Okay, I need to create a Wikipedia-style document about K-Means clustering in the context of data science. Let me start by understanding the user's requirements.

First, the structure: H1 for the title, an overview section, several sections with H2 and H3 headings, and maybe references or related documents. The content needs to be accurate, objective, detailed enough, easy to read, and use technical terms with explanations. Also, markdown formatting with proper hierarchy, lists, tables, code blocks if needed. The document should be at least 800 characters long.

The main topic is K-Means under Data Science's Analysis category. Let me outline the sections. The title will be "K-평균" (K-Means). Then an introduction explaining what it is. Next, maybe a section on the algorithm's principles, steps involved, applications, advantages and disadvantages, and related methods.

Wait, the user mentioned K-평균 as the topic. I should make sure to cover the basics: definition, how it works, key concepts like centroids, distance metrics, iterations. Also, mention common use cases in data science. Maybe include a step-by-step explanation of the algorithm.

I need to check if there are any technical terms that require explanation. For example, "centroid" or "distance metric." I should define them briefly. Also, maybe a table comparing K-Means with other clustering algorithms like DBSCAN or hierarchical clustering.

Including code examples could be helpful. Maybe a simple Python example using scikit-learn to demonstrate how K-Means is implemented. But since the user didn't specify code, perhaps just mention it as an example without actual code blocks unless necessary.

I should also address common issues with K-Means, like sensitivity to initial centroids and the need for choosing the right number of clusters (K). Maybe discuss methods like the elbow method or silhouette analysis for determining K.

Make sure each section is clear. The introduction should set the stage, then go into details. Use H2 for main sections and H3 for subsections if needed. For example, under "알고리즘 원리," have H3 headings for steps, distance metrics, etc.

Check that all information is accurate. K-Means is an iterative algorithm that partitions data into K clusters based on distances. The steps are: initialize centroids, assign points to nearest centroid, update centroids, repeat until convergence.

Also, mention the mathematical formulas if necessary, but keep it simple. Maybe a formula for Euclidean distance in the distance metrics section.

Avoid being too technical without explanation. For instance, when talking about the cost function (sum of squared distances), explain what it means.

Include applications: customer segmentation, image compression, anomaly detection. These are common use cases.

In the advantages and disadvantages section, list pros like simplicity and efficiency for large datasets, cons like sensitivity to initial centroids and outliers.

For references, maybe cite standard textbooks or reputable sources on data science, but since this is a wiki-style document, perhaps just mention related topics or further reading without specific citations unless required.

Now, structure the markdown. Start with H1: # K-평균. Then an overview section. Next sections like "알고리즘 원리," "실행 단계," "응용 분야," "장단점," and "관련 기법." Each of these can have H2 headings. Under each, use H3 for subsections if needed.

Make sure the language is clear and concise. Avoid jargon without explanation. Keep paragraphs short for readability. Use bullet points or numbered lists where appropriate, like in the steps of the algorithm or advantages/disadvantages.

Check the length: 800 characters. The example provided by the user has around 1200 words, so I need to ensure that my draft meets this. Let me start drafting each section with these points in mind.