INTRODUCTION TO AI (DATA MINING] MODEL
FUNDAMENTALS EXAM STUDY GUIDE AND PRACTICE
EXAM 2024/2025 | ACCURATE REAL EXAM QUESTIONS
WITH VERIFIED ANSWERS | EXPERT VERIFIED FOR A
GUARANTEED PASS | LATEST UPDATE
What is data mining? - ✔✔ANSW✔✔..The process of sorting through large data sets to
identify patterns and establish relationships to solve problems through data analysis
What are the steps involved in data mining when viewed as a process of knowledge
discovery? - ✔✔ANSW✔✔..Data Cleaning
Data Integration
Data Selection
Data Transformation
Data Mining
Pattern Evaluation
Knowledge Presentation
What are the data mining functionalities - ✔✔ANSW✔✔..Characterization and
discrimination
Mining of frequent patterns, associations, and correlations Classification and regression
Clustering analysis
Outlier analysis
Data Characterization - ✔✔ANSW✔✔..A summary of the general characteristics or
features of a target class of data. The data corresponding to the user-specified class is
typically collected by a query. For example, to study the characteristics of software
products with sales that increased by 10% in the previous year, the data related to such
products can be collected by executing an SQL query on the sales database.
Data discrimination - ✔✔ANSW✔✔..comparison of the target class with one or a set of
comparative classes
Data mining methodology challenges - ✔✔ANSW✔✔..Mining various and new kinds of
knowledge
Mining knowledge in multidimensional space
Integrating new methods from multiple disciplines
Boosting the power of discovery in a networked environment
Handling uncertainty, noise, or incompleteness of data
Pattern evaluation and pattern- or constraint-guided mining
,Explain one challenge of mining a huge amount of data in comparison with mining a
small amount of data. - ✔✔ANSW✔✔..Algorithms that deal with data need to scale
nicely so that even vast amounts of data can be handled efficiently, and take short
amounts of time
What is an outlier? - ✔✔ANSW✔✔..An object which does not fit in with the general
behavior of the model.
What do we understand by similarity measure? - ✔✔ANSW✔✔..It quantifies the
similarity between two objects. Usually, large values are for similar objects and zero or
negative values are for dissimilar objects.
What is the importance of similarity measures - ✔✔ANSW✔✔..They are important
because they help us see patterns in data. They also give us knowledge about our data.
They are used in clustering algorithms. Similar data points are put into the same
clusters, and dissimilar points are placed into different clusters.
What do we understand by dissimilarity measure and what is its importance? -
✔✔ANSW✔✔..Measuring the difference between to objects, the greater the difference
between two objects the higher the value.
What is the importance of dissimilarity measures - ✔✔ANSW✔✔..The importance of this
is that in some instances, having two objects with low dissimilarity could mean
something negative. For example, cheating.
Discuss one of the distance measures that are commonly used for computing the
dissimilarity of objects described by numeric attributes. - ✔✔ANSW✔✔..Euclidean
distance d(i, j) =sqrt((xi1 − xj1)^2 + (xi2 − xj2)^2 +··· )
Manhattan Distance |x1 - x2| + |y1 - y2|
Minkowski distance d(i, j) = sqrt(h, |xi1 − xj1|^h + |xi2 − xj2|^h + ...)
Supremum distance d(i, j) = max(f, p) |xif − xjf |
In many real-life databases, objects are described by a mixture of attribute types. How
can we compute the dissimilarity between objects of mixed attribute types? -
✔✔ANSW✔✔..In order to determine the dissimilarity between objects of mixed
attributes there are two main approaches. One of them indicates to separate each
attribute type and do a data mining analysis for each of them. This method is acceptable
if the results are consistent. Applying this method to real life projects is not viable as
analyzing the attribute types separately will most likely generate different results. The
second approach is more acceptable. It processes all attributes types together and do
only one analysis by combining the attributes into a dissimilarity matrix
, Does an outlier need to be discarded always? - ✔✔ANSW✔✔..In most cases of data
mining, outliers are discarded. However, there are special circumstances, such as fraud
detection, where outliers can be useful.
The mode is the only measure of central tendency that can be used for nominal
attributes. (T/F) - ✔✔ANSW✔✔..True. An example of this would be hair color, with
different categories such as black, brown, blond, and red. Which one is the most
common one?
Nominal attribute - ✔✔ANSW✔✔..refer to symbols or names of things. Categorical. It
can also be represented using a number, however, they are not meant to be used
quantitatively. Has no median, but has a mode
Binary Attributes - ✔✔ANSW✔✔..A nominal attribute with only two categories or states:
0 or 1, where
0 typically means that the attribute is absent, and 1 means that it is present.
Ordinal Attributes - ✔✔ANSW✔✔..An attribute with possible values that have a
meaningful order or
ranking among them, but the magnitude between successive values is not known.
Numeric Attributes - ✔✔ANSW✔✔..Quantitative; that is, it is a measurable quantity,
represented in
integer or real values. Can be interval-scaled or ratio-scaled.
Discrete Attribute - ✔✔ANSW✔✔..has a finite or countably infinite set of variables
Continuous Attributes - ✔✔ANSW✔✔..typically represented as floating-point variables.
The mean is in general affected by outliers (T/F) - ✔✔ANSW✔✔..True
Not all numerical data sets have a median. (T/F) - ✔✔ANSW✔✔..False
What are the differences between the measures of central tendency and the measures
of dispersion? - ✔✔ANSW✔✔..The measures of central tendency are the mean,
median, mode and midrange. They are used to measure the location of the middle or
the center of the data distribution, basically where the most values fall. Whereas, the
dispersion measures are the range, quartiles, interquartile range, the five-number
summary, boxplots, the variance and standard deviation of the data. They are mainly
used to find an idea of the dispersion of the data, how is the data spread out, and to
identify outliers.
How would you catalog a boxplot, as a measure of dispersion or as a data visualization
aid? Why? - ✔✔ANSW✔✔..As a data visualization aid. The boxplot shows how the
boundaries relate to each other visually, where the minimum, maximum values lie, and
