Data Mining

) Which of the following refers to the problem of finding abstracted patterns (or structures) in the unlabeled data?

Which one of the following refers to querying the unstructured textual data?

Which of the following is an essential process in which the intelligent methods are applied to extract data patterns

What is KDD in data mining?

What are the functions of Data Mining?

In the following given diagram, which type of clustering is used?

Which of the following statements is incorrect about the hierarchal clustering?

Which one of the following can be considered as the final output of the hierarchal type of clustering?

Which one of the following statements about the K-means clustering is incorrect?

Which one of the clustering technique needs the merging approach?

The self-organizing maps can also be considered as the instance of _________ type of learning.

Which of the following statement is true about the classification?

Which of the following statements is correct about data mining?

Which of the following can be considered as the classification or mapping of a set or class with some predefined group or classes?

The analysis performed to uncover the interesting statistical correlation between associated -attributes value pairs are known as the _______.

Which one of the following statements is not correct about the data cleaning?

Which one of the following correctly defines the term cluster?

Which of the following correctly refers the data selection?

Which one of the following correctly refers to the task of the classification?

Which of the following correctly defines the term "Hybrid"?

Which one of the following can be considered as the correct application of the data mining?

Which of the following refers to the steps of the knowledge discovery process, in which the several data sources are combined?

The term "DMQL" stands for _____

Which one of the following issues must be considered before investing in data mining?

Which of the following also used as the first step in the knowledge discovery process?

€�…. Is a data field, representing a characteristic or feature of a data object.

All of the following are attribute types except:

Given two objects represented by tuples (3, 5) and (2, 0). What is the Euclidean distance between two objects?

Given two objects represented by tuples (3, 5) and (2, 0). What is the Manhattan distance between two objects?

Euclidean distance is also known as ______________

€�.. Attribute has only a finite or countably infinite set of values while …. Attribute has real numbers as attribute values.

Value that occurs most frequently in the data?

Middle value if odd number of values, or average of the middle two values otherwise.

At symmetric data distribution

All of the following measure data dispersion except:

Basic statistical descriptions can be displayed graphically by:

€�… Graph display of tabulated frequencies, shown as bars

€�… is a numerical measure of how alike two data objects are.

Is a numerical measure of how different two data objects are.

We use data visualization to ….

In Pixel-Oriented Visualization Techniques for a data set of M dimensions we create ….. Windows on the screen.

Data set has more than mode

Data objects are described by attributes.

Data objects are made up of data sets

Dissimilarity value is lower when objects are more alike

Manhattan distance is represented by a direct line

Similarity value is lower when objects are more alike

We can achieve the Pixel-Oriented Visualization Techniques using direct visualization

Scatterplot is one of the ways to achieve Geometric Projection Visualization Techniques.

In Pixel-Oriented Visualization Techniques the colors of the pixels reflect the corresponding dimensions.

We use data visualization because it helps find interesting regions and suitable parameters for further quantitative analysis.

In Parallel Coordinates the axes are equidistant.

...........,routines work to “clean” the data by filling in missing values, smooth- ing noisy data, identifying or removing outliers, and resolving inconsistencies.

)All of them are methods of Missing Values except.

………, data encoding schemes are applied so as to obtain a reduced or “compressed” representation of the original data.

..........., the data are replaced by alternative, smaller representa-tions using parametric models or nonparametric models

……… methods smooth a sorted data value by consulting its “neighbor- hood,” that is, the values around it.

........... Says that each value of the given attribute must be different from all other values for that attribute.

........says that there can be no miss- ing values between the lowest and highest values for the attribute, and that all values must also be unique.

………..specifies the use of blanks, question marks, special characters, or other strings that may indicate the null condition.

...........tools use simple domain knowledge to detect errors and make corrections in the data. These tools rely on parsing and fuzzy matching techniques when cleaning data from multiple sources.

Which of the following is a common technique used in data cleaning?

Which of the following is a technique used to combine data from multiple sources?

Which of the following is a technique used to remove noise from data?

Which of the following is a technique used in data reduction?

Which of the following is a technique used to convert categorical data into numerical data?

Which of the following is a technique used to identify and remove duplicate records in a dataset?

Which of the following is a technique used to remove missing values from a dataset

Which of the following is a technique used to reduce the number of variables in a dataset?

Which of the following is a technique used to scale data to a specific range?

Which of the following is a technique used to transform data into a new representation?

Which of the following is a technique used to identify and remove irrelevant or redundant variables in a dataset?

Which of the following is a technique used to reduce the dimensionality of a dataset?

Which of the following is a technique used to convert continuous data into discrete intervals?

Which of the following is a technique used to standardize data by subtracting the mean and dividing by the standard deviation?

Which of the following is a technique used to replace missing values in a dataset with the median value?

Which of the following is a technique used to identify and handle inconsistent data in a dataset?

Which of the following is a technique used to reduce the number of dimensions in a dataset while retaining important information?

Which of the following is a technique used to handle missing values in a dataset by predicting the missing values using statistical models?

Which of the following is a technique used to transform data into a form that is more suitable for machine learning algorithms?

