The Area Under the Curve (AUC) is a commonly used performance measure in the field of supervised learning. Recently, there has been interest in using AUC as a performance measure in unsupervised learning, particularly in cluster analysis. A new measure known as Area Under the Curve for Clustering (AUCC) has been proposed as an internal/relative measure of clustering quality. This article explores the use of AUCC in cluster analysis and discusses its compelling features.
The Basics of Cluster A
Understanding Contextualized Topic Models
In recent years, advancements in machine learning and natural language processing have led to the development of a new approach to analyzing text called Contextualized Topic Models. This approach utilizes neural networks to identify patterns and themes within text based on the context in which the words are used.
How Contextualized Topic Models Work
The approach used by Contextualized Topic Models is based on a Neural-ProdLDA variational autoencoding
Ensemble clustering, also known as consensus clustering, is a method that combines different clustering algorithms in order to produce more accurate results. It has been a popular topic of research in recent years due to its ability to improve the performance of traditional clustering methods. Ensemble clustering is used in numerous fields such as community detection and bioinformatics.
What is clustering?
Before we delve into ensemble clustering, it is important to understand the basics of c
When it comes to analyzing data, it is essential to group similar elements together. Clustering algorithms are used to do just that. FINCH clustering is a popular clustering algorithm that is fast, scalable, and accurate.
The Basics of FINCH Clustering
FINCH clustering stands for Fast INcremental Clustering Hierarchy. It is an unsupervised learning algorithm, which means it learns patterns and structures from data on its own without the need for explicit instruction. It is used to cluster dat
HRI Pipeline: An Introduction
Human-Robot Interaction, commonly known as HRI, is an important and growing field. It involves the interaction between humans and robots in various tasks, such as caregiving, education, entertainment, and more. However, the development of an efficient HRI system is a complex task that involves different aspects, including recognition, detection, and learning. The HRI pipeline is a framework that addresses these issues for natural, heterogeneous, and multimodal HRI.
k-Means Clustering: An Overview
k-Means Clustering is a type of algorithm used in machine learning that helps classify data into different groups based on their similarity to one another. By dividing a training set into k different clusters, k-Means Clustering can assist in finding patterns and trends within large datasets. This algorithm is commonly used in fields such as marketing, finance, and biology to group together similar data points and better understand the relationships between them.
Spectral clustering is a technique used to separate data points into clusters based on the similarity of the points using a similarity matrix. The process involves constructing a similarity matrix, calculating the graph Laplacian, and applying eigen-decomposition to the graph Laplacian. However, conventional spectral clustering is not feasible for large-scale clustering tasks due to the significant computational resources it requires.
What is Large-scale Spectral Clustering?
Large-scale spect
Clustering is a technique that helps us group similar items together.
Imagine you have a bag of colorful candies, and you want to organize them by color.
You would naturally group the red candies together, the blue candies together, and so on. Clustering algorithms do something similar, but with data points instead of candies.
One such algorithm is called "Mean Shift Clustering," and in this article, we'll explore how it works in a simple and intuitive way.
Mean shift
Mean shift is based o
The Self-Organizing Map (SOM) is a computational technique that enables visualization and analysis of high-dimensional data. It is popularly known as Kohonen network, named after its inventor, Teuvo Kohonen, who first introduced the concept in 1982.
How does SOM work?
At its core, SOM is a type of artificial neural network that represents data in a two-dimensional or three-dimensional map. It does so by mapping high-dimensional inputs to a low-dimensional space. In other words, it is a method
What is SCAN-Clustering?
SCAN-Clustering is an innovative approach to grouping images in a way that is semantically meaningful. This means that the groups are created based on common themes or ideas within the images rather than random groupings. The unique part of SCAN-Clustering is that it can do this without any prior knowledge about what the images represent. It can also do this in an unsupervised way, meaning that there is no need for human input or annotations.
How does SCAN-Clustering
Spectral clustering is a method used for clustering data points together based on their similarities. It is becoming increasingly popular in the field of machine learning because it is very effective at dealing with datasets that are not easily separable.
What is Spectral Clustering?
Spectral clustering is a method used for clustering data points together based on their similarities. It is based on the eigenvalues and eigenvectors of a matrix called the graph Laplacian, which is used to repre
**SCCL: Supporting Clustering with Contrastive Learning**
Clustering is a process used in unsupervised machine learning to group data points with similar characteristics together. By clustering, we can divide a large dataset into smaller subsets that share common features. Clustering is useful in many fields, including marketing, healthcare, and biology.
Supporting Clustering with Contrastive Learning, or SCCL, is a framework to improve unsupervised clustering performance using contrastive lea
Deep Manifold Attributed Graph Embedding (DMAGE) is a novel graph embedding framework that aims to tackle the challenge of unsupervised attributed graph representation learning, which requires both structural and feature information to be represented in the latent space. Existing methods can face issues with oversmoothing and cannot directly optimize representation, thus limiting their applications in downstream tasks. In this article, we will discuss the DMAGE framework and how it can be used t