Data mining involves using computer algorithms to analyze large datasets and infer new information. It has traditionally been done by data analysts but computers now allow for more efficient analysis. Data mining has two main components - knowledge discovery from known data and knowledge prediction using data to forecast trends. It uses techniques like decision trees and clustering. While valuable, data mining raises privacy concerns as personal data is increasingly mined without consent.
DATA MINING AND DATA WAREHOUSE
W.H. Inmon
OLAP, (On-line analytical processing)
OLTP, ( On-line transaction processing )
Data Cleaning
Data Integration
Data Selection
Data Transformation
Data warehouse vs Data Mining
Use in Urban Planning
Data Mining is a set of method that applies to large and complex databases. This is to eliminate the randomness and discover the hidden pattern. As these data mining methods are almost always computationally intensive. We use data mining tools, methodologies, and theories for revealing patterns in data. There are too many driving forces present. And, this is the reason why data mining has become such an important area of study.
Data Mining: What is Data Mining?
History
How data mining works?
Data Mining Techniques.
Data Mining Process.
(The Cross-Industry Standard Process)
Data Mining: Applications.
Advantages and Disadvantages of Data Mining.
Conclusion.
DATA MINING AND DATA WAREHOUSE
W.H. Inmon
OLAP, (On-line analytical processing)
OLTP, ( On-line transaction processing )
Data Cleaning
Data Integration
Data Selection
Data Transformation
Data warehouse vs Data Mining
Use in Urban Planning
Data Mining is a set of method that applies to large and complex databases. This is to eliminate the randomness and discover the hidden pattern. As these data mining methods are almost always computationally intensive. We use data mining tools, methodologies, and theories for revealing patterns in data. There are too many driving forces present. And, this is the reason why data mining has become such an important area of study.
Data Mining: What is Data Mining?
History
How data mining works?
Data Mining Techniques.
Data Mining Process.
(The Cross-Industry Standard Process)
Data Mining: Applications.
Advantages and Disadvantages of Data Mining.
Conclusion.
meaning of data warehousing
needs of data warehousing
applications of data warehousing
architecture of data warehousing
advantages of data warehousing
disadvantages of data warehousing.
meaning of data mining
needs of data mining
applications of data mining
architecture of data mining
advantages of data mining
disadvantages of data mining
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.
This presentation includes major application areas of data mining and its techniques in real world.This ppt includes various field where data mining is playing a crucial role in the development of every sector by its techniques.i hope it would be helpful to everyone.
Data Mining is defined as extracting information from huge sets of data. In other words, we can say that data mining is the procedure of mining knowledge from data.
According to Inmon, a data warehouse is a subject oriented,
integrated, time-variant, and non-volatile collection of data. He defined the terms
in the sentence as follows:
This Technical presentation compares data warehouse to Big data by trying to answer the question if data warehouse are still need in the advent of Big data .
In today’s competitive world, every business has to fight huge competition to achieve success. So it is necessary for every business organization to collect large amount of information like employee’s data, Sales data, customer’s information, market analysis reports, etc.
meaning of data warehousing
needs of data warehousing
applications of data warehousing
architecture of data warehousing
advantages of data warehousing
disadvantages of data warehousing.
meaning of data mining
needs of data mining
applications of data mining
architecture of data mining
advantages of data mining
disadvantages of data mining
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.
This presentation includes major application areas of data mining and its techniques in real world.This ppt includes various field where data mining is playing a crucial role in the development of every sector by its techniques.i hope it would be helpful to everyone.
Data Mining is defined as extracting information from huge sets of data. In other words, we can say that data mining is the procedure of mining knowledge from data.
According to Inmon, a data warehouse is a subject oriented,
integrated, time-variant, and non-volatile collection of data. He defined the terms
in the sentence as follows:
This Technical presentation compares data warehouse to Big data by trying to answer the question if data warehouse are still need in the advent of Big data .
