Data Mining

Interview Questions

Data mining is the process of discovering patterns, trends, and valuable insights from large datasets using various techniques and algorithms.
The data mining process typically involves data collection, data cleaning, data preprocessing, model building, evaluation, and deployment.
Data mining involves extracting patterns from data, while machine learning focuses on building models that can make predictions or decisions based on data.
Applications include fraud detection, customer segmentation, market basket analysis, recommendation systems, and predictive maintenance.
Clustering is a data mining technique that groups similar data points together, helping to identify natural patterns and structures within a dataset.
Classification predicts categorical labels, while regression predicts numerical values.
Association rule mining identifies relationships between variables in a dataset, revealing patterns such as \\
Outlier detection involves identifying data points that deviate significantly from the norm or expected behavior in a dataset.
The Apriori algorithm is used for association rule mining, specifically for discovering frequent itemsets in transactional databases.
Decision trees are used for classification and regression tasks, representing decisions and their possible consequences in a tree-like structure.

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