Data Science Dojo

JANUARY 30, 2024

Exploring Disease Mechanisms : Vector databases facilitate the identification of patient clusters that share similar disease progression patterns. Nearest neighbor search algorithms : Efficiently retrieving the closest patient vec t o r s to a given query.

Database 361

Database K-nearest Neighbors Natural Language Processing Algorithm 361

Towards AI

MAY 1, 2024

We shall look at various types of machine learning algorithms such as decision trees, random forest, K nearest neighbor, and naïve Bayes and how you can call their libraries in R studios, including executing the code. R Studios and GIS In a previous article, I wrote about GIS and R.,

Machine Learning 98

Machine Learning Machine Learning K-nearest Neighbors Decision Trees 98

Flipboard

NOVEMBER 17, 2023

Set up a MongoDB cluster To create a free tier MongoDB Atlas cluster, follow the instructions in Create a Cluster. MongoDB Atlas Vector Search uses a technique called k-nearest neighbors (k-NN) to search for similar vectors. k-NN works by finding the k most similar vectors to a given vector.

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K-nearest Neighbors AWS Clustering Database 170

AWS Machine Learning Blog

FEBRUARY 13, 2025

The following image uses these embeddings to visualize how topics are clustered based on similarity and meaning. You can then say that if an article is clustered closely to one of these embeddings, it can be classified with the associated topic. This is the k-nearest neighbor (k-NN) algorithm.

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AWS K-nearest Neighbors Clustering Algorithm 108

Pickl AI

JANUARY 21, 2025

k-Nearest Neighbors (k-NN) k-NN is a simple algorithm that classifies new instances based on the majority class among its k nearest neighbours in the training dataset. K-Means Clustering K-means clustering partitions data into k distinct clusters based on feature similarity.

Machine Learning 52

Machine Learning Machine Learning Algorithm Decision Trees 52

Towards AI

APRIL 4, 2024

A sector that is currently being influenced by machine learning is the geospatial sector, through well-crafted algorithms that improve data analysis through mapping techniques such as image classification, object detection, spatial clustering, and predictive modeling, revolutionizing how we understand and interact with geographic information.

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K-nearest Neighbors Machine Learning Machine Learning Decision Trees 81

Pickl AI

APRIL 13, 2025

Examples include: Classifying species of plants Categorizing images into animals, vehicles, or landscapes Algorithms like Random Forests, Naive Bayes, and K-Nearest Neighbors (KNN) are commonly used for multi-class classification. Each instance is assigned to one of several predefined categories.

Machine Learning 52

Machine Learning Machine Learning Decision Trees K-nearest Neighbors 52

Towards AI

APRIL 7, 2024

Created by the author with DALL E-3 Statistics, regression model, algorithm validation, Random Forest, K Nearest Neighbors and Naïve Bayes— what in God’s name do all these complicated concepts have to do with you as a simple GIS analyst? Author(s): Stephen Chege-Tierra Insights Originally published on Towards AI.

Machine Learning 105

Machine Learning Machine Learning K-nearest Neighbors Supervised Learning 105

AWS Machine Learning Blog

MARCH 11, 2025

The implementation included a provisioned three-node sharded OpenSearch Service cluster. Retrieval (and reranking) strategy FloTorch used a retrieval strategy with a k-nearest neighbor (k-NN) of five for retrieved chunks. Each provisioned node was r7g.4xlarge, FloTorch used HSNW indexing in OpenSearch Service.

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K-nearest Neighbors AWS Database AI 108

PyImageSearch

SEPTEMBER 16, 2024

Home Table of Contents Credit Card Fraud Detection Using Spectral Clustering Understanding Anomaly Detection: Concepts, Types and Algorithms What Is Anomaly Detection? Spectral clustering, a technique rooted in graph theory, offers a unique way to detect anomalies by transforming data into a graph and analyzing its spectral properties.

Clustering 57

Clustering Algorithm Machine Learning Machine Learning 57

AWS Machine Learning Blog

FEBRUARY 5, 2025

OpenSearch Service then uses the vectors to find the k-nearest neighbors (KNN) to the vectorized search term and image to retrieve the relevant listings. After extensive A/B testing with various k values, OfferUp found that a k value of 128 delivers the best search results while optimizing compute resources.

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IBM Journey to AI blog

DECEMBER 20, 2023

Classification algorithms include logistic regression, k-nearest neighbors and support vector machines (SVMs), among others. K-means clustering is commonly used for market segmentation, document clustering, image segmentation and image compression.

Machine Learning 92

Machine Learning Machine Learning Supervised Learning Clustering 92

AWS Machine Learning Blog

NOVEMBER 13, 2024

To search against the database, you can use a vector search, which is performed using the k-nearest neighbors (k-NN) algorithm. With Amazon OpenSearch Serverless, you don’t need to provision, configure, and tune the instance clusters that store and index your data.