In today’s competitive world, every business has to fight huge competition to achieve success. So it is necessary for every business organization to collect large amount of information like employee’s data, Sales data, customer’s information, market analysis reports, etc.
Data Donderdag - Making your own smart ‘machine learning’ thermostatNiek Temme
Presentation at the 10th edition of the Dutch Meetup group ‘data donderdag’ (presentation in English). De presentation describes my personal journey in making your own smart thermostat using machine learning (K-means clustering) and a bunch of hardware: Arduino, Raspberry PI, two XBee’s and an Amazon Cloud sever.
A secure cloud computing based framework for big information management syste...Pawan Arya
—Smart grid is a technological innovation that improves efficiency, reliability, economics, and sustainability of electricity services. It plays a crucial role in modern energy infrastructure. The main challenges of smart grids, however, are how to manage different types of front-end intelligent devices such as power assets and smart meters efficiently; and how to process a huge amount of data received from these devices. Cloud computing, a technology that provides computational resources on demands, is a good candidate to address. a secure cloud computing based framework for big data information management in smart grids, which we call “Smart-Frame.
Difference between data warehouse and data miningmaxonlinetr
What exactly is a Data Warehouse?
Termed as a special type of database, a Data Warehouse is used for storing large amounts of data, such as analytics, historical, or customer data, which can be leveraged to build large reports and also ensure data mining against it.@ http://maxonlinetraining.com/why-is-data-warehousing-online-training-important/
What is Data mining?
The process of extracting valid, previously unknown, comprehensible and actionable information from large databases and using it to make crucial business decisions’
Call us at For any queries, please contact:
+1 940 440 8084 / +91 953 383 7156 TODAY to join our Online IT Training course & find out how Max Online Training.com can help you embark on an exciting and lucrative IT career.
TODAY to join our Online IT Training course & find out how Max Online Training.com can help you embark on an exciting and lucrative IT career.
Visit www.maxonlinetraining.com
For Complete Course Overview and to a book @https://goo.gl/QbTVal
Data it's big, so, grab it, store it, analyse it, make it accessible...mine, warehouse and visualise...use the pictures in your mind and others will see it your way!
Unit - I
Mobile Computing – Mobile Computing Vs wireless Networking – Mobile Computing Applications – Characteristics of Mobile computing – Structure of Mobile Computing Application. MAC Protocols – Wireless MAC Issues – Fixed Assignment Schemes – Random Assignment Schemes – Reservation Based Schemes.
PDF version (with notes) of my talk at the ACM Data Mining Unconference on 01 Nov 2009. How to use an open source stack (Hadoop, Cascading, Bixo) in EC2 for cost effective, scalable and reliable web mining.
This Presentation covers data mining, data mining techniques,
data analysis, data mining subtypes, uses of data mining, sources of data for mining, privacy concerns.
Big data-analytics-changing-way-organizations-conducting-businessAmit Bhargava
Hi Friends ,
There is an interesting post on how to leveraging Big data analytics in an Integrated GRC Environment in an Organize to have visibility in core enterprises issues on real time basis . This presentation is from Metric stream -an international and Global GRC soloutioning providers in association with Dr. Kirk. D. Borne - Big data consultant and Adviser .Hope you like it and enjoy as well.
Efficient Data Filtering Algorithm for Big Data Technology in Telecommunicati...Onyebuchi nosiri
Efficient data filtering algorithm for Big Data technology Telecommunication is a concept aimed at effectively filtering desired information for preventive purposes, the challenges posed by unprecedented rise in volume, variety and velocity of information has necessitated the need for exploring various methods Big Data which is simply a data sets that are so large and complex that traditional data processing tools and technologies cannot cope with is been considered. The process of examining such data to uncover hidden patterns in them was evolved, this was achieved by coming up with an Algorithm comprising of various stages like Artificial neural Network, Backtracking Algorithm, Depth First Search, Branch and Bound and dynamic programming and error check. The algorithm developed gave rise to the flowchart, with each line of block representing a sub-algorithm.
