The document discusses data mining and knowledge discovery from large data sets. It begins by defining the terms data, information, knowledge, and wisdom in a hierarchy. It then explains that the growth of data from various sources has created a need for data mining to extract useful knowledge from large data repositories. The key aspects of data mining discussed are that it aims to discover previously unknown, implicit and potentially useful patterns from large data sets in an automated manner. The document outlines the interdisciplinary nature