# Data Mining Questions and Answers – Major Issues in Data Mining

This set of Data Mining Multiple Choice Questions & Answers (MCQs) focuses on “Major Issues in Data Mining”.

1. There are many ways in which various types of data can be stored. Ex: 0,1 as binary data and names as nominal data. Choose the attribute which is not relevant to store data.
a) Binary Attributes
b) Nominal Attributes
c) Executable Attributes
d) Ordinal Attributes

Explanation: There are in about 4 kinds of attributes for data. They include nominal attributes, binary attributes, ordinal attributes, and numeric attributes. There exist executable files but not executable attributes to store the executable files.

2. Which among the given is not among the basic statistical descriptions of data?
a) Mean
b) Median
c) Average
d) Mode

Explanation: Average is the aggregate operator but not the statistical variable. Basic statistical descriptions of data include mean, median, mode, standard deviation etc.

3. What cannot be done when the basic statistics regarding each attribute and its types are known?
a) Null values can be filled
b) Noisy values
c) Mining the data using various tools
d) Fixing the inconsistencies

Explanation: basic statistics regarding each attribute makes it easier to ﬁll in missing values, smooth noisy values, and spot outliers during data preprocessing. Knowledge of the attributes and attribute values can also help in ﬁxing inconsistencies incurred during data integration.

4. Plotting the data can help in knowing the type of data.
a) True
b) False

Explanation: Plotting the measures of central tendency shows us if the data are symmetric or skewed. Quantile plots, histograms, and scatter plots are other graphic displays of basic statistical descriptions.

5. Which field of data mining helps in removing uncertainty, noise etc.
a) Data preprocessing
b) Outlier detection and removal
c) Data Mining
d) Uncertainty Reasoning

Explanation: Data Mining refers to the process of extraction of hidden patterns from the Data Warehouse data. Data Preprocessing, Outlier detection and removal and Uncertainty Reasoning are the methods which aim at removing uncertainty, noise, or incompleteness of data.

6. Which type of mining allows slicing, dicing, pivoting?
a) Presentation and Visualization
b) Ad-hoc data mining
c) Interactive Mining
d) Incorporation of background knowledge

Explanation: Interactive mining should allow users to dynamically change the focus of a search, to reﬁne mining requests based on returned results, and to drill, dice and pivot through the data and knowledge space interactively, dynamically exploring “cube space” while mining.

7. By which parameters are the data sets made up of?
a) Data Relations
b) Data Objects
c) Data Classes
d) Data Patterns

Explanation: Data sets are made up of data objects. A data object represents an entity in the sales table which may include item id, item name, item description, customer id, customer phone number etc; in the medical database the objects may refer to the patients, doctors, surgeries etc.

8. An object refers to the feature of the data.
a) True
b) False

Explanation: An attribute is a data ﬁeld, representing a characteristic or feature of a data object. The nouns attribute, dimension, feature, and variable are often used interchangeably in the literature.

9. Which among the following is not the name for the data whose values are simply names?
a) Nominal Attributes
b) Categorical Attributes
c) Symmetrical data
d) Enumerations

Explanation: Nominal means “relating to names.” The values of a nominal attribute are symbols or names of things. Each value represents some kind of category, code, or state, and so nominal attributes are also referred to as categorical. The values do not have any meaningful order. In computer science, the values are also known as enumerations.

10. If the value to the variable is 35 degrees, into which type of attribute can the data be classified?
a) Nominal Attribute
b) Binary Attribute
c) Ordinal Attribute
d) Numeric Attribute

Explanation: A numeric attribute is quantitative; that is, it is a measurable quantity, represented in integer or real values. Numeric attributes can be interval-scaled or ratio-scaled.

11. Can the aggregation operations, median, mean be performed on Nominal Data.
a) Yes
b) No

Explanation: Because nominal attribute values do not have any meaningful order about them and are not quantitative, it makes no sense in ﬁnding the mean (average) value or median (middle) value for such an attribute, given a set of objects.

12. The most frequently occurring values or the mode of the data can be easily found in the nominal attributes.
a) True
b) False

Explanation: the attribute’s most commonly occurring value. This value, known as the mode, is one of the measures of central tendency and can be found from the given nominal data. The other basic statistical descriptions of data are difficult to be found.

13. What are the objective interesting measures for Classification (If Then) rules?
a) Support and Confidence
b) Coverage and Confidence
c) Support and Accuracy
d) Accuracy and Coverage

Explanation: The objective interesting measures for Association rules are Support and Confidence and the objective interesting measures for Classification (IF- Then) rules are Accuracy and Coverage.

14. Which among the following is not the interesting pattern as far the System perspective of the Interesting patterns are concerned?
a) Novel
b) Easily understood by Humans
c) Valid
d) Unexpected

Explanation: A pattern is interesting as per the System Perspective if it is (1) easily understood by humans, (2) valid on new or test data with some degree of certainty, (3) potentially useful, and (4) novel.

15. Which among the following is not the interesting pattern as far the user perspective of the Interesting patterns are concerned?
a) Completeness
b) Actionable
c) Valid
d) Unexpected

Explanation: These measures find patterns interesting if the patterns are unexpected (contradicting a user’s belief) or offer strategic information on which the user can act. In the latter case, such patterns are referred to as actionable. Patterns that are expected can be interesting if they validate on a hypothesis.

Sanfoundry Global Education & Learning Series – Data Mining.

To practice all areas of Data Mining, here is complete set of Multiple Choice Questions and Answers.

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