The data lake has become extremely popular, but there is still confusion on how it should be used. In this presentation I will cover common big data architectures that use the data lake, the characteristics and benefits of a data lake, and how it works in conjunction with a relational data warehouse. Then I’ll go into details on using Azure Data Lake Store Gen2 as your data lake, and various typical use cases of the data lake. As a bonus I’ll talk about how to organize a data lake and discuss the various products that can be used in a modern data warehouse.
Data Science Tutorial | What is Data Science? | Data Science For Beginners | ...Edureka!
** Data Science Certification using R: https://www.edureka.co/data-science **
In this PPT on Data Science Tutorial, you’ll get an in-depth understanding of Data Science and you’ll also learn how it is used in the real world to solve data-driven problems. It’ll cover the following topics in this session:
Need for Data Science
Walmart Use case
What is Data Science?
Who is a Data Scientist?
Data Science – Skill set
Data Science Job roles
Data Life cycle
Introduction to Machine Learning
K- Means Use case
K- Means Algorithm
Hands-On
Data Science certification
Blog Series: http://bit.ly/data-science-blogs
Data Science Training Playlist: http://bit.ly/data-science-playlist
Follow us to never miss an update in the future.
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
The data lake has become extremely popular, but there is still confusion on how it should be used. In this presentation I will cover common big data architectures that use the data lake, the characteristics and benefits of a data lake, and how it works in conjunction with a relational data warehouse. Then I’ll go into details on using Azure Data Lake Store Gen2 as your data lake, and various typical use cases of the data lake. As a bonus I’ll talk about how to organize a data lake and discuss the various products that can be used in a modern data warehouse.
Data Science Tutorial | What is Data Science? | Data Science For Beginners | ...Edureka!
** Data Science Certification using R: https://www.edureka.co/data-science **
In this PPT on Data Science Tutorial, you’ll get an in-depth understanding of Data Science and you’ll also learn how it is used in the real world to solve data-driven problems. It’ll cover the following topics in this session:
Need for Data Science
Walmart Use case
What is Data Science?
Who is a Data Scientist?
Data Science – Skill set
Data Science Job roles
Data Life cycle
Introduction to Machine Learning
K- Means Use case
K- Means Algorithm
Hands-On
Data Science certification
Blog Series: http://bit.ly/data-science-blogs
Data Science Training Playlist: http://bit.ly/data-science-playlist
Follow us to never miss an update in the future.
Instagram: https://www.instagram.com/edureka_learning/
Facebook: https://www.facebook.com/edurekaIN/
Twitter: https://twitter.com/edurekain
LinkedIn: https://www.linkedin.com/company/edureka
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.
data mining, data preprocessing, data cleaning, knowledge discovery, association, classification, clustering, introduction, why data mining, application
Broad introduction to information retrieval and web search, used to teaching at the Yahoo Bangalore Summer School 2013. Slides are a mash-up from my own and other people's presentations.
Slides for my Associate Professor (oavlönad docent) lecture.
The lecture is about Data Streaming (its evolution and basic concepts) and also contains an overview of my research.
What Is Data Science? | Introduction to Data Science | Data Science For Begin...Simplilearn
This Data Science Presentation will help you in understanding what is Data Science, why we need Data Science, prerequisites for learning Data Science, what does a Data Scientist do, Data Science lifecycle with an example and career opportunities in Data Science domain. You will also learn the differences between Data Science and Business intelligence. The role of a data scientist is one of the sexiest jobs of the century. The demand for data scientists is high, and the number of opportunities for certified data scientists is increasing. Every day, companies are looking out for more and more skilled data scientists and studies show that there is expected to be a continued shortfall in qualified candidates to fill the roles. So, let us dive deep into Data Science and understand what is Data Science all about.
This Data Science Presentation will cover the following topics:
1. Need for Data Science?
2. What is Data Science?
3. Data Science vs Business intelligence
4. Prerequisites for learning Data Science
5. What does a Data scientist do?
6. Data Science life cycle with use case
7. Demand for Data scientists
This Data Science with Python course will establish your mastery of data science and analytics techniques using Python. With this Python for Data Science Course, you’ll learn the essential concepts of Python programming and become an expert in data analytics, machine learning, data visualization, web scraping and natural language processing. Python is a required skill for many data science positions, so jumpstart your career with this interactive, hands-on course.
