Clustering, Definition and K-nearest Neighbors

Data mining

Dataconomy

MARCH 4, 2025

Clustering Clustering groups similar data points based on their attributes. One common example is k-means clustering, which segments data into distinct groups for analysis. Decision trees and K-nearest neighbors (KNN) Both decision trees and KNN play vital roles in classification and prediction.

Data Mining

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Retrieval-Augmented Generation with LangChain, Amazon SageMaker JumpStart, and MongoDB Atlas semantic search

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.

K-nearest Neighbors

K-nearest Neighbors AWS Clustering Database

Machine learning algorithms

Dataconomy

MARCH 28, 2025

Definition and importance of machine learning algorithms The core value of machine learning algorithms lies in their capacity to process and analyze vast amounts of data efficiently. Common types include: K-means clustering: Groups similar data points together based on specific metrics.

Machine Learning

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AWS Machine Learning Blog

NOVEMBER 1, 2024

We tried different methods, including k-nearest neighbor (k-NN) search of vector embeddings, BM25 with synonyms , and a hybrid of both across fields including API routes, descriptions, and hypothetical questions. The request arrives at the microservice on our existing Amazon Elastic Container Service (Amazon ECS) cluster.

Python

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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.

K-nearest Neighbors

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AWS Machine Learning Blog

FEBRUARY 25, 2025

Instead of relying on predefined, rigid definitions, our approach follows the principle of understanding a set. Its important to note that the learned definitions might differ from common expectations. Instead of relying solely on compressed definitions, we provide the model with a quasi-definition by extension.

Algorithm

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Fundamentals of Recommendation Systems

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.,

K-nearest Neighbors

K-nearest Neighbors Clustering Algorithm Deep Learning

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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

K-nearest Neighbors Clustering Deep Learning Deep Learning

Coactive AI’s CEO: quality beats quantity for data selection

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

K-nearest Neighbors Clustering Deep Learning Deep Learning

Coactive AI’s CEO: quality beats quantity for data selection

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

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Image Embedding: Benefits, Use Cases, and Best Practices

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

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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. They have done a very comprehensive study regarding this topic so lot of things we can talk about.

Supervised Learning

Supervised Learning Deep Learning Deep Learning K-nearest Neighbors

Understanding and Building Machine Learning Models

Pickl AI

NOVEMBER 18, 2024

Key steps involve problem definition, data preparation, and algorithm selection. Clustering and dimensionality reduction are common tasks in unSupervised Learning. For example, clustering algorithms can group customers by purchasing behaviour, even if the group labels are not predefined. For unSupervised Learning tasks (e.g.,

Machine Learning

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Mlearning.ai

MAY 23, 2023

There are majorly two categories of sampling techniques based on the usage of statistics, they are: Probability Sampling techniques: Clustered sampling, Simple random sampling, and Stratified sampling. The K-Nearest Neighbor Algorithm is a good example of an algorithm with low bias and high variance.

Data Science

Data Science Decision Trees Machine Learning Machine Learning

Feature vectors

Dataconomy

MARCH 7, 2025

Definition and structure of feature vectors A feature vector contains numerical values that represent the attributes of an observed phenomenon. This can be useful in clustering algorithms where distance metrics help define groups. Each feature corresponds to a specific measurable element, allowing for detailed comparative analysis.

K-nearest Neighbors

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Machine learning algorithms

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Feature vectors

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