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adaptive-landmark-based-spectral-clustering-alsc-

In this section, an algorithm for determining the optimal number of clusters (kopt) is proposed. This number (kopt) is used in the clustering of an input big dataset. This algorithm is referred to as the ALSC algorithm. The proposed algorithm comprises three phases and uses parallel processing to speed up its performance. The main advantages of the proposed algorithm are determining the optimal number of clusters and producing high clustering accuracy with the identification of the number of clusters.

advanced-density-peaks

anne-dbscan-demo

Demo of using aNNE similarity for DBSCAN.

automatic-speech-sequence-segmentation

The Main Aim of this project is to segment and cluster an audio sample based on speaker when number of speakers are not known before hand. Main challenge in the process of speaker recognition is separting audio based on speaker.It can enhance the readability of an automatic speech transcription by structuring the audio stream into speaker turns and, when used together with speaker recognition systems, by providing the speaker's true identity.Other challenges are due to multiple speakers present at the time instant

avcmnn-python

An adaptive mutual K-nearest neighbors clustering algorithm based on maximizing mutual information 模式识别2024 Python version implementation of adaptive VCMNN algorithm (AVCMNN) project

borderpeelingclustering

Python Implementation of Border-Peeling clustering

cluster_number_estimation

The Last Leap and the Last Major Leap methods for cluster number estimation

clustering-by-intree-ensemble-pr2020

"Enhancing In-Tree-based Clustering via Distance Ensemble and Kernelization", Teng Qiu, Yongjie Li, in Pattern Recognition, 2020.

clustering-datasets

common-used datasets for clustering

clusters-features-cluster-number-

The Clusters-Features package allows data science users to compute high-level linear algebra operations on any type of data set. It computes approximatively 40 internal evaluation scores such as Davies-Bouldin Index, C Index, Dunn and its Generalized Indexes and many more ! Other features are also available to evaluate the clustering quality.

cnak

Cluster number assisted K-means

cnmbi

CNMBI: a robust method for determining the number of clusters using center pairwise matching and boundary information

color_image_segmentation

Color image segmentatiom using several state-of-the-art clustering algorithms, including GMM, FCM, FSC and MEC.

colour-k-means-clustering

Matlab program that clusters and re-colours each pixel in a colour image to k number of mean colours using the k-means clustering algorithm.

comparativedensitypeaks

The source code of the Comparative Density Peaks algorithm.

covid-19

Novel Coronavirus (COVID-19) Cases, provided by JHU CSSE

covid-19-tourism-effect

Determine if tourism had an impact on the number of COVID-19 cases in Hawaii. Find any possible information in regards to COVID-19 in Hawaii, with a primary focus on trans-pacific travelers. Find possible trends through the use of K-Means Clustering.

cstab

Selection of Number of Clusters via Resampled Normalized Cluster Stability

dbcv-cluster-number-determination-

Python implementation of Density-Based Clustering Validation

deepdpm

"DeepDPM: Deep Clustering With An Unknown Number of Clusters" [CVPR 2022]

demos

TKDE2023 第一章节对比算法Clustering by pruning a density-boosting cluster tree of density mounts---DEnsity MOuntains Separation clustering algorithm

dlore-dp

The matlab code and synthetic data sets for Dense Members of Local Cores-based Density Peaks Clustering Algorithm

dpa

The DPA package is the scikit-learn compatible implementation of the Density Peaks Advanced clustering algorithm. The algorithm provides robust and visual information about the clusters, their statistical reliability and their hierarchical organization.

dpc-dbfn

Density Peaks Clustering Based on Density Backbone and Fuzzy Neighborhood

dpc-knn-pca

We implemented the algorithm from paper"Study on density peaks clustering based on k-nearest neighbors and principal component analysis"

dpcv

Ding, S., Du, W., Li, C. et al. Density peaks clustering algorithm based on improved similarity and allocation strategy. Int. J. Mach. Learn. & Cyber. 14, 1527–1542 (2023). https://doi.org/10.1007/s13042-022-01711-7

dsc-net

A Pytorch implementation of DSC-Net (Deep subspace clustering networks, NIPS17)

externalvalidity

This package contains the code for calculating external clustering validity indices in Spark. The package includes Chi Index among others.

extreme-clustering

extreme clustering– A clustering method via density extreme points

fast-ldp-mst-clustering-ieee-tkde-2022

Fast LDP-MST: an efficient density-based clustering method for large-size datasets (Teng Qiu, Yongjie Li, IEEE Transactions on Knowledge and Data Engineering, 2022)