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AWS Database K-nearest Neighbors AI 116

Towards AI

APRIL 14, 2023

The prediction is then done using a k-nearest neighbor method within the embedding space. The feature space reduction is performed by aggregating clusters of features of balanced size. This clustering is usually performed using hierarchical clustering.

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K-nearest Neighbors Algorithm Clustering Support Vector Machines 98

Pickl AI

JULY 17, 2023

There are different kinds of unsupervised learning algorithms, including clustering, anomaly detection, neural networks, etc. The algorithms will perform the task using unsupervised learning clustering, allowing the dataset to divide into groups based on the similarities between images. It can be either agglomerative or divisive.

Machine Learning 52

Machine Learning Machine Learning Clustering K-nearest Neighbors 52

PyImageSearch

JUNE 19, 2023

K-Nearest Neighbor K-nearest neighbor (KNN) ( Figure 8 ) is an algorithm that can be used to find the closest points for a data point based on a distance measure (e.g., Figure 8: K-nearest neighbor algorithm (source: Towards Data Science ). Several clustering algorithms (e.g.,

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K-nearest Neighbors Clustering Algorithm Deep Learning 57

Dataconomy

APRIL 4, 2023

Some of the common types are: Linear Regression Deep Neural Networks Logistic Regression Decision Trees AI Linear Discriminant Analysis Naive Bayes Support Vector Machines Learning Vector Quantization K-nearest Neighbors Random Forest What do they mean? Let’s dig deeper and learn more about them!

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K-nearest Neighbors Decision Trees AI AI 91

Dataconomy

APRIL 4, 2023

Some of the common types are: Linear Regression Deep Neural Networks Logistic Regression Decision Trees AI Linear Discriminant Analysis Naive Bayes Support Vector Machines Learning Vector Quantization K-nearest Neighbors Random Forest What do they mean? Let’s dig deeper and learn more about them!

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K-nearest Neighbors Decision Trees AI AI 91

Mlearning.ai

FEBRUARY 17, 2023

Logistic Regression K-Nearest Neighbors (K-NN) Support Vector Machine (SVM) Kernel SVM Naive Bayes Decision Tree Classification Random Forest Classification I will not go too deep about these algorithms in this article, but it’s worth it for you to do it yourself. It’s a fantastic world, trust me!

Machine Learning 52

Machine Learning Machine Learning K-nearest Neighbors Clustering 52

IBM Journey to AI blog

DECEMBER 19, 2023

Common machine learning algorithms for supervised learning include: K-nearest neighbor (KNN) algorithm : This algorithm is a density-based classifier or regression modeling tool used for anomaly detection. “Means,” or average data, refers to the points in the center of the cluster that all other data is related to.

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Machine Learning Machine Learning Supervised Learning K-nearest Neighbors 82

Towards AI

FEBRUARY 19, 2025

But heres the catch scanning millions of vectors one by one (a brute-force k-Nearest Neighbors or KNN search) is painfully slow. Instead, vector databases rely on Approximate Nearest Neighbors (ANN) techniques, which trade a tiny bit of accuracy for massive speed improvements. 💡 Why?

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Database K-nearest Neighbors Machine Learning Machine Learning 73

Snorkel AI

APRIL 11, 2023

Now the key insight that we had in solving this is that we noticed that unseen concepts are actually well clustered by pre-trained deep learning models or foundation models. And effectively in the latent space, they form kind of tight clusters for these unseen concepts that are very well-connected components. of the unlabeled data.

K-nearest Neighbors 52

K-nearest Neighbors Clustering Deep Learning Deep Learning 52

Snorkel AI

APRIL 11, 2023

Now the key insight that we had in solving this is that we noticed that unseen concepts are actually well clustered by pre-trained deep learning models or foundation models. And effectively in the latent space, they form kind of tight clusters for these unseen concepts that are very well-connected components. of the unlabeled data.

K-nearest Neighbors 52

K-nearest Neighbors Clustering Deep Learning Deep Learning 52

Snorkel AI

APRIL 11, 2023

Now the key insight that we had in solving this is that we noticed that unseen concepts are actually well clustered by pre-trained deep learning models or foundation models. And effectively in the latent space, they form kind of tight clusters for these unseen concepts that are very well-connected components. of the unlabeled data.

K-nearest Neighbors 52

K-nearest Neighbors Clustering Deep Learning Deep Learning 52

Pickl AI

AUGUST 22, 2023

This harmonization is particularly critical in algorithms such as k-Nearest Neighbors and Support Vector Machines, where distances dictate decisions. Scaling steps in as a guardian, harmonizing the scales and ensuring that algorithms treat each feature fairly.