Why learn Data Science?
Data Scientists are being deployed in all kinds of industries, creating a huge demand for skilled professionals. Data scientist is the pinnacle rank in an analytics organization. Glassdoor has ranked data scientist first in the 25 Best Jobs for 2016, and good data scientists are scarce and in great demand. As a data you will be required to understand the business problem, design the analysis, collect and format the required data, apply algorithms or techniques using the correct tools, and finally make recommendations backed by data.
The Data Science with python is recommended for:
1. Analytics professionals who want to work with Python
2. Software professionals looking to get into the field of analytics
3. IT professionals interested in pursuing a career in analytics
4. Graduates looking to build a career in analytics and data science
5. Experienced professionals who would like to harness data science in their fields
text mining, data mining, machine learning, unstructured data, big data, database, data warehouse, text mining (industry), research (industry), text analysis, text, text analytics, unstructured, data science, structured data, advanced analytics, what is data mining, data mining lecture, data mining techniques, information, learning from data, computre technolog, technology, data process, data mining tutorial,
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.
data mining, data preprocessing, data cleaning, knowledge discovery, association, classification, clustering, introduction, why data mining, application
Broad introduction to information retrieval and web search, used to teaching at the Yahoo Bangalore Summer School 2013. Slides are a mash-up from my own and other people's presentations.
Slides for my Associate Professor (oavlönad docent) lecture.
The lecture is about Data Streaming (its evolution and basic concepts) and also contains an overview of my research.
What Is Data Science? | Introduction to Data Science | Data Science For Begin...Simplilearn
This Data Science Presentation will help you in understanding what is Data Science, why we need Data Science, prerequisites for learning Data Science, what does a Data Scientist do, Data Science lifecycle with an example and career opportunities in Data Science domain. You will also learn the differences between Data Science and Business intelligence. The role of a data scientist is one of the sexiest jobs of the century. The demand for data scientists is high, and the number of opportunities for certified data scientists is increasing. Every day, companies are looking out for more and more skilled data scientists and studies show that there is expected to be a continued shortfall in qualified candidates to fill the roles. So, let us dive deep into Data Science and understand what is Data Science all about.
This Data Science Presentation will cover the following topics:
1. Need for Data Science?
2. What is Data Science?
3. Data Science vs Business intelligence
4. Prerequisites for learning Data Science
5. What does a Data scientist do?
6. Data Science life cycle with use case
7. Demand for Data scientists
This Data Science with Python course will establish your mastery of data science and analytics techniques using Python. With this Python for Data Science Course, you’ll learn the essential concepts of Python programming and become an expert in data analytics, machine learning, data visualization, web scraping and natural language processing. Python is a required skill for many data science positions, so jumpstart your career with this interactive, hands-on course.
Why learn Data Science?
Data Scientists are being deployed in all kinds of industries, creating a huge demand for skilled professionals. Data scientist is the pinnacle rank in an analytics organization. Glassdoor has ranked data scientist first in the 25 Best Jobs for 2016, and good data scientists are scarce and in great demand. As a data you will be required to understand the business problem, design the analysis, collect and format the required data, apply algorithms or techniques using the correct tools, and finally make recommendations backed by data.
The Data Science with python is recommended for:
1. Analytics professionals who want to work with Python
2. Software professionals looking to get into the field of analytics
3. IT professionals interested in pursuing a career in analytics
4. Graduates looking to build a career in analytics and data science
5. Experienced professionals who would like to harness data science in their fields
text mining, data mining, machine learning, unstructured data, big data, database, data warehouse, text mining (industry), research (industry), text analysis, text, text analytics, unstructured, data science, structured data, advanced analytics, what is data mining, data mining lecture, data mining techniques, information, learning from data, computre technolog, technology, data process, data mining tutorial,
My keynote talk at San Diego Superdata conference, looking at history and current state of Analytics and Data Mining, and examining the effects of Big Data
Machine Learning and Data Mining: 19 Mining Text And Web DataPier Luca Lanzi
Course "Machine Learning and Data Mining" for the degree of Computer Engineering at the Politecnico di Milano. In this lecture we overview text and web mining. The slides are mainly taken from Jiawei Han textbook.
