Which of the following forms of data mining assigns records to one of a predefined set of classes?
(C) Both A and B
The correct answer for the given question is Option (B) Clustering
Clustering is the form of data mining that assigns records to one of a predefined set of classes. It is a method of unsupervised learning, which means that it does not require a labeled dataset. Clustering is used to group data points that are similar to each other and to find hidden patterns in data. It is also used to cluster large datasets for easier analysis.
Clustering in Data Mining
A cluster consists of a collection of related data objects. A group of data is a cluster. In cluster analysis, which is based on data similarity, data sets are divided into different groups. A label is assigned to each group of data after it has been classified into various groups. The classification helps the organization adapt to changes.
In Data Mining, cluster analysis is used to identify a group of objects that are similar within a group, but different from the objects in other groups. Data clustering can be applied to image processing, data analysis, pattern recognition, market research, etc. Businesses can discover new groups of customers using Data clustering. They can also classify data by purchasing patterns.
In Data Mining, clustering helps classify animals and plants based on their similar functions or genes. This provides insight into how species are structured. Data mining uses clustering to identify geographical areas. Earth observation databases identify similar lands. Based on the type of house, the value, and the location of a house, a group of houses are defined in a city. By classifying the files on the Internet, clustering in data mining helps in the discovery of information.Clustering is also used in detection applications.. The pattern of deception in a credit card can be detected using clustering in data mining.
Requirements of Clustering in Data Mining
Here are some reasons why clustering is necessary in data mining:
- Scalability: When dealing with large databases, high-performance clustering algorithms are essential.
- Ability to deal with different kinds of attributes: In general, algorithms should be able to handle any type of data, including interval-based (numerical), categorical, and binary data.
- Discovery of clusters with attribute shape: Clustering algorithms should be able to detect clusters of arbitrary shapes. It should not be limited to only distance measures that tend to find small spherical clusters.
- High dimensionality: As well as low-dimensional data, the clustering algorithm should be capable of handling high-dimensional data as well.
- Ability to deal with noisy data: The data in databases is noisy, missing or erroneous. These data may be sensitive to some algorithms, resulting in poor quality clusters.
- Interpretability: Clustering results should be interpretable, understandable, and usable.