In business intelligence, data clustering and classification are closely related, however classification is predictive while clustering is descriptive. Using variables with known values to forecast the unknowable or future values of other variables is the core of data categorization.
Examples of applications include direct marketing, spotting insurance fraud, and making medical diagnoses.
In order to produce the desired number of categories, the data set utilized for category training must first be clustered. The categories are next subjected to an algorithm known as the classifier, which produces a descriptive model for each. The newly developed classification system may then be utilized with these models to classify new things.
Effectiveness
These are the ways in which Golfarelli and Rizzi assess the performance of the classifier:
- Accuracy of prediction: How well does it anticipate the categories for brand-new observations?
- Speed: How much computing power does the classifier require?
- Robustness: How well do the developed models work when the quality of the data is poor?
- Scalability: Can the classifier handle vast volumes of data without losing effectiveness?
- Interpretability: Can consumers comprehend the results?
Variables like demographics, lifestyle details, or economic behavior are typical examples of the input for data categorization.
Challenges
When working with data categorization, there are several difficulties. One in particular is that an iterative modeling approach is required for all categories that will be used, such as consumers or clients. This is done to ensure that the existing categories do not become outmoded and obsolete due to undetected changes in the characteristics of client groups.
Companies in the insurance or banking industries, where fraud detection is crucial, may find this to be of particular value. If ways to monitor these changes and notify when categories are changing, vanishing, or new ones emerge are not established and deployed, new fraud practices may go undetected.