There are as many views and definitions of Data Mining as there are people working in and on the topic. Confusion reigns and people ask; what is it; why do we need it; and isn’t it just Data Mining rebranded? In this slide deck and presentation we set the scene an highlight the differences and need for Data Mining in order to give a framework for case studies and future projects.
So - why do we need it?
The economic, industrial, commercial, social, political and sustainability problems we face cannot be successfully addressed using the management techniques and models largely inherited from the Industrial Revolution. The world no longer appears infinite in resources, slow paced, linear and stable. We now see the limitations; feel the impact of rapid change; and we can conceptualize the non-linear and unstable nature of it all! We are also starting to comprehend the scale and the need for machine assistance.
Modeling our situation !
Sophisticated computer models for weather systems are now complemented by ecological, economic, conflict and resource modeling of varying depth and accuracy. However, the key is always the accuracy and coverage of the primary data. We started with modest databases and data mining, but they mostly proved inadequate, and we are now amassing vast databases on every aspect of life - people, planet and machines. This ‘BIG DATA’ explosion demands a rethink of how, what, and where we gather data; the way we analyze and model; and the way we make decisions.
So - what is the big difference?
Data Mining was limited, planer, simple, linear and constrained to a few relationships amongst people: what they did, where they went, who they knew and so on. In contrast; Big Data is unbounded, spans all peoples and machines in all domains and activities with application to every aspect of life, business, industry, government and sustainability etc. It also takes into account the non-linear nature of relationships and events.
“Big Data is an almost unconscious outcome of the desire and need to sustain all peoples on a rapidly smaller looking planet”
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
Slides for the 2016/2017 edition of the Data Mining and Text Mining Course at the Politecnico di Milano. The course is also part of the joint program with the University of Illinois at Chicago.
Data mining Course
Chapter 1
Definition of Data Mining
Data Mining as an Interdisciplinary field
The process of Data Mining
Data Mining Tasks
Challenges of Data Mining
Data mining application examples
Introduction to RapidMiner
ODSC East 2017: Data Science Models For GoodKarry Lu
Abstract: The rise of data science has been largely fueled by the promise of changing the business landscape - enhancing one's competitive advantage, increasing business optimization and efficiency, and ultimately delivering a better bottom-line. This promise reaches across sectors as machine learning methods are getting better, data access continues to grow, and computation power is easily accessible. However, because the practice of doing data science can be expensive, there is a danger that this so-called promise of data science may only be available to the most well-resourced organizations with sophisticated data capabilities and staff. For the past five years, DataKind has been working to ensure social change organizations too have access to data science, teaming them up with data scientists to build machine learning and artificial intelligence solutions that aim to reduce human suffering. In doing so, DataKind has learned what it takes to apply data science in the social sector and the many applications it has for creating positive change in the world. This session presents DataKind projects showcasing the wide range of applications for ML/AI for social good. From using satellite imagery and remote sensing techniques to detect wheat farm boundaries to protect livelihoods in Ethiopia, to leveraging NLP to automate the time consuming process of synthesizing findings from academic studies to inform conservation efforts and to classifying text records to better understand human rights conditions across the world to using machine learning to reduce traffic fatalities in U.S. cities, learn about some of the latest breakthroughs and findings in the data science for social good space and learn how you can get involved
Linked Data and Semantic Technologies can support a next generation of science. This talk shows examples of discovery, access, integration, analysis, and shows directions towards prediction and vision.
Introduction To Data Mining: Introduction - The evolution of database
system technology - Steps in knowledge discovery from database process
- Architecture of a data mining systems - Data mining on different kinds
of data - Different kinds of pattern - Technologies used - Applications -
Major issues in data mining - Classification of data mining systems - Data
mining task primitives - Integration of a data mining system with a
database or data warehouse system.
First, Firster, Firstest: Three lessons from history on information overloadmark madsen
Keynote from the 2011 Strata New York conference.
The first person to conceive of something is usually not the first. They're the first to re-conceive at a point where the current technology caught up to someone else's idea. We're at a point today where many old ideas are being reinvented. Hear why looking to the past, beyond your core field of interest, is worthwhile.