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.
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.
Introduction to AI for Nonprofits with Tapp NetworkTechSoup
Dive into the world of AI! Experts Jon Hill and Tareq Monaur will guide you through AI's role in enhancing nonprofit websites and basic marketing strategies, making it easy to understand and apply.
Normal Labour/ Stages of Labour/ Mechanism of LabourWasim Ak
Normal labor is also termed spontaneous labor, defined as the natural physiological process through which the fetus, placenta, and membranes are expelled from the uterus through the birth canal at term (37 to 42 weeks
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.
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.
Francesca Gottschalk - How can education support child empowerment.pptxEduSkills OECD
Francesca Gottschalk from the OECD’s Centre for Educational Research and Innovation presents at the Ask an Expert Webinar: How can education support child empowerment?
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
Model Attribute Check Company Auto PropertyCeline George
In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
2. Data Mining
New buzzword, old idea.
Inferring new information from already
collected data.
Traditionally job of Data Analysts
Computers have changed this.
Far more efficient to comb through data using
a machine than eyeballing statistical data.
3. Data Mining – Two Main Components
Wikipedia definition: “Data mining is the entire process of applying
computer-based methodology, including new techniques for knowledge
discovery, from data.”
Knowledge Discovery
Concrete information gleaned from known data. Data you may not have
known, but which is supported by recorded facts.
(ie: Diapers and beer example from previous presentation)
Knowledge Prediction
Uses known data to forecast future trends, events, etc. (ie: Stock market
predictions)
Wikipedia note: "some data mining systems such as neural networks are
inherently geared towards prediction and pattern recognition, rather than
knowledge discovery.“ These include applications in AI and Symbol
analysis
4. Data Mining vs. Data Analysis
In terms of software and the marketing thereof
Data Mining != Data Analysis
Data Mining implies software uses some intelligence
over simple grouping and partitioning of data to
infer new information.
Data Analysis is more in line with standard
statistical software (ie: web stats). These usually
present information about subsets and relations
within the recorded data set (ie: browser/search
engine usage, average visit time, etc. )
5. Data Mining Subtypes
Data Dredging
The process of scanning a data set for relations and then
coming up with a hypothesis for existence of those relations.
MetaData
Data that describes other data. Can describe an individual
element, or a collection of elements.
Wikipedia example: “In a library, where the data is the
content of the titles stocked, metadata about a title would
typically include a description of the content, the author, the
publication date and the physical location”
Applications for Data Dredging in business include Market
and Risk Analysis, as well as trading strategies.
Applications for Science include disaster prediction.
6. Propositional vs. Relational Data
Old data mining methods relied on Propositional Data, or
data that was related to a single, central element, that could
be represented in a vector format. (ie: the purchasing history
of a single user. Amazon uses such vectors in its related item
suggestions [a multidimensional dot product])
Current, advanced data mining methods rely on Relational
Data, or data that can be stored and modeled easily through
use of relational databases. An example of this would be data
used to represent interpersonal relations.
Relational Data is more interesting than Propositional data to
miners in the sense that an entity, and all the entities to which
it is related, factor into the data inference process.
7. Key Component of Data Mining
Whether Knowledge Discovery or Knowledge
Prediction, data mining takes information that was
once quite difficult to detect and presents it in an
easily understandable format (ie: graphical or
statistical)
Data mining Techniques involve sophisticated
algorithms, including Decision Tree Classifications,
Association detection, and Clustering.
Since Data mining is not on test, I will keep things
superficial.
8. Uses of Data Mining
AI/Machine Learning
Combinatorial/Game Data Mining
Good for analyzing winning strategies to games, and thus
developing intelligent AI opponents. (ie: Chess)
Business Strategies
Market Basket Analysis
Identify customer demographics, preferences, and purchasing
patterns.
Risk Analysis
Product Defect Analysis
Analyze product defect rates for given plants and predict
possible complications (read: lawsuits) down the line.
