This document provides an introduction to data mining techniques. It discusses data mining concepts like data preprocessing, analysis, and visualization. For data preprocessing, it describes techniques like similarity measures, down sampling, and dimension reduction. For data analysis, it explains clustering, classification, and regression methods. Specifically, it gives examples of k-means clustering and support vector machine classification. The goal of data mining is to retrieve hidden knowledge and rules from data.
What is Datamining? Which algorithms can be used for Datamining?Seval Çapraz
This presentation includes what is datamining, which technics and algorithms are available in datamining. This presentation helps you to understand the concepts of datamining.
presentation on recent data mining Techniques ,and future directions of research from the recent research papers made in Pre-master ,in Cairo University under supervision of Dr. Rabie
This lecture gives various definitions of Data Mining. It also gives why Data Mining is required. Various examples on Classification , Cluster and Association rules are given.
What is Datamining? Which algorithms can be used for Datamining?Seval Çapraz
This presentation includes what is datamining, which technics and algorithms are available in datamining. This presentation helps you to understand the concepts of datamining.
presentation on recent data mining Techniques ,and future directions of research from the recent research papers made in Pre-master ,in Cairo University under supervision of Dr. Rabie
This lecture gives various definitions of Data Mining. It also gives why Data Mining is required. Various examples on Classification , Cluster and Association rules are given.
key note address delivered on 23rd March 2011 in the Workshop on Data Mining and Computational Biology in Bioinformatics, sponsored by DBT India and organised by Unit of Simulation and Informatics, IARI, New Delhi.
I do not claim any originality either to slides or their content and in fact aknowledge various web sources.
This Presentation is about Data mining and its application in different fields. This presentation shows why data mining is important and how it can impact businesses.
Data preprocessing techniques
See my Paris applied psychology conference paper here
https://www.slideshare.net/jasonrodrigues/paris-conference-on-applied-psychology
or
https://prezi.com/view/KBP8JnekVH9LkLOiKY3w/
key note address delivered on 23rd March 2011 in the Workshop on Data Mining and Computational Biology in Bioinformatics, sponsored by DBT India and organised by Unit of Simulation and Informatics, IARI, New Delhi.
I do not claim any originality either to slides or their content and in fact aknowledge various web sources.
This Presentation is about Data mining and its application in different fields. This presentation shows why data mining is important and how it can impact businesses.
Data preprocessing techniques
See my Paris applied psychology conference paper here
https://www.slideshare.net/jasonrodrigues/paris-conference-on-applied-psychology
or
https://prezi.com/view/KBP8JnekVH9LkLOiKY3w/
2015 D-STOP Symposium session by UT Austin's Joydeep Ghosh. Watch the presentation at http://youtu.be/y2kYLM8GdbI?t=19m42s
Get symposium details: http://ctr.utexas.edu/research/d-stop/education/annual-symposium/
You Can Run, But You Can't Hide! - How far should the government regulate the Internet? -Has data mining gone too far? (Great secondary research project.) Digital footprint makeover handouts available at: digitalmakeover.wikispaces.com Find ideas for classroom instruction, also, on this wikispace.
This presentation was a breakout session at WLMA14 and has been updated to reflect recent revelations on medical information mining. We did not even get to discuss the EDUCATIONAL DATA MINING that is occurring on our students and children! Caveat!
We analysed fertility rate on total population of Island which includes Northern Ireland and Republic of Ireland. We used “All Island Population dataset” and checked the relationship between the dependent variable and multiple independent variables to find the meaningful information to enhance sales. Tools: Python Programming.
Data Warehousing and Business Intelligence is one of the hottest skills today, and is the cornerstone for reporting, data science, and analytics. This course teaches the fundamentals with examples plus a project to fully illustrate the concepts.
Identifying and classifying unknown Network Disruptionjagan477830
Since the evolution of modern technology and with the drastic increase in the scale of network communication more and more network disruptions in traffic and private protocols have been taking place. Identifying and classifying the unknown network disruptions can provide support and even help to maintain the backup systems.
In this slide I answer the basic questions about machine learning like:
What is Machine Learning?
What are the types of machine learning?
How to deal with data?
How to test model performance?