Video can be found at http://www.youtube.com/watch?v=Qv0yF47L8WE
Big Data [sorry] & Data Science: What Does a Data Scientist Do?Data Science London
What 'kind of things' does a data scientist do? What are the foundations and principles of data science? What is a Data Product? What does the data science process looks like? Learning from data: Data Modeling or Algorithmic Modeling? - talk by Carlos Somohano @ds_ldn at The Cloud and Big Data: HDInsight on Azure London 25/01/13
Presented by Rob Hanna at 2012 STC Summit in Rosemont, IL.
Take a journey into the Information Ecosystem where you will discover how structured information lives within your organization. Content is all around you—in places you may least expect. It exhibits predictable properties and behaviors that will help you capture and classify information for better management of your content.
Slides for the 2016/2017 edition of the Data Mining and Text Mining Course at the Politecnico di Milano. The course is also part of the joint program with the University of Illinois at Chicago.
Slides for the 2016/2017 edition of the Data Mining and Text Mining Course at the Politecnico di Milano. The course is also part of the joint program with the University of Illinois at Chicago.
Slides for the 2016/2017 edition of the Data Mining and Text Mining Course at the Politecnico di Milano. The course is also part of the joint program with the University of Illinois at Chicago.
Slides for the 2016/2017 edition of the Data Mining and Text Mining Course at the Politecnico di Milano. The course is also part of the joint program with the University of Illinois at Chicago.
Slides for the 2016/2017 edition of the Data Mining and Text Mining Course at the Politecnico di Milano. The course is also part of the joint program with the University of Illinois at Chicago.
Slides for the 2016/2017 edition of the Data Mining and Text Mining Course at the Politecnico di Milano. The course is also part of the joint program with the University of Illinois at Chicago.
Slides for the 2016/2017 edition of the Data Mining and Text Mining Course at the Politecnico di Milano. The course is also part of the joint program with the University of Illinois at Chicago.
DMTM Lecture 13 Representative based clusteringPier Luca Lanzi
Slides for the 2016/2017 edition of the Data Mining and Text Mining Course at the Politecnico di Milano. The course is also part of the joint program with the University of Illinois at Chicago.
Slides for the 2016/2017 edition of the Data Mining and Text Mining Course at the Politecnico di Milano. The course is also part of the joint program with the University of Illinois at Chicago.
Slides for the 2016/2017 edition of the Data Mining and Text Mining Course at the Politecnico di Milano. The course is also part of the joint program with the University of Illinois at Chicago.
Slides for the 2016/2017 edition of the Data Mining and Text Mining Course at the Politecnico di Milano. The course is also part of the joint program with the University of Illinois at Chicago.
Slides for the 2016/2017 edition of the Data Mining and Text Mining Course at the Politecnico di Milano. The course is also part of the joint program with the University of Illinois at Chicago.
Slides for the 2016/2017 edition of the Data Mining and Text Mining Course at the Politecnico di Milano. The course is also part of the joint program with the University of Illinois at Chicago.
SAP Sapphire 2024 - ASUG301 building better apps with SAP Fiori.pdfPeter Spielvogel
Building better applications for business users with SAP Fiori.
• What is SAP Fiori and why it matters to you
• How a better user experience drives measurable business benefits
• How to get started with SAP Fiori today
• How SAP Fiori elements accelerates application development
• How SAP Build Code includes SAP Fiori tools and other generative artificial intelligence capabilities
• How SAP Fiori paves the way for using AI in SAP apps
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Epistemic Interaction - tuning interfaces to provide information for AI supportAlan Dix
Paper presented at SYNERGY workshop at AVI 2024, Genoa, Italy. 3rd June 2024
https://alandix.com/academic/papers/synergy2024-epistemic/
As machine learning integrates deeper into human-computer interactions, the concept of epistemic interaction emerges, aiming to refine these interactions to enhance system adaptability. This approach encourages minor, intentional adjustments in user behaviour to enrich the data available for system learning. This paper introduces epistemic interaction within the context of human-system communication, illustrating how deliberate interaction design can improve system understanding and adaptation. Through concrete examples, we demonstrate the potential of epistemic interaction to significantly advance human-computer interaction by leveraging intuitive human communication strategies to inform system design and functionality, offering a novel pathway for enriching user-system engagements.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
2. Lecture outline 2
Why Data Mining?
What is Data Mining?
What are the typical tasks?
What are the primitives?
What are the typical applications?
What are the major issues?
