Всё или ничего Clusteranalyse — K-Means-Zahlengruppierung
Die Clusteranalyse gruppiert Zahlen der Lotterie Всё или ничего nach ähnlichem Verhalten mithilfe des K-Means-Algorithmus. Jede Zahl wird durch ihre Häufigkeit, aktuelle Tiefe und durchschnittliches Trefferintervall beschrieben.
Cluster Visualization
Clustering Table
| Kugel hinzugefügt | Kugel | Cluster | Häufigkeit | Tiefe |
|---|---|---|---|---|
Hinzufügen | Warming up | 6 | 1 | |
Hinzufügen | Warming up | 9 | 1 | |
Hinzufügen | Warming up | 8 | 1 | |
Hinzufügen | Warming up | 8 | 0 | |
Hinzufügen | Warming up | 8 | 0 | |
Hinzufügen | Warming up | 8 | 1 | |
Hinzufügen | Cold stable | 10 | 4 | |
Hinzufügen | Cold stable | 9 | 3 | |
Hinzufügen | Cold stable | 9 | 2 | |
Hinzufügen | Cold stable | 7 | 3 | |
Hinzufügen | Hot stable | 13 | 3 | |
Hinzufügen | Hot stable | 17 | 1 | |
Hinzufügen | Hot stable | 14 | 0 | |
Hinzufügen | Warming up | 12 | 0 | |
Hinzufügen | Warming up | 9 | 0 | |
Hinzufügen | Warming up | 13 | 0 | |
Hinzufügen | Warming up | 11 | 0 | |
Hinzufügen | Warming up | 10 | 0 | |
Hinzufügen | Warming up | 10 | 1 | |
Hinzufügen | Warming up | 11 | 0 | |
Hinzufügen | Warming up | 11 | 0 | |
Hinzufügen | Warming up | 9 | 0 | |
Hinzufügen | Warming up | 8 | 1 | |
Hinzufügen | Warming up | 10 | 0 |
Generator by Cluster Analysis
How to Use Cluster Analysis for Всё или ничего
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 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