#1 Introduction To Data Mining, Types Of Data |DM|
Summary
TLDRIn this video, the host introduces a new playlist on data mining, a field many viewers have requested. The first video defines data mining as the process of extracting useful information from large datasets, akin to mining for precious metals. It explains three primary data types for mining: database, data warehouse, and transactional data. The host also touches on miscellaneous data types like sequence, data streams, spatial, engineering, hypertext, multimedia, and web data. The video aims to educate viewers on the basics of data mining and its applications in analyzing trends and patterns within data.
Takeaways
- 🎥 The video introduces a new playlist focused on data mining, a topic frequently requested by viewers.
- 🗂️ Data mining is defined as the process of extracting useful information from large datasets, akin to mining for gold or coal.
- 📊 The video explains that data mining involves searching for trends and patterns within data, such as sales figures or student marks.
- 📈 An example given is using data mining to predict credit card risk for new customers based on historical data.
- 💾 The script outlines three main types of data that can be mined: database data, data warehouse data, and transactional data.
- 📚 Database data comes from RDBMS and is structured in tables, rows, and columns, where trends and patterns can be identified.
- 🏭 Data warehouse data is integrated from various sources and stored in a multi-dimensional structure, facilitating querying and decision-making.
- 🛒 Transactional data refers to records or attributes treated as transactions, such as sales or web clicks, which can reveal frequent patterns.
- 🔍 The video also mentions other data types like sequence data (e.g., stock market), data streams, spatial data (e.g., maps), and multimedia.
- 🚀 The presenter commits to completing the playlist despite the challenges of covering a wide range of topics and the differences in syllabi.
Q & A
What is the main topic of the video?
-The main topic of the video is an introduction to data mining, including what data mining is and the types of data that can be mined.
Why did the YouTuber initially hesitate to start a data mining playlist?
-The YouTuber initially hesitated to start a data mining playlist because they felt they wouldn't have enough time to complete it, and they felt obligated to finish it once started.
What is the definition of data mining given in the video?
-Data mining is defined as the process of extracting information from large sets of data, identifying useful patterns, and trends.
What are the three main types of data that can be mined according to the video?
-The three main types of data that can be mined are database data, data warehouse data, and transactional data.
What is the purpose of data mining in the context of customer data analysis?
-In the context of customer data analysis, data mining is used to predict the credit card risk of new customers based on previous customer data.
How is data stored in a data warehouse as described in the video?
-In a data warehouse, data is stored in a multi-dimensional structure, often represented as a data cube where each dimension represents an attribute.
What is a transaction in the context of transactional databases?
-In the context of transactional databases, a transaction refers to each record or attribute, such as customer sales, flight bookings, or user clicks on a webpage.
What are some other types of data that can be mined besides the three main types mentioned in the video?
-Other types of data that can be mined include sequence data, data streams, spatial data, engineering and design data, hypertext, multimedia, and web data.
What is an example of how data mining can be used in sales data analysis?
-Data mining can be used in sales data analysis to identify deviations in sales trends, such as increases or decreases in sales, and to make decisions like offering discounts to boost sales.
What is the YouTuber's commitment to the audience regarding the data mining playlist?
-The YouTuber commits to continuing the data mining playlist without interruptions and addressing any additional topics or questions the audience might have in the comment section.
Outlines
🔍 Introduction to Data Mining
The speaker introduces a new playlist on data mining, explaining that despite initial hesitations due to time constraints, they decided to start it based on audience demand. The video aims to cover various aspects of data mining, including what it is and the types of data that can be mined. Data mining is described as the process of extracting useful information from large datasets, akin to mining for valuable minerals. The analogy of finding relevant videos on YouTube is used to illustrate the concept of data mining. The speaker promises to cover different types of data that can be mined, such as database data, data warehouse data, and transactional data, and hints at exploring less common data types in subsequent videos.
📊 Types of Data for Mining
The speaker delves into the different types of data that can be mined, starting with database data, which is associated with relational database management systems (RDBMS). They explain that mining database data involves identifying trends and patterns, such as sales figures over time. An example is given where data mining can predict credit card risk for new customers based on historical data. The speaker then moves on to data warehouse data, which is a collection of integrated data from various sources, stored in a multi-dimensional structure known as a data cube. The data warehouse facilitates querying and decision-making processes. The explanation includes a brief personal anecdote about the speaker's educational background and the challenges of covering a new syllabus for data mining. Transactional data is also discussed, where each record is considered a transaction, such as sales or web clicks, and data mining can reveal frequent patterns within these transactions.