Machine Learning 52

Machine Learning Machine Learning K-nearest Neighbors Algorithm 52

AWS Machine Learning Blog

APRIL 5, 2023

out" embeddings.append(json.load(open(embedding_file))[0]) Create an ML-powered unified search engine This section discusses how to create a search engine that that uses k-NN search with embeddings. This includes configuring an OpenSearch Service cluster, ingesting item embedding, and performing free text and image search queries.

ML 111

ML ML AWS K-nearest Neighbors 111

AWS Machine Learning Blog

JANUARY 6, 2023

OpenSearch Service currently has tens of thousands of active customers with hundreds of thousands of clusters under management processing trillions of requests per month. OpenSearch Service offers the latest versions of OpenSearch, support for 19 versions of Elasticsearch (1.5 Solution overview. Prerequisites.

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AWS ML ML Machine Learning 101

AWS Machine Learning Blog

FEBRUARY 25, 2025

Instead of treating each input as entirely unique, we can use a distance-based approach like k-nearest neighbors (k-NN) to assign a class based on the most similar examples surrounding the input. This doesnt imply that clusters coudnt be highly separable in higher dimensions.

Algorithm 76

Algorithm Machine Learning Machine Learning K-nearest Neighbors 76

AWS Machine Learning Blog

APRIL 19, 2024

This solution includes the following components: Amazon Titan Text Embeddings is a text embeddings model that converts natural language text, including single words, phrases, or even large documents, into numerical representations that can be used to power use cases such as search, personalization, and clustering based on semantic similarity.

AWS 126

AWS ML ML Database 126

Pickl AI

SEPTEMBER 3, 2023

Density-Based Spatial Clustering of Applications with Noise (DBSCAN): DBSCAN is a density-based clustering algorithm. It identifies regions of high data point density as clusters and flags points with low densities as anomalies. Anomalies, being different from normal data, result in higher reconstruction errors.

Machine Learning 52

Machine Learning Machine Learning K-nearest Neighbors Algorithm 52

DagsHub

JUNE 24, 2024

This can lead to enhancing accuracy but also increasing the efficiency of downstream tasks such as classification, retrieval, clusterization, and anomaly detection, to name a few. This can lead to higher accuracy in tasks like image classification and clusterization due to the fact that noise and unnecessary information are reduced.

Clustering 40

Clustering Machine Learning Machine Learning K-nearest Neighbors 40

Snorkel AI

JANUARY 26, 2023

We tackle that by learning these clusters in the foundation models embedding space and providing those clusters as the subgroups—and basically learning a weak supervision model on each of those clusters. So, we propose to do this sort of K-nearest-neighbors-type extension per source in the embedding space.

K-nearest Neighbors 52

K-nearest Neighbors Clustering Computer Science Computer Science 52

Snorkel AI

JANUARY 26, 2023

We tackle that by learning these clusters in the foundation models embedding space and providing those clusters as the subgroups—and basically learning a weak supervision model on each of those clusters. So, we propose to do this sort of K-nearest-neighbors-type extension per source in the embedding space.

K-nearest Neighbors 52

K-nearest Neighbors Clustering Computer Science Computer Science 52

AWS Machine Learning Blog

FEBRUARY 10, 2023

We design a K-Nearest Neighbors (KNN) classifier to automatically identify these plays and send them for expert review. As an example, in the following figure, we separate Cover 3 Zone (green cluster on the left) and Cover 1 Man (blue cluster in the middle).

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ML ML Machine Learning Machine Learning 89

Flipboard

FEBRUARY 7, 2025

Complete the following steps: On the OpenSearch Service console, choose Dashboard under Managed clusters in the navigation pane. In most cases, you will use an OpenSearch Service vector database as a knowledge base, performing a k-nearest neighbor (k-NN) search to incorporate semantic information in the retrieval with vector embeddings.

Database 86

Database AWS Python ML 86

PyImageSearch

OCTOBER 30, 2023

Spotify also establishes a taste profile by grouping the music users often listen into clusters. These clusters are not based on explicit attributes (e.g., text mining, K-nearest neighbor, clustering, matrix factorization, and neural networks). genre, artist, etc.) to train their algorithm. Gosthipaty, S.

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K-nearest Neighbors Algorithm Clustering Machine Learning 70

Mlearning.ai

JULY 6, 2023

The sub-categories of this approach are negative sampling, clustering, knowledge distillation, and redundancy reduction. Some common quantitative evaluations are linear probing , K nearest neighbors (KNN), and fine-tuning. More details of this approach will be described in a different article.

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Supervised Learning Deep Learning Deep Learning K-nearest Neighbors 52