9. Uses of Data Mining (Continued)
User Behavior Validation
Fraud Detection
In the realm of cell phones
Comparing phone activity to calling records.
Can help detect calls made on cloned phones.
Similarly, with credit cards, comparing
purchases with historical purchases. Can
detect activity with stolen cards.
10. Uses of Data Mining (Continued)
Health and Science
Protein Folding
Predicting protein interactions and functionality within
biological cells. Applications of this research include
determining causes and possible cures for Alzheimers,
Parkinson's, and some cancers (caused by protein "misfolds")
Extra-Terrestrial Intelligence
Scanning Satellite receptions for possible transmissions from
other planets.
For more information see Stanford’s Folding@home and
SETI@home projects. Both involve participation in a widely
distributed computer application.
11. Sources of Data for Mining
Databases (most obvious)
Text Documents
Computer Simulations
Social Networks
12. Privacy Concerns
Mining of public and government databases is done,
though people have, and continue to raise concerns.
Wiki quote:
"data mining gives information that would not be
available otherwise. It must be properly interpreted
to be useful. When the data collected involves
individual people, there are many questions
concerning privacy, legality, and ethics."
13. Prevalence of Data Mining
Your data is already being mined, whether you like it or not.
Many web services require that you allow access to your information [for
data mining] in order to use the service.
Google mines email data in Gmail accounts to present account owners
with ads.
Facebook requires users to allow access to info from non-Facebook
pages. Facebook privacy policy:
"We may use information about you that we collect from other sources,
including but not limited to newspapers and Internet sources such as
blogs, instant messaging services and other users of Facebook, to
supplement your profile.
This allows access to your blog RSS feed (rather innocuous), as well as
information obtained through partner sites (worthy of concern).
14. Data Mining Controversies
Latest one: Facebook's Beacon Advertising program
(Just popped on Slashdot within the last week)
What Beacon does:
“when you engage in consumer activity at a
[Facebook] partner website, such as Amazon, eBay,
or the New York Times, not only will Facebook
record that activity, but your Facebook connections
will also be informed of your purchases or actions.”
[taken from
http://trickytrickywhiteboy.blogspot.com/2007/11/be
ware-of-facebooks-beacon.html]
15. Controversies continued
Implications: "Thus where Facebook used to be collecting data only
within the confines of its own website, it will now extend that ability to
harvest data across other websites that it partners with. Some of the
companies that have signed on to participate on the advertising side
include Coca-Cola, Sony, Verizon, Comcast, Ebay — and the CBC. The
initial list of 44 partner websites participating on the data collection side
include the New York Times, Blockbuster, Amazon, eBay, LiveJournal,
and Epicurious.”
[Remember the privacy policy on the previous slide]
Verdict is still out. This may violate an old (100+ years) New York law
prohibiting advertising using endorsements without the endorsee’s
consent.
Facebook currently offers users no way to opt out of Beacon (once it has
been activated ?). Users can close the accounts, but account data is never
deleted.
16. Bottom Line
Data obtained through Data Mining is
incredibly valuable
Companies are understandably reluctant to
give up data they have obtained.
Expect to see prevalence of Data Mining and
(possibly subversive) methods increase in
years to come.
17. Recommended Resources and
Works Consulted
Wikipedia Data Mining entry
http://en.wikipedia.org/wiki/Data_mining
"Privacy is Dead - Get Over It: Revisited"
Steve Rambam's Hope Number Six lecture
http://www.hopenumbersix.net/speakers.html#pid2
Facebook's Faux Pas
http://www.newsweek.com/id/69275
Beware of Facebook’s Beacon
http://trickytrickywhiteboy.blogspot.com/2007/11/beware-of-facebooks-beacon.html
Facebook Data Mining guide
http://saunderslog.com/2007/11/25/facebook-market-research-secrets/
Data Mining in Social Networks
http://kdl.cs.umass.edu/papers/jensen-neville-nas2002.pdf