🌐 Beyond Traditional Data: Miscellaneous Data Types
In the final paragraph, the speaker expands on other types of data that can be mined beyond the traditional categories. These include sequence data, relevant to stock market analysis; data streams, which are continuous data transmissions; spatial data, such as maps; and engineering and design data, like integrated circuits. Hypertext and multimedia data, including audio and video, are also mentioned, as well as web data, which pertains to information related to web pages. The speaker concludes by expressing their intent to continue the playlist without interruptions and encourages viewers to provide feedback or ask questions in the comments section.
Mindmap
Keywords
💡Data Mining
💡Database Data
💡Data Warehouse
💡Transactional Data
💡Patterns
💡Knowledge Discovery
💡Relational Database Management System (RDBMS)
💡Multi-dimensional Structure
💡Querying
💡Decision Making
💡Credit Card Risk
Highlights
Introduction to a new playlist on data mining.
Explanation of the term 'data mining' as the process of extracting useful information from large data sets.
Differentiation between database data, data warehouse data, and transactional data in the context of data mining.
Description of database data as structured data stored in RDBMS with rows and columns representing tuples and attributes.
Discussion on how data mining can reveal trends and patterns within database data, such as sales data.
Example of using data mining to predict credit card risk for new customers based on historical data.
Explanation of a data warehouse as a collection of integrated data from various sources, stored in a multi-dimensional structure.
Clarification of querying and decision-making processes in the context of data warehouses.
Illustration of how data warehouses store data in a data cube, with each dimension representing an attribute.
Introduction to transactional data, which includes records referred to as transactions, such as sales or user clicks.
Mention of mining frequent patterns from transactional data as a key aspect of data mining.
Introduction to other types of data that can be mined, including sequence data, data streams, spatial data, engineering and design data, hypertext, multimedia, and web data.
Emphasis on the importance of data mining in analyzing and predicting various aspects of business and technology.
Acknowledgment of the challenges in covering a wide range of data mining topics due to the diversity of the subject matter.
Commitment to continue the playlist and address viewer's needs despite the complexity of the subject.
Invitation for viewers to comment with any questions or topics they would like to see covered in future videos.
Transcripts
[Music]
hello everyone welcome back to my
youtube channel trouble free in this
video let's get started with a new
playlist that is data mining so many of
you have been asking me to make videos
on data mining well still i thought of
not to start this playlist because i'm
not actually getting time uh if i start
i feel like i have to complete and you
know so i thought of not not starting it
as actually but you know so many of you
have been asking uh you know i felt like
okay
so many people are asking i have to do i
have to definitely do and the topics
also
you know so many topics are there
where which are not found anywhere i
don't know how to deal with that topics
but still somehow i'll manage to
complete the playlist by your exams time
so let's get started
this is the first video in this video
i'm going to explain you what is data
mining and what types of data that can
be mined okay so let's get into the
video now first what is data mining you
know what is mining right
mining uh coal in coal
fields or in gold mining sorry not coal
i don't know coral is mined or not but
gold mining we will be doing like you
know what is mining you will be
extracting things so here also data
mining is defined as the procedure for
extracting information from huge sets of
data that is you are having a lots and
lots of data from that data everything
is not actually required to you right
for example on youtube
uh in my channel
there are so many videos almost i have
done 500 550 plus videos among those all
videos all videos are not useful for you
only videos which are related to your
subjects or videos which are related to
your placements are useful for you so
among the those how will you do you will
be searching right you will be searching
then you will be getting the videos
so here also the same from the huge sets
of data whatever information you want
you will be extracting that information
that is what data mining is okay
it is also defined as mining knowledge
from the data so from data whatever is
required that is called as simply
knowledge or information
so that you are mining it you are
extracting it that is what data mining
means okay very simple definition now
what types of data can be mined that is
you are doing the data mining activity
then which which what what types of data
you can find
so we have three types database data
data warehouse data and transactional
data about everything i'll explain in
detail and apart from these three we
have also miscellaneous types like other
types we have so i'll tell about them
also okay let's get started now first is
database data database data is nothing
but the rdbms database management system
we have rdbms like
r is nothing but relational
relational database management system so
relational database management system
means what simply it is of tables so it
has set of tables and this tables will
have rows and columns okay table is
nothing but it is a combo of both rows
and columns right so row will represent
a tuple and column will represent a
attribute so what is tuple what is
attribute you will understand don't
worry now
so while you are mining the databases so
while you are mining the tables what you
can
get what these the output that you can
get out of it
that is you can search for trends and
you can search for data patterns trends
in the sense where the data is
increasing or where for example sales
so at which point sales increased at
which point sales decreased and at which
point sales are neutral that trends you
can
uh you know find out right and data
patterns in the sense suppose
you are
having a list of marks of all the
students of a class in a table so you
can under observe the pattern like uh
you know majority of the marks
sorry not majority of the marks
maximum marks are scored by how many
people minimum marks are scored by how
many people all that patterns you can
identify out of that right
so that is what
you can mine out of a database for
example with examples you'll understand
it more better
by using data mining you can analyze the
