#1 Introduction To Data Mining, Types Of Data |DM|

Summary

In 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

00:00

🔍 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.

05:01

📊 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.

10:01

🌐 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.

Keywords

💡Data Mining

Data mining is a process used to extract useful information from large sets of data. In the context of the video, it is likened to mining for gold, where the goal is to find valuable insights. The video explains that not all data is useful, and data mining helps in identifying patterns and trends that are relevant to specific needs, such as predicting credit card risk for new customers based on historical data.

💡Database Data

Database data refers to information stored in a structured format within a relational database management system (RDBMS). The video uses the analogy of tables with rows and columns to describe how data is organized. It emphasizes that data mining can reveal trends and patterns within this data, such as identifying points of increase or decrease in sales data.

💡Data Warehouse

A data warehouse is a system used for reporting and data analysis. It integrates data from various sources and stores it for querying and decision-making purposes. The video explains that data in a data warehouse is organized in a multi-dimensional structure, unlike the tabular format in RDBMS, and it serves as a central repository for historical data that can be analyzed to support business decisions.

💡Transactional Data

Transactional data pertains to records of individual events or actions, such as sales, bookings, or user interactions. Each transaction is a unit of data with its own unique identifier and details. The video mentions that mining transactional data can uncover frequent patterns, which can be crucial for businesses to understand customer behavior and optimize operations.

💡Patterns

Patterns in data mining refer to regularities or trends that can be identified within the data. The video explains that by analyzing data, one can observe patterns such as the distribution of student marks or the frequency of sales. These patterns can provide insights that help in decision-making and predicting future outcomes.

💡Knowledge Discovery

Knowledge discovery in databases (KDD) is an umbrella term for the process of finding useful information in large volumes of data. The video simplifies this concept by stating that data mining is essentially the extraction of knowledge or information from data, which can then be used to inform decisions and strategies.

💡Relational Database Management System (RDBMS)

RDBMS is a database management system that uses relational model concepts for storing and managing data. The video describes RDBMS as a system of tables, where each table contains rows and columns, representing tuples and attributes respectively. Data mining within RDBMS can help in identifying trends and patterns across the relational data.

💡Multi-dimensional Structure

A multi-dimensional structure, as mentioned in the context of data warehouses, is a way of organizing data where each dimension represents an attribute or a characteristic of the data. The video uses the example of a data cube where dimensions might represent time, location, and item type, allowing for complex analysis and decision-making based on the interplay of these dimensions.

💡Querying

Querying in the context of databases and data warehouses refers to the act of writing and executing queries to retrieve, update, or manipulate data. The video explains that querying is a fundamental operation in data management, allowing users to extract specific information or make changes to the data as needed.

💡Decision Making

Decision making in data mining involves using the insights and patterns discovered in the data to inform business strategies and operations. The video suggests that data mining can support decisions such as increasing or decreasing sales, adjusting prices, or offering discounts based on the analysis of historical data.

💡Credit Card Risk

Credit card risk in the context of the video refers to the potential for financial loss due to credit card fraud or default. The speaker mentions that data mining can be used to predict the credit risk of new customers by analyzing existing customer data, which can help in making informed decisions about creditworthiness.

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]