduates

c_math_viewer

Parses, and hovers math formula of c mathematical library functions

zadu

A Python Library for Evaluating the Reliability of Dimensionality Reduction Embeddings

steadiness-cohesiveness

Quality Metrics for evaluating the inter-cluster reliability of Multidimensional Projections

snc-reliability-map

Visualizing the distortion measured by Steadiness & Cohesiveness

labeled-datasets

96 benchmark datasets for external clustering validation with class-label matching score

gpu-knn-snn-graph

k-nearest neighbor and Shared-nearest neighbors graph construction on GPU (with pycuda + faiss)

multid-brushing

Distortion-aware brushing for exploring and analyzing multidimensional projection

ltnc

Label-level Trustworthiness & Continuity

Jupyter Notebook

cluster-ambiguity

Visual Quality Measure for estimating cluster ambiguity

Jupyter Notebook

mdp_comp_preference_survey

umato_exp

UMATO experiment

md-hierarchical-parser

Markdown parser with the support for hierarchical heading structure

clams

CLAMS: A Cluster Ambiguity Measure for Estimating Perceptual Variability in Visual Clustering

cluster-ambiguity-main-study

nonlinear-storyworld

distortion-aware-brushing-backend

Server code for distortion-aware-brushing

infovis-assns

Assignments for Information Visualization & Visual Analytics (Fall 2021)

cluster-mining-for-memory-network

Jupyter Notebook

ccma

Class-Cluster Matching Analysis

Jupyter Notebook

vivim_plus

Convienient, light, simple vim plugin for IntelliJ IDEA

general-ambiguity

Jupyter Notebook

snu-infovis-assn-2-skeleton

md-brushing

Implementation / Demo of Multidimensional Brushing Methods ([Martin et al. 95], [Aupetit et al. 14] [Novotony and Hauser, 06])

multid-brush-demo

clm

Adjusted Internal Validation Measures