customer data in order to predict the
credit card risk of new customers that
is
based on the previous data
of the customers
of new customers based on previous data
okay
so based on previous data you can mine
that data you can
you know extract some useful points from
that data and based on that you can
predict the credit risk of the newly
coming customers got it you will be
analyzing the existing data and you will
be predicting some points predicting
some situations where newly coming
customers credit cards or credit
information could be at risk okay in
this situation you can use data mining
and the other situation is analyzing the
sales data so
you are analyzing the sales data of a
particular company and in that you can
analyze any deviations that is the sales
are going good or is there any deviation
or is there any hike in the sales like
that you can
you know
analyze by using the data mining this
also by using previous years data and
all you can do that okay this is about
the database data next data warehouse so
what is data warehouse actually when i
was in my engineering in three two or
four one i don't exactly remember but we
used to have the subject data warehouse
and data mining not just data mining it
used to be data of eighth house and data
mining the syllabus is completely
different actually so that is what i am
not able to you know
that is what i have not started videos
all these days if the syllabus is same
since i have prepared i used to have the
pdfs i used to have the materials and
when i prepared i write my self
handwritten notes and i used to have
that and all but for data mining the
syllabus is completely different only
some topics were matching so
that's what but still i'll manage to
complete it don't worry so here data
warehouse is nothing but it is the
collection of data integrated from
different sources from different sources
you are integrating the data and from
that data you are collecting the data
okay with querying and decision making
on the date confused i will explain with
diagram so what is querying and what is
decision making querying is nothing but
you are suppose in dbms you will be
writing queries write insert create
delete
so querying that is if you want to make
any changes if you want to extract the
data or if you want to make changes into
the data you want to delete the data all
that you can do with the help of query
and what is decision making on data
decision making is nothing but you can
make decisions like whatever you want to
like
whether you want to increase the sales
or whether you want to decrease the
sales whether you want to increase the
prices so if you say sorry if the sales
are decreasing in that case what you
have to do you have to give some
discounts so that the sales will again
get back like that you can make some
decisions right and in data warehouse
the data is stored in a
multi-dimensional structure so in data
in relational database how did we store
the data we store the data in form of
tables right but in data warehouse we
will be storing it in a
multi-dimensional structure which is
nothing but the data queue
where each dimension will represent each
attribute so in tables
what represented attributes columns used
to represent the attributes right but
here in data warehouse in the data cube
each dimension of the cube will
represent the attributes okay so i'll
explain that this is the diagram if you
can see i said data will be integrated
from different data sources right so
from these three data sources data is
being integrated into a data warehouse
and from this data warehouse querying
and analysis can be done okay that will
be done by clients so not only client
one and two you can have any number of
clients here okay and as i said data
will be stored in form of a data cube
right so this is the data cube here each
dimension will represent each attribute
so this dimension is representing time
this dimension is representing location
this dimension is representing the item
type suppose the item type is a pen or a
pencil or anything
then time is how much time it takes to
product to produce that one unit how
much time to produce it production time
and location at what location it is
being produced so you are representing
the item the
production time of the item and at what
location the item is being reduced with
the help of a single cube this is what
data warehouse is okay so after this we
have transactional database
transactional database is nothing but uh
you can simply say
it is also similar to the previous two
types here each record or each
attribute is referred as a transaction
okay so here you will be calling each
record as a transaction a transaction
could be anything it could be customer
sales or it could be flight booking
flight ticket booking or it could be
user clicks on web page that is how many
times a user has clicked on a particular
web page or how many times the user has
clicked on a particular banner or a
particular advertisement like that all
these all these will come under the
transactions only okay a transaction
will have transaction id and list of all
the other items which are making up that
transaction that is the id of the
transaction the name of the transaction
at what time the transaction started
transaction end time and transaction
date transaction location transaction
details everything will be there okay
like bank transaction you can take for
example so here from the transaction
database also you can mine the frequent
patterns that is the patterns which are
occurring frequently
got it this is about the transactional
database all transactional data you can
say actually
transactional data now after this we
have other types of data as well so what
are the other types of data like
sequence data sequence data means stocks
stock market related data and data
streams data streams is nothing but data
which is continuously being transmitted
okay and spatial data spatial data is
nothing but maps
okay and engineering and design data you
can for example all the data which is
related to engineering and designs for
example we can take integrated circuits
okay and we have hypertext you know what
is hypertext and multimedia multimedia
also you know what is multimedia right
like audio video and all and web data as
well like web page related data and all
so this is about the other types of data
this is all about the types of data that
can be mined and what is data mining and
all so
let's continue i will maximum try to
continue the playlist without any uh
obstructions thanks for watching the
video till the end let's meet ups in the
next coming video with another topic if
you're still having any needles just let
me know in the comment section
[Music]
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