Prof. Pier Luca Lanzi
4. Why Data Mining? 4
“Necessity is the mother of invention”
Explosive Growth of Data
Terabytes of available data
Data collections and data availability
Major sources of abundant data
Pressing need for the automated analysis of massive data
Prof. Pier Luca Lanzi
5. Evolution of Database Technology 5
1960s:
Data collection, database creation, IMS and network DBMS
1970s:
Relational data model, relational DBMS implementation
1980s:
RDBMS, advanced data models
(extended-relational, OO, deductive, etc.)
Application-oriented DBMS
(spatial, scientific, engineering, etc.)
1990s:
Data mining, data warehousing, multimedia databases,
and Web databases
2000s
Stream data management and mining
Data mining and its applications
Web technology (XML, data integration)
Global information systems
Prof. Pier Luca Lanzi
6. Examples 6
In vitro fertilization
Given: embryos described by 60 features
Problem: selection of embryos that will survive
Data: historical records of embryos and outcome
Cow culling
Given: cows described by 700 features
Problem: selection of cows that should be culled
Data: historical records and farmers’ decisions
Prof. Pier Luca Lanzi
7. Examples 7
Customer attrition
Given: customer information for the past months
Problem: predict who is likely to attrite next month,
or estimate customer value
Data: historical customer records
Credit assessment
Given: a loan application
Problem: predict whether the bank should
approve the loan
Data: records from other loans
Prof. Pier Luca Lanzi
9. What is Data Mining? 9
The non-trivial process of identifying
valid
novel
potentially useful, and
ultimately understandable patterns in data.
Alternative names,
Data Fishing, Data Dredging (1960-)
Data Mining (1990-), used by DB and business
Knowledge Discovery in Databases (1989-), used by AI
Business Intelligence, Information Harvesting,
Information Discovery, Knowledge Extraction, ...
Currently, Data Mining and Knowledge Discovery
are used interchangeably
Prof. Pier Luca Lanzi
11. Example: Credit Risk 11
IF salary<k THEN not repaid
loan
k salary
Prof. Pier Luca Lanzi
12. Example: Credit Risk 12
Is it valid?
The pattern has to be valid with respect
to a certainty level (rule true for the 86%)
Is it novel?
The value k should be previously
unknown or obvious
Is it useful?
The pattern should provide information
useful to the bank for assessing credit risk
Is it understandable?
Prof. Pier Luca Lanzi
13. What is the general idea? 13
Build computer programs that sift through databases
automatically, seeking regularities or patterns
There will be problems
Most patterns are banal and uninteresting
Most patterns are spurious, inexact, or contingent on
accidental coincidences in the particular dataset used
Real data is imperfect: Some parts will be garbled,
and some will be missing
Algorithms need to be robust enough to cope with imperfect
data and to extract regularities that are inexact but useful
Prof. Pier Luca Lanzi
14. What are the related fields? 14
Machine
Visualization
Learning
Knowledge Discovery
And Data Mining
Statistics Databases
Prof. Pier Luca Lanzi
15. Statistics, Machine Learning, 15
and Data Mining
Statistics:
more theory-based, focused on testing hypotheses
Machine learning
more heuristic, focused on building program
that learns, more general than Data Mining
Knowledge Discovery
integrates theory and heuristics
focus on the entire process of discovery, including
data cleaning, learning, integration and visualization
Data Mining
focus on the algorithms to extract patterns from data
Distinctions are blurred!
Prof. Pier Luca Lanzi
16. Why Not Traditional Data Analysis? 16
Tremendous amount of data
High scalability to handle terabytes of data
High-dimensionality of data
Micro-array may have tens of thousands of dimensions
High complexity of data
Data streams and sensor data
Time-series data, temporal data, sequence data
Structure data, graphs, social networks
and multi-linked data
Heterogeneous databases and legacy databases
Spatial, spatiotemporal, multimedia, text and Web data
Software programs, scientific simulations
New and sophisticated applications
Prof. Pier Luca Lanzi
17. Knowledge Discovery Process 17
raw data
selection
cleaning
evaluation
transformation
mining
Prof. Pier Luca Lanzi
18. Knowledge Discovery Process 18
What are the main steps?
Learning the application domain to extract
relevant prior knowledge and goals
Data selection
Data cleaning
Data reduction and transformation
Mining
Select the mining approach: classification,
regression, association, clustering, etc.
