These Data Mining multiple-choice questions and their answers will help you strengthen your grip on the subject of Data Mining. You can prepare for an upcoming exam or job interview with these 100+ Data Mining MCQs.
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A. All of these
B. Retail
C. Manufacturing
D. Finance/Banking
A. Output Layer
B. Hidden Layer
C. Transparent layer
D. Input layer
A. inconsistent
B. dirty
C. nonintegrated
D. granular
A. The range of variables in a set
B. The number of nodes utilized
C. The graphical visualization of the data
D. The number of layers and the number of nodes in each layer
A. Single-Link
B. DSBSCAN
C. Both of these
D. None of these
A. False
B. True
A. CHAID
B. artificial
C. pruning
D. associative
A. <body answer="valid">This One</body>
B. <valid>This One</valid>
C. <valid>"This One"</valid>
D. All are valid
A. All of the above
B. Apache Cassandra
C. Google Big Table
D. MongoDB
A. The technical term for the act of data being stored in a server
B. A structured and developed prediction of data results
C. The visual interpretation of complex relationships in multidimensional data
A. Differential Decryption
B. Knoop-hardness measured through high-impact dimension
C. Knowledge Discovery in Databases
D. K-mean Data Discovery
A. All are valid types
B. Neural network
C. Statistical
D. Machine learning
A. False
B. True
A. All of the above
B. Artificial Intelligence
C. Statistics
D. Linguistics
A. Dependent
B. All of these
C. Response
D. Target variables
A. Classification
B. Regression
C. Segmentation
A. Predictable Sets
B. Punctional Organizations
C. Degrees of Fit
D. Clusters
A. Complex reports generated by a qualified data scientist
B. Hierarchical dimensions that can be created with a hyper cube browser
C. Data not collected by the organization, such as data available from a reference book
D. Structures that generate rules for the classification of a dataset
A. Relational Learning Models
B. Decision Trees and Rules
C. All of these
D. Probabilistic Graphical Dependency Models
A. False
B. True
A. A decision tree developed in the 1980's but almost entirely replaced by the CART method today
B. A six phase method for predicting e-commerce buying habits
C. Microsoft's linear regression algorithm
D. A cross-industry standard process for data mining
A. Antecedent
B. Activation Function
C. Confusion matrix
D. Chi-square
A. True
B. False
A. binary standard deviation
B. covariance
C. polyconvergence
D. stochastic inertia
A. Using business experience and gut instinct to design a new floorplan in a grocery store
B. Reorganizing your basketball team's starting lineup based on an analysis of performance
C. Placing two frequently purchased items next to each other on the shelf
D. Predicting the likelihood of a backpack being purchased based on a consumer's purchase of sleeping bags and hiking shoes
A. Segmentation
B. Classification
C. Regression
A. An intuitive and user friendly user interface
B. Firewalls established to protect data from malicious sources
C. The hardware designed specifically for storage of massive amounts of data
D. The team of programmers who designed the software utilized in a particular mining project
A. decision boundary separating classes of data
B. variant of the C4.5 algorithm
C. collection of linked hypertext files
D. non-terminating error condition
A. Overlay
B. Overfitting
C. Noise
D. Non-applicable date
A. Price
B. Economic downturns
C. Staff Skills
D. Product Positioning
A. Sequential Patterning
B. Clustering
C. Classification
D. Gamification
A. Structural Level
B. Qualitative Level
C. Primary Level
D. Quantitative Level
A. Decrease the size of the training dataset
B. Increase the size of the training dataset
C. Increase the size of the test dataset
D. Decrease the size of the test dataset
A. AdaBoost
B. The Brin-Page Method
C. GoogleCrawler
D. PageRank
A. The antecedent is always a very complex variable
B. Nothing, they are interchangeable
C. The antecedent is on the right, the consequent is on the left.
D. The antecedent is on the left, the consequent on the right
A. partial average
B. unbiased mean
C. compounded mean
D. moving average
A. Learning a function that maps a data item into one of several predefined groups.
B. An expression E in a language L describing facts in a subset FE of F.
C. A descriptive task where one seeks to identify a finite set of categories to describe the data.
D. Learning a function that maps a data item to a real-valued prediction variable.
A. A multi-step process involving data preparation, pattern searching, knowledge evaluation, and refinement with iteration after modification.
B. Learning a function that maps a data item into one of several predefined groups or clusters.
C. The process of finding a model which describes significant dependencies between variables
D. A task which consists of techniques for estimating, from data, the joint multi-variate probability density function of all of the variables/fields in the database.
A. Hidden
B. Input
C. Output
D. Functional
A. a measure of the noise in a database's contents
B. partioning a database for distribution across different servers
C. simultaneously accessing multiple object databases over SSH
D. none of the above
A. A task focusing on discovering the most significant changes in the data from previously measured or normative values
B. Methods for finding a compact description for a subset of data.
C. The process of finding a model which describes significant dependencies between variables
D. A task which consists of techniques for estimating, from data, the joint multi-variate probability density function of all of the variables/fields in the database.
A. Fuzzy Logic
B. Association Learning
C. Anomaly Detection
D. Clustering Algorithms
A. Restricted Boltzmann machine
B. info-fuzzy networks
C. k-nearest neighbor
D. k-means algorithm
A. MongoDB
B. SQLite
C. MySQL
D. MariaDB
A. (None of these)
B. Disjoint training
C. Test Datasets
D. disjoint training and test datasets
A. Overfit
B. Parametric analysis
C. Underfit
D. Poorly defined Chernoff Bound
A. Heuristic algorithms
B. Bayesian inference algorithms
C. Genetic algorithms
D. Clustering algorithms
A. none of the above
B. easier to train via online learning
C. more resistent to local minima convergence
D. parametric
A. Node
B. SAP source
C. UDC
D. DB Connect
A. Nearest Neighbor
B. Logistic Regression
C. Association Model Query
D. Decision Treeing