Safalta Digital marketing institute in Noida, provide complete applications that encompass a huge range of virtual advertising and marketing additives, which includes search engine optimization, virtual communication advertising, pay-per-click on marketing, content material advertising, internet analytics, and greater. These university courses are designed for students who possess a comprehensive understanding of virtual marketing strategies and attributes.Safalta Digital Marketing Institute in Noida is a first choice for young individuals or students who are looking to start their careers in the field of digital advertising. The institute gives specialized courses designed and certification.
for beginners, providing thorough training in areas such as SEO, digital communication marketing, and PPC training in Noida. After finishing the program, students receive the certifications recognised by top different universitie, setting a strong foundation for a successful career in digital marketing.
Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdfTechSoup
In this webinar you will learn how your organization can access TechSoup's wide variety of product discount and donation programs. From hardware to software, we'll give you a tour of the tools available to help your nonprofit with productivity, collaboration, financial management, donor tracking, security, and more.
Honest Reviews of Tim Han LMA Course Program.pptxtimhan337
Personal development courses are widely available today, with each one promising life-changing outcomes. Tim Han’s Life Mastery Achievers (LMA) Course has drawn a lot of interest. In addition to offering my frank assessment of Success Insider’s LMA Course, this piece examines the course’s effects via a variety of Tim Han LMA course reviews and Success Insider comments.
June 3, 2024 Anti-Semitism Letter Sent to MIT President Kornbluth and MIT Cor...Levi Shapiro
Letter from the Congress of the United States regarding Anti-Semitism sent June 3rd to MIT President Sally Kornbluth, MIT Corp Chair, Mark Gorenberg
Dear Dr. Kornbluth and Mr. Gorenberg,
The US House of Representatives is deeply concerned by ongoing and pervasive acts of antisemitic
harassment and intimidation at the Massachusetts Institute of Technology (MIT). Failing to act decisively to ensure a safe learning environment for all students would be a grave dereliction of your responsibilities as President of MIT and Chair of the MIT Corporation.
This Congress will not stand idly by and allow an environment hostile to Jewish students to persist. The House believes that your institution is in violation of Title VI of the Civil Rights Act, and the inability or
unwillingness to rectify this violation through action requires accountability.
Postsecondary education is a unique opportunity for students to learn and have their ideas and beliefs challenged. However, universities receiving hundreds of millions of federal funds annually have denied
students that opportunity and have been hijacked to become venues for the promotion of terrorism, antisemitic harassment and intimidation, unlawful encampments, and in some cases, assaults and riots.
The House of Representatives will not countenance the use of federal funds to indoctrinate students into hateful, antisemitic, anti-American supporters of terrorism. Investigations into campus antisemitism by the Committee on Education and the Workforce and the Committee on Ways and Means have been expanded into a Congress-wide probe across all relevant jurisdictions to address this national crisis. The undersigned Committees will conduct oversight into the use of federal funds at MIT and its learning environment under authorities granted to each Committee.
• The Committee on Education and the Workforce has been investigating your institution since December 7, 2023. The Committee has broad jurisdiction over postsecondary education, including its compliance with Title VI of the Civil Rights Act, campus safety concerns over disruptions to the learning environment, and the awarding of federal student aid under the Higher Education Act.
• The Committee on Oversight and Accountability is investigating the sources of funding and other support flowing to groups espousing pro-Hamas propaganda and engaged in antisemitic harassment and intimidation of students. The Committee on Oversight and Accountability is the principal oversight committee of the US House of Representatives and has broad authority to investigate “any matter” at “any time” under House Rule X.
• The Committee on Ways and Means has been investigating several universities since November 15, 2023, when the Committee held a hearing entitled From Ivory Towers to Dark Corners: Investigating the Nexus Between Antisemitism, Tax-Exempt Universities, and Terror Financing. The Committee followed the hearing with letters to those institutions on January 10, 202
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
Operation “Blue Star” is the only event in the history of Independent India where the state went into war with its own people. Even after about 40 years it is not clear if it was culmination of states anger over people of the region, a political game of power or start of dictatorial chapter in the democratic setup.
