Equal-frequency Interval BinningΒΆ
Global Algorithm - One-Dimensional Algorithm
Equal-frequency Interval Binning algorithm partitions the data values into disjoint subsets, which have the same number of data samples. The original data are then summarized in each pre-defined subset.
Input Parameters
| Parameter | Type | Constraint | Description | Remarks |
|---|---|---|---|---|
| \(Y\) | \(Y \in \mathbb R^N\) | \(N \in \mathbb{N}\) | Input data vector of length \(N\) | |
| \(n\) | \(n \in \mathbb N\) |
Output Parameters
| Parameter | Type | Constraint | Description | Remarks |
|---|---|---|---|---|
| \(\hat{Y}\) | \(\hat{Y} \in \mathbb R^M\) | \(M \in \mathbb{N}, \quad M \lt N\) | Output data vector of length \(M\) |
Single Steps using the Algorithm
- Data Discretization with Equal-frequency Interval Binning
- Data Reduction With Equal-frequency Interval Binning
References
- J. Han, M. Kamber and J. Pei, Data Mining - Concepts and Techniques, 3rd ed., Amsterdam: Morgan Kaufmann Publishers, 2012.