Choosing the mining algorithm(s)
Perform mining: search for patterns of interest
Pattern evaluation and knowledge presentation
visualization, transformation,
removing redundant patterns, etc.
Use of discovered knowledge
Prof. Pier Luca Lanzi
19. Knowledge Discovery and 19
Business Intelligence
Increasing potential
to support business End User
decisions Making
Decisions
Data Presentation Business
Visualization Techniques Analyst
Data Mining Data
Information Discovery Analyst
Data Exploration
Statistical Analysis, Querying and Reporting
OLAP, MDA
Data Warehouses / Data Marts
DBA
Data Sources
Paper, Files, Information Providers, Database Systems, OLTP
Prof. Pier Luca Lanzi
20. Architecture of a Typical 20
Knowledge Discovery System
Graphical user interface
Pattern evaluation
KB
Data mining engine
Database or data warehouse server
DB DW
Prof. Pier Luca Lanzi
22. Major Data Mining Tasks 22
Classification: predicting an item class
Clustering: finding clusters in data
Associations: frequent occurring events…
Visualization: to facilitate human discovery
Summarization: describing a group
Deviation Detection: finding changes
Estimation: predicting a continuous value
Link Analysis: finding relationship
Prof. Pier Luca Lanzi
23. Data Mining Tasks: classification 23
IF salary<k THEN not repaid
loan
?
?
k salary
Prof. Pier Luca Lanzi
24. Data Mining Tasks: classification 24
Classification and Prediction
Finding models (functions) that describe
and distinguish classes or concepts
The goal is to describe the data or
to make future prediction
E.g., classify countries based on climate,
or classify cars based on gas mileage
Presentation: decision-tree, classification rule, neural
network
Prediction: Predict some unknown numerical values
Prof. Pier Luca Lanzi
26. Data Mining Tasks: clustering 26
Cluster analysis
The class label is unknown
Group data to form new classes, e.g., cluster houses to
find distribution patterns
Clustering based on the principle: maximizing the intra-
class similarity and minimizing the interclass similarity
Prof. Pier Luca Lanzi
27. Data Mining Tasks: associations 27
Bread Bread Steak Jam
Peanuts Jam Jam Soda
Milk Soda Soda Peanuts
Fruit Chips Chips Milk
Jam Milk Bread Fruit
Fruit
Is there something interesting?
Jam Fruit Fruit Fruit
Soda Soda Soda Peanuts
Chips Chips Peanuts Cheese
Milk Milk Milk Yogurt
Bread
Prof. Pier Luca Lanzi
28. Data Mining Tasks: associations 28
Association Rule Mining
Finds interesting associations and/or correlation
relationships among large set of data items.
E.g., 98% of people who purchase tires and auto
accessories also get automotive services done
Prof. Pier Luca Lanzi
29. Data Mining Tasks: others 29
Outlier analysis
Outlier: a data object that does not comply with the
general behavior of the data
It can be considered as noise or exception but is quite
useful in fraud detection, rare events analysis
Trend and evolution analysis
Trend and deviation: regression analysis
Sequential pattern mining, periodicity analysis
Similarity-based analysis
Text Mining, Graph Mining, Data Streams
Other pattern-directed or statistical analyses
Prof. Pier Luca Lanzi
30. Are all the “Discovered” Patterns 30
Interesting?
Data Mining may generate thousands of patterns,
not all of them are interesting.
Suggested approach: Human-centered, query-based,
focused mining
Interestingness measures: a pattern is interesting if it is
easily understood by humans, valid on new or test data with
some degree of certainty, potentially useful, novel, or
validates some hypothesis that a user seeks to confirm
Objective vs. subjective interestingness measures:
Objective: based on statistics and structures of patterns,
e.g., support, confidence, etc.
Subjective: based on user’s belief in the data, e.g.,
unexpectedness, novelty, etc.
Prof. Pier Luca Lanzi
31. Can we find all and only 31
interesting patterns?
Completeness: Find all the interesting patterns
Can a data mining system find all
the interesting patterns?
Association vs. classification vs. clustering
Optimization: Search for only interesting patterns:
Can a data mining system find only
the interesting patterns?
Approaches
• First general all the patterns and then filter out the
uninteresting ones.