The people of Punjab felt alienated from main stream due to denial of their just demands during a long democratic struggle since independence. As it happen all over the word, it led to militant struggle with great loss of lives of military, police and civilian personnel. Killing of Indira Gandhi and massacre of innocent Sikhs in Delhi and other India cities was also associated with this movement.
Macroeconomics- Movie Location
This will be used as part of your Personal Professional Portfolio once graded.
Objective:
Prepare a presentation or a paper using research, basic comparative analysis, data organization and application of economic information. You will make an informed assessment of an economic climate outside of the United States to accomplish an entertainment industry objective.
2. Schedule:
1. Example of Datamining
2. What and Where is Datamining in the System
3. Datamining Techniques
Data preprocessing
Data Analysis
Data Visualization
3. How data look like?
X Y
3 3
3 1
2 2
4 6
2 3
6 7
7 5
5 6
Can we get some thing from this?
The row represents
an object and its
columns represent
its attributes
Ex: can we identify the group of these objects? YES
1. Example of Datamining
4. Now, forget the table, consider a row as a point then we have
0
2
4
6
8
0 2 4 6 8
X
Y
B
A
C
From each data point, we find its neighbors by scanning with a radius r .
For Example : A will have 2 Neighbors B and C , denoted: A{B,C}
r
D
A and D have same neighbors so they are considered as neighbors
Same for B {A,B,C,D} ,C{A,B,C,D}, D{B,C}
The points have neighborhood will be in the same group.
1. Example of Datamining
5. Finally we have 2 groups after considering all points
0
2
4
6
8
0 2 4 6 8
X
Y
What do we see here?
Data has not been classified into groups but we now have the groups
This is just an example of technique called CLUSTERING in DATAMINING
1. Example of Datamining
6. 2. What and Where is Datamining in the System
So. What exactly is Datamining?
Datamining is the set of tools and techniques to retrieve
hidden Knowledge/Rules from data
The name of datamining could make us to misunderstand
Data was there, we do not need to ‘mining’ it
For ore mining you need hammers and shovels
However, for datamining you need mathematic, statistic and
probability, machine learning, computer programming,
database techniques,...
7. 2. What and Where is Datamining in the System
Where is Datamining in the system?
Employee/Staff
Day by day, The staff using the software (Web/
Desktop/Mobile application) to generate data by recording
all of his/her business activities (customers, products,
order detail, contracts ,…) Database
Data is added to Database
Online transaction processing (OLTP)
Database
Database
….
Data from several data sources (OLTP) will be collected to a common repository
Data
warehouse
Integration
Service
Datamining service will access to the Data warehouse to process
Data Mining
8. 3. Datamining Techniques
What are the techniques in Datamining?
There are so many techniques can be applied in datamining
Basically we can classify them into 3 groups / phases
Data-Preprocessing
Data Analysis
Data Presentation
10. 3. Datamining Techniques
We can understand that:
The quality of collected data would be not good.
It is necessary to clean / format / transform .... Before analyzing
This is very important process. It is very hard to find an
abstract way to describe.
Data-Preprocessing
Here we will see few examples of data pre-processing
techniques:
• Similarity Measure
• Down Sampling
• Dimension Reduction
• Vectorization
11. 3. Datamining Techniques
How can we know which object are similar?
Data-Preprocessing Similarity Measure
A(x1,y1)
B(x2,y2)
C(x1,y1)
D2D1
Measure the distance between AB and AC
We see that D1 < D2 -> A is more similar with B than C
Every point can be represented as vector. Measure the angle between
pair of vectors: A and B, then A and C
We see that 𝜶 < 𝜷 -> A is more similar with B than C
𝜶
𝜷
12. 3. Datamining Techniques
What if, you have so many data, performing data analysis on all
of them may be not necessary and reducing performance ?
