which of the following is true of unsupervised data mining that uses algorithms to detect patterns in large datasets without human guidance or supervision. It is used to uncover previously unknown relationships in data and identify clusters or groups of data points with similar characteristics.
Unsupervised data mining can be used to identify anomalies in data, uncover hidden relationships among data points, and provide insights into the underlying structure of data.
Q1: What is unsupervised data mining?
A1: Unsupervised data mining is a type of data mining that uses algorithms to detect patterns in large datasets without human guidance or supervision. It is used to uncover previously unknown relationships in data and identify clusters or groups of data points with similar characteristics.
Q2: What are the applications of unsupervised data mining?
A2: Unsupervised data mining can be used for a variety of applications, such as anomaly detection, predictive analytics, clustering, and segmentation. It can also be used to uncover hidden relationships among data points and provide insights into the underlying structure of data.
Q3: How does unsupervised data mining work?
A3: Unsupervised data mining uses algorithms to detect patterns in large datasets without human guidance or supervision. The algorithms analyze the data and identify clusters or groups of data points with similar characteristics, uncover previously unknown relationships between data points, and identify anomalies in data.
Q4: What is the difference between supervised and unsupervised data mining?
A4: The main difference between supervised and unsupervised data mining is that supervised data mining requires human guidance or supervision, while unsupervised data mining does not.
Supervised data mining is used to find relationships between variables, while unsupervised data mining is used to discover new relationships between variables.
Q5: What are the advantages of unsupervised data mining?
A5: Unsupervised data mining has several advantages, including the ability to uncover previously unknown relationships in data, identify anomalies in data, provide insights into the underlying structure of data, and reduce the need for manual data exploration and analysis.
Additionally, unsupervised data mining can help reduce the time and cost associated with manual data analysis.
