Summary: Data from a semi-conductor manufacturing process.
| Parameter | Value |
|---|---|
| Name | SECOM |
| Labeled | Yes |
| Time Series | No |
| Simulation | No |
| Missing Values | Yes |
| Dataset Characteristics | Multivariate |
| Feature Type | Real |
| Associated Tasks | Classification, Causal-Discovery |
| Number of Instances | 1567 |
| Number of Features | 591 |
| Date Donated | 2008-11-18 |
| Source | UCI Machine Learning Repository |
Key facts: Data Structure: The data consists of 2 files—the dataset file SECOM consisting of 1567 examples, each with 591 features, forming a 1567 x 591 matrix, and a labels file containing the classifications and date-time stamp for each example.
As with any real-life data situations, this data contains null values, varying in intensity depending on the individual features. This needs to be taken into consideration when investigating the data, either through pre-processing or within the technique applied.
The data is represented in a raw text file, each line representing an individual example, and the features separated by spaces. The null values are represented by the 'NaN' value as per MATLAB.
Manufacturing, Semi-conductor, Process optimization, Feature selection, Industrial data