Was ist K-Means-Clustering?

K-Means ist ein Algorithmus, der Datenpunkte in K Gruppen aufteilt, sodass ähnliche Punkte im selben Cluster landen. Für Lotteriezahlen nutzen wir Häufigkeit, Tiefe und Intervall als Merkmale.

Wie viele Cluster sollte man wählen?

3-4 Cluster funktionieren gut für die meisten Lotterien: heiss, kalt und neutral. Experimentieren Sie mit verschiedenen Werten.

Garantiert die Clusteranalyse Gewinne?

Nein. Die Analyse zeigt statistische Muster, kann aber keine Ziehungsergebnisse vorhersagen. Nutzen Sie sie als zusätzliches analytisches Werkzeug.

Does cluster analysis guarantee a win?

No, cluster analysis is a statistical analysis tool, not a prediction tool. It helps structure information about numbers, but each lottery draw is an independent random event.

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SiVinceTutto Clusteranalyse — K-Means-Zahlengruppierung

SiVinceTutto: zahlen nach ähnlichem Verhalten in Cluster gruppieren

Die Clusteranalyse gruppiert Zahlen der Lotterie SiVinceTutto nach ähnlichem Verhalten mithilfe des K-Means-Algorithmus. Jede Zahl wird durch ihre Häufigkeit, aktuelle Tiefe und durchschnittliches Trefferintervall beschrieben.

Analyse basierend auf 20 Ziehungen von 12/10/2025, 07:00 PM bis 04/22/2026, 06:00 PM

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How to Use Cluster Analysis for SiVinceTutto

Step-by-step guide to clustering lottery numbers for SiVinceTutto lottery

Choose the field and number of clusters

For multi-drum lotteries, select the desired field. The number of clusters (3–6) determines grouping detail — start with 4.

Study the cluster cards

Each cluster has a name describing the numbers' behavior: "hot stable", "cold stable", "warming up". Pay attention to the average frequency and depth.

Analyze the scatter plot

The "Frequency vs Depth" chart shows how numbers are distributed among clusters. Numbers in the top-left are frequent and recent; bottom-right are rare and long absent.

Use the generator

Mark numbers from clusters of interest and generate combinations. For example, choose "hot stable" for an aggressive strategy or "warming up" to bet on the trend.

About Cluster Analysis

K-means method and its application

K-means is one of the most popular unsupervised machine learning algorithms. It groups objects (numbers) into K clusters by minimizing the distance to the cluster center.

Frequency

How many times the number has been drawn

Depth

How many draws ago was the last appearance

Interval

Average gap between appearances

Comprehensive approach

Cluster analysis evaluates a number not by a single indicator, but by a combination of characteristics. A number with high frequency but great depth is not the same as a number with high frequency and low depth.

Cluster Analysis Tips

Practical recommendations for effective use of the method

Start with 4 clusters — this is the optimal number for most lotteries. Increase only if you need more detailed grouping.

Pay attention to the "warming up" cluster — these are numbers that have recently started appearing more frequently after a long quiet period.

Combine numbers from different clusters to create a balanced combination.

Clustering results depend on the number of draws. Use the period filter to compare results across different time intervals.

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