Which of the following is a technique used to reduce the number of dimensions in a dataset by transforming the data into a new space with fewer dimensions?

Which of the following is a technique used to convert categorical data into numerical data by creating a new binary variable for each category?

Data cleaning is the process of identifying and correcting or removing inaccurate or irrelevant data from a dataset.

Data integration is the process of converting data from one format to another.

Data transformation is the process of converting data from one format to another.

Data discretization is the process of converting continuous data into discrete intervals.

Outlier detection is the process of identifying data points that are significantly different from the rest of the data.

Data sampling is the process of selecting a subset of data from a larger dataset.

Data normalization is the process of converting data into a standard format.

Data imputation is the process of identifying and removing missing values from a dataset.

Feature selection is the process of identifying and removing irrelevant or redundant variables from a dataset.

Data smoothing is the process of identifying and removing noise from a dataset.

Data deduplication is the process of identifying and removing duplicate records from a dataset.

Data discretization is the process of converting categorical data into numerical data.

Principal Component Analysis (PCA) is a technique used in data reduction.

Data transformation is the process of scaling data to a specific range.

Data normalization and standardization are the same thing.

Data integration is the process of combining data from multiple sources into a single format.

Data imputation is the process of identifying and handling inconsistent data in a dataset.

Data reduction is the process of increasing the number of dimensions in a dataset.

...... Refers to a data repository that is maintained separately from an organization’s operational databases.

The major features of a data warehouse are.............

Data warehouses usually require only two operations in data accessing, which are .....

Systems that perform online transaction and query processing are called....

systems that can organize and present data in various formats in order to accommodate the diverse needs of different users, are called.....

Data warehouses often adopt a ..... architecture.

From bottom to top, the order of the three-tier architecture levels of a data warehouse is.....

Examples of data Warehouse Models are....

.......contains a subset of corporate-wide data that is of value to a specific group of users.

.... Data marts are sourced from data captured from external information providers but ..... Data marts are sourced directly from enterprise data warehouses.

.....gathers data from multiple, heterogeneous, and external sources.

......detects errors in the data and rectifies them when possible.

.....which converts data from legacy or host format to warehouse format.

.....are the perspectives or entities with respect to which an organization wants to keep records.

.....are numeric measures or quantities by which we want to analyze relationships between dimensions.

The most popular data model for a data warehouse is a multidimensional model, which can exist in the form of ......

A concept hierarchy that is a total or partial order among attributes in a database schema is called ....

Measures can be organized into categories, such as....

.... Is an OLAP operation that performs aggregation on a data cube, either by climbing up a concept hierarchy for a dimension or by dimension reduction.

.... Is an OLAP operation that's the reverse of roll-up and It navigates from less detailed data to more detailed data.

.... Is an OLAP operation that performs a selection on one dimension of the given cube, resulting in a subcube.

.... Is an OLAP operation that is a visualization operation that rotates the data axes in view to provide an alternative data presentation.

The construction of data warehouses involves data cleaning, data integration, and data transformation.

Data warehouses provide online transaction processing (OLTP) tools for the interactive analysis of multidimensional data

Data mining functions can be integrated with OLAP operations to enhance interactive mining of knowledge at multiple levels of abstraction.

The major features of a data warehouse are subject-oriented, integrated, time-variant, and nonvolatile

data warehouse doesn't focus on the modeling and analysis of data for decision makers

A data warehouse is usually constructed by integrating multiple heterogeneous sources, such as relational databases, flat files, and online transaction records.

In data warehouses, data are stored to provide information from an historic perspective (e.g., the past 5–10 years).

A data warehouse does require transaction processing, recovery, and concurrency control mechanisms.

Data warehousing employs an query-driven approach.

query processing in data warehouses does not interfere with the processing at local sources

An OLTP system is customer-oriented but an OLAP system is market-oriented.

An OLAP system manages current data that are too detailed but an OLTP system manages large amounts of historic data

An OLTP system usually adopts an application-oriented database design but an OLAP system adopts an subject-oriented database design.

The access patterns of an OLTP system consist mainly of short, atomic transactions but accesses to OLAP systems are mostly read-only operations.

An OLAP server is typically implemented using either a ROLAP model or a MOLAP model

A virtual warehouse is a set of views over operational databases.

Metadata are data about data.

Data warehouses and OLAP tools are based on a one-dimensional data model.

A data cube allows data to be modeled and viewed in multiple dimensions

The cuboid that holds the lowest level of summarization is called the base cuboid.

The 0-Dimension cuboid, which holds the highest level of summarization, is called the apex cuboid

A concept hierarchy defines a sequence of mappings from a set of low-level concepts to higher-level, more general concepts

Drill-through is an OLAP operation that executes queries involving more than one fact table.

Drill-across is an OLAP operation that uses relational SQL facilities to drill through the bottom level of a data cube down to its back-end relational tables.

A statistical database is a database system that is designed to support statistical applications.

A starnet model is a model that consists of radial lines emanating from a central point, where each line represents a concept hierarchy for a dimension.

Concept hierarchies can be used to generalize data or to specialize data.