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Clustering algorithms

Dataconomy

Clustering algorithms play a vital role in the landscape of machine learning, providing powerful techniques for grouping various data points based on their intrinsic characteristics. What are clustering algorithms? Key criteria include: The number of clusters data points can belong to.

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Research: A periodic table for machine learning

Dataconomy

The idea is deceptively simple: represent most machine learning algorithmsclassification, regression, clustering, and even large language modelsas special cases of one general principle: learning the relationships between data points. A state-of-the-art image classification algorithm requiring zero human labels.

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Density-based clustering

Dataconomy

Density-based clustering stands out in the realm of data analysis, offering unique capabilities to identify natural groupings within complex datasets. What is density-based clustering? This method effectively distinguishes dense regions from sparse areas, identifying clusters while also recognizing outliers.

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What is Discretization in Machine Learning?

Analytics Vidhya

It plays a crucial role in improving data interpretability, optimizing algorithm efficiency, and preparing datasets for tasks like classification and clustering. This article explores data discretisation’s methodologies, benefits, and applications, offering […] The post What is Discretization in Machine Learning?

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K-Means Clustering Algorithm

Data Flair

Program 1 from sklearn.cluster import KMeans import pandas as pd # Sample data data = pd.DataFrame({ "Income": [15000, 16000, 90000, 95000, 60000, 62000,65000,98000,12000], "SpendingScore": [90, 85, 20, 15, 50, 55,54,23,94] }) # Apply K-Means... The post K-Means Clustering Algorithm appeared first on DataFlair.

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Revisiting k-Means: 3 Approaches to Make It Work Better

Flipboard

The k-means algorithm is a cornerstone of unsupervised machine learning, known for its simplicity and trusted for its efficiency in partitioning data into a predetermined number of clusters.

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Hierarchical Clustering in Machine Learning: An In-Depth Guide

Pickl AI

Summary: Hierarchical clustering in machine learning organizes data into nested clusters without predefining cluster numbers. Unlike partition-based methods such as K-means, hierarchical clustering builds a nested tree-like structure called a dendrogram that reveals the multi-level relationships between data points.