DATA MINING MCQS
Data mining is also known as:Data SummarizationKnowledge Discovery in DatabasesData CleaningData WarehousingB) Knowledge Discovery in DatabasesData mining is part of the KDD process for extracting patterns.
Which technique is used for market basket analysis?RegressionClusteringAssociation Rule MiningClassificationC) Association Rule MiningAssociation rules identify relationships between items.
Outlier detection is used to:Find missing dataIdentify unusual patternsGroup similar dataNormalize dataB) Identify unusual patternsOutlier detection highlights data points that deviate significantly.
Which algorithm is used for clustering?Decision TreesAprioriK-MeansNaive BayesC) K-MeansK-Means is a popular clustering algorithm.
Data preprocessing includes:Cleaning, Integration, Transformation, ReductionOnly data collectionOnly visualizationOnly model buildingA) Cleaning, Integration, Transformation, ReductionPreprocessing prepares data for mining.
The process of discovering patterns in large datasets is known as:Data CleaningData MiningData ModelingData FetchingB) Data MiningData mining extracts useful patterns, relationships, and trends from large datasets.
Clustering is an example of: Supervised learningUnsupervised learningSemi-Unsupervised learningReinforcement learningB) Unsupervised learningClustering groups similar data points without predefined labels.
Which of the following is a popular association rule mining algorithm?AprioriKNNDecision TreeRegressionA) AprioriApriori is widely used to find frequent itemsets and generate association rules.
Outlier detection helps in:Finding frequent patternsIdentifying unusual data pointsData normalizationClustering identical dataB) Identifying unusual data pointsOutlier detection spots data points that deviate significantly from the rest.
Data preprocessing involves:Data Cleaning, Integration, TransformationOnly Data VisualizationBuilding Decision TreesWriting QueriesA) Data Cleaning, Integration, TransformationPreprocessing prepares data for mining by cleaning and transforming it into usable format.
Which technique is used to predict future trends?ClusteringRegressionAssociationDimensionality ReductionB) RegressionRegression predicts continuous numeric values based on past data.
Which of these is a measure of interestingness in association rule mining?Support and ConfidenceAccuracy and PrecisionSensitivity and SpecificityRecall and F1A) Support and ConfidenceSupport and confidence measure frequency and reliability of discovered rules.
Data warehousing is mainly used for:OLAPTransaction ProcessingFile BackupNetwork ManagementA) OLAPData warehouses support Online Analytical Processing for decision-making.
Which clustering algorithm is based on density?K-MeansDBSCANAprioriFP-GrowthB) DBSCANDBSCAN forms clusters based on density of data points, handling noise effectively.
Dimensionality curse occurs when:Too few dimensions reduce accuracyToo many dimensions cause sparse data and poor performanceData is normalizedData has missing valuesB) Too many dimensions cause sparse data and poor performanceHigh-dimensional data can degrade algorithm performance due to sparsity and complexity.