Data-Preprocessing Down Sampling
Just pick some of them to evaluate
Example: using a cell-size of 𝑔. Keep only object / cell
𝑔
𝑔
Origin Data Down Sampling
13. 3. Datamining Techniques
All example data have been presented to you are in 2
dimensions, 2 attributes (X,Y) . What if it was ~10.000 attributes
for each object
Data-Preprocessing Dimension Reduction
This could reduce the performance (and or accuracy) of data-
analysis algorithms . Somehow we need to reduce number of
dimensions
Principal component Analysis & Singular value Decomposition
are 2 of most effective methods to do this
14. 3. Datamining Techniques
Data-Preprocessing Dimension Reduction - PCA
PCA
X
Y
𝑃1
𝑃2
Origin Data Data projected to Principal Components
We Only keep 𝑘 Principal Components that have highest eigenvalues. On above
example. We can let 𝑘 = 1 then keep 𝑃1 instead of both 𝑃1 , 𝑃2
By this way the number of dimensions has been reduced
15. 3. Datamining Techniques
Data-Preprocessing Vectorization
Most of Data Analysis algorithms consider the input as set of
vectors, so we need to transform the collected data into set of
vectors.
Ex: Giving a document: “Mr A has not passed the exam this
year. He will do it again next year”
Some of important words will be extracted like “Mr A” , “not” ,
“pass” ,”exam” , “again” , “next” , “year”
Measure the frequency of each word, we get the vector that
represent the document
Mr A not pass exam again next year
1 1 1 1 1 1 2
17. 3. Datamining Techniques
There are so many techniques in this phase:
• Clustering
• Classification
• Regression
• Rule Bases
• ….
This is the most important phase, where we find all of
hidden knowledge/ rules in the data
Data Analysis
18. 3. Datamining Techniques
The process of clustering is to find ways to group objects
into groups (clusters)
Data Analysis Clustering
The objects in the same cluster are similar and otherwise
they are not similar.
There are 2 types of clustering : Partional & Hierarchical
In this presentation: we see an example of the most famous
clustering method : K-Mean
19. 3. Datamining Techniques
Data Analysis Clustering – K mean Algorithm
1. Randomly select K center (centroid) for K clusters (cluster).
2. Calculate the distance between objects (objects) to the K center
3. Group objects to the nearest group
4. Defining the new focus for the group
5. Repeat step 2 until no change of subject groups
21. 3. Datamining Techniques
Data Analysis Clustering – K mean Algorithm
Select K=2 centroids Compute the new position of
centroids
Finally centroids stop changing
The object belongs to the group of
its closest centroid
The key point of algorithm is to
select a good k
22. 3. Datamining Techniques
Data Analysis Classification
How can we identify the group of unclassified object ?
Sure! we can perform clustering to do this.
However, what if we know some of classified objects in
the past? Can we do better than Clustering? YES.
We can construct a prediction model to predict the group
of unclassified objects based on the classified objects
This process called CLASSIFICATION
23. 3. Datamining Techniques
Data Analysis Classification
The process of Classification can be described as below
Learning
Algorithm
Model
24. 3. Datamining Techniques
Data Analysis Classification - SVM
Support Vector Machine (SVM) is one of famous classification
method. It belongs to group of linear classifiers
For example: data classified in red and blue Training Data
𝑤 : normal vector
𝑏 : bias / distance from the line to origin
?
𝑥
𝑦 𝑤 + 𝑏 > 0 → blue
Classification Model?
𝑥
𝑦 𝑤 + 𝑏 < 0 → red
25. 3. Datamining Techniques
Data Analysis Regression
Use for prediction: but to predict the missing value of an
attribute
For example:
Y
X𝑥𝑖
𝑦𝑖
• How to find 𝑦𝑖 , if 𝑥𝑖 known?
• We can estimate the line
that describe The data
• Plug 𝑥𝑖 to line equation to
Find 𝑦𝑖
• This is just an example of
Linear Regression
26. 3. Datamining Techniques
Data Analysis Rule Base
Rule Base techniques : to find hidden patterns in the data
Example of rule base techniques:
• Customer normally buy rice always buy vegetable
• Young people want to more expensive phone than others
• People always buy laptop before buying cell-phone
Frequent Pattern
Gradual Pattern
Sequential Pattern
28. 3. Datamining Techniques
Data Visualization
Techniques to present knowledge that you retrieved to user
0
2
4
6
8
10
12
14
Series 3
Series 2
Series 1
Series 1 Series 2 Series 3
Category
1 4.3 2.4 2
Category
2 2.5 4.4 2
Category
3 3.5 1.8 3
Category
4 4.5 2.8 5