• Generate only the interesting patterns—mining query
optimization
Prof. Pier Luca Lanzi
32. Data Mining tasks 32
General functionality
Descriptive data mining
Predictive data mining
Different views, different classifications
Kinds of data to be mined
Kinds of knowledge to be discovered
Kinds of techniques utilized
Kinds of applications adapted
Prof. Pier Luca Lanzi
34. Primitives that Define a Data Mining Task 34
Task-relevant data
Type of knowledge to be mined
Background knowledge
Pattern interestingness measurements
Visualization/presentation of discovered patterns
Prof. Pier Luca Lanzi
35. Primitive 1: 35
Task-Relevant Data
Database or data warehouse name
Database tables or data warehouse cubes
Condition for data selection
Relevant attributes or dimensions
Data grouping criteria
Prof. Pier Luca Lanzi
36. Primitive 2: 36
Types of Knowledge to Be Mined
Characterization
Discrimination
Association
Classification/prediction
Clustering
Outlier analysis
Other data mining tasks
Prof. Pier Luca Lanzi
37. Primitive 3: 37
Background Knowledge
A typical kind of background knowledge: Concept hierarchies
Schema hierarchy
E.g., Street < City < ProvinceOrState < Country
Set-grouping hierarchy
E.g., {20-39} = young, {40-59} = middle_aged
Operation-derived hierarchy
email address: hagonzal@cs.uiuc.edu
login-name < department < university < country
Rule-based hierarchy
LowProfitMargin (X) <= Price(X, P1) and Cost (X, P2)
and (P1 - P2) < $50
Prof. Pier Luca Lanzi
39. Primitive 5: 39
Presentation of Discovered Patterns
Different backgrounds/usages may require
different forms of representation
E.g., rules, tables, crosstabs, pie/bar chart, etc.
Concept hierarchy is also important
Discovered knowledge might be more understandable
when represented at high level of abstraction
Interactive drill up/down, pivoting, slicing and dicing
provide different perspectives to data
Different kinds of knowledge require different representation:
association, classification, clustering, etc.
Prof. Pier Luca Lanzi
40. Integration of Data Mining and 40
Data Warehousing
Data mining systems, DBMS, Data warehouse systems
coupling
No coupling, loose-coupling, semi-tight-coupling, tight-
coupling
On-line analytical mining data
integration of mining and OLAP technologies
Interactive mining multi-level knowledge
Necessity of mining knowledge and patterns at different
levels of abstraction by drilling/rolling, pivoting,
slicing/dicing, etc.
Integration of multiple mining functions
Characterized classification, first clustering and then
association
Prof. Pier Luca Lanzi
41. Coupling Data Mining with 41
Data bases and Datawarehouses
No coupling—flat file processing, not recommended
Loose coupling
Fetching data from DB/DW
Semi-tight coupling—enhanced DM performance
Provide efficient implement a few data mining primitives
in a DB/DW system, e.g., sorting, indexing, aggregation,
histogram analysis, multiway join, precomputation of
some stat functions
Tight coupling—A uniform information processing
environment
DM is smoothly integrated into a DB/DW system, mining
query is optimized based on mining query, indexing,
query processing methods, etc.
Prof. Pier Luca Lanzi
43. Major Issues in Data Mining 43
Mining methodology
Mining different kinds of knowledge from diverse data types, e.g., bio,
stream, Web
Performance: efficiency, effectiveness, and scalability
Pattern evaluation: the interestingness problem
Incorporation of background knowledge
Handling noise and incomplete data
Parallel, distributed and incremental mining methods
Integration of the discovered knowledge with existing one: knowledge
fusion
User interaction
Data mining query languages and ad-hoc mining
Expression and visualization of data mining results
Interactive mining of knowledge at multiple levels of abstraction
Applications and social impacts
Domain-specific data mining & invisible data mining
Protection of data security, integrity, and privacy
Prof. Pier Luca Lanzi
45. Summary 45
Data mining: Discovering interesting patterns
from large amounts of data
A natural evolution of database technology,
in great demand, with wide applications
A KDD process includes data cleaning, data integration,
data selection, transformation, data mining,
pattern evaluation, and knowledge presentation
Data mining functionalities: characterization, discrimination,
association, classification, clustering,
outlier and trend analysis, etc.
Data mining systems and architectures
Major issues in data mining
Prof. Pier Luca Lanzi