Data Mining

Data processing techniques in data mining include:

1. Data Cleaning: Removing noise, errors, and inconsistencies from the dataset.
2. Data Integration: Combining multiple data sources into a unified dataset.
3. Data Transformation: Converting raw data into a suitable format for analysis.
4. Dimensionality Reduction: Reducing the number of variables while retaining essential information.
5. Discretization: Converting continuous variables into categorical or ordinal attributes.
6. Sampling: Selecting a subset of data from a large dataset.
7. Data Visualization: Visual exploration and understanding of the dataset.
8. Pattern Mining: Discovering meaningful patterns in the data.
9. Predictive Modeling: Building models to predict future outcomes.

There are three main data processing methods – manual, mechanical and electronic.

Manual Data Processing

This data processing method is handled manually. The entire process of data collection, filtering, sorting, calculation, and other logical operations are all done with human intervention and without the use of any other electronic device or automation software. It is a low-cost method and requires little to no tools, but produces high errors, high labor costs, and lots of time and tedium.

Mechanical Data Processing

Data is processed mechanically through the use of devices and machines. These can include simple devices such as calculators, typewriters, printing press, etc. Simple data processing operations can be achieved with this method. It has much lesser errors than manual data processing, but the increase of data has made this method more complex and difficult.

Electronic Data Processing

Data is processed with modern technologies using data processing software and programs. A set of instructions is given to the software to process the data and yield output. This method is the most expensive but provides the fastest processing speeds with the highest reliability and accuracy of output.