which of the following is an unsupervised data mining technique that groups similar items together into clusters. The goal of clustering is to identify similar clusters in the data and to understand the relationships between them.
Clustering is used in a variety of applications, such as market segmentation, fraud detection, image processing, and gene expression analysis.
Q. What are the advantages of clustering?
A. The advantages of clustering include being able to identify meaningful patterns and relationships in the data, being able to detect outliers, and being able to make predictions about future data points.
Clustering can also help reduce the cost of data storage and processing, as it requires fewer resources than supervised learning techniques.
Q. What are the types of clustering?
A. There are several types of clustering algorithms, including hierarchical clustering, k-means clustering, and density-based clustering. Each type of clustering algorithm has its own set of advantages and disadvantages, so it is important to choose the algorithm that best fits the data and the task.
Q. What is the difference between clustering and classification?
A. The primary difference between clustering and classification is that clustering is an unsupervised learning technique, while classification is a supervised learning technique.
Clustering algorithms attempt to group similar items together without any prior knowledge of the data, while classification algorithms attempt to assign labels to each input based on training data.
Q. How is clustering used in data mining?
A. Clustering is used in data mining to help identify meaningful patterns in the data. Clustering can be used to group similar items together, detect outliers, and make predictions about future data points.
Additionally, clustering can be used to reduce the cost of data storage and processing, as it requires fewer resources than supervised learning techniques.
Q. What is the purpose of clustering?
A. The purpose of clustering is to identify similar clusters in the data and to understand the relationships between them. Clustering is used for a variety of applications, such as market segmentation, fraud detection, image processing, and gene expression analysis.
It can also be used to reduce the cost of data storage and processing, as it requires fewer resources than supervised learning techniques.
