Okay, I need to create a Wikipedia-style document about DBSCAN under the categories of Technology → Data Science → Analysis. Let me start by understanding what DBSCAN is. It's a clustering algorithm, right? So first, I should explain what it does and its purpose.

The user wants an outline with sections like introduction, detailed content with H2 and H3 headings, and references. The document needs to be at least 800 characters, so I need to make sure each section is sufficiently detailed.

Starting with the title: "DBSCAN" as H1. Then an overview section introducing DBSCAN as a clustering algorithm. Next, maybe break down into sections like Core Concepts, Algorithm Steps, Advantages and Disadvantages, Applications, and References.

For core concepts, I should explain terms like density, core points, border points, noise points. Maybe use bullet points for clarity. Then the algorithm steps: initializing parameters (epsilon, min_samples), finding core points, expanding clusters, and labeling noise. Need to make sure each step is clear.

Advantages could include handling arbitrary shapes, not needing to specify cluster count, robustness to outliers. Disadvantages might be sensitivity to parameters, performance with varying densities, computational cost for large datasets.

Applications: customer segmentation, image processing, anomaly detection. Maybe give examples in each area.

References should list authoritative sources like the original paper by Ester et al., scikit-learn documentation, and maybe some textbooks or articles.

I need to check if I'm using technical terms correctly. For example, "density-based" is key here. Also, make sure explanations are concise but thorough. Avoid jargon without explanation. Maybe use a table for parameters like epsilon and min_samples with their descriptions.

Wait, the user mentioned markdown formatting with H1, H2, H3. So I need to structure each section properly. Use bold for headings, maybe code blocks if there's any pseudocode or example code. But since it's a general explanation, perhaps not necessary unless specified.

Also, ensure that the content is accurate. For instance, DBSCAN's parameters: epsilon (radius) and min_samples (minimum points in a neighborhood). The algorithm works by expanding clusters from core points, which are surrounded by enough points within epsilon distance.

I should mention how it differs from other clustering algorithms like K-means, which requires specifying the number of clusters. DBSCAN is better for datasets with noise and varying densities.

Check if there's any common pitfalls or tips when using DBSCAN. Maybe in the disadvantages section, talk about choosing appropriate epsilon and min_samples values, which can be challenging.

Make sure each section flows logically. Start with an intro, then core concepts, how it works step by step, pros/cons, applications, and references. Keep paragraphs short for readability.

Also, verify that all information is up-to-date and accurate. The original paper was from 1996, so mention that in the references. Scikit-learn's implementation is a common tool, so include that as a reference.

Avoid any markdown formatting beyond what's specified. Use H1, H2, H3, bullet points, tables if needed. No code blocks unless necessary, but maybe a pseudocode example could be helpful. However, the user didn't specify code examples, so perhaps stick to explanations.

Double-check for clarity and that each part answers potential questions someone might have about DBSCAN. Ensure that technical terms are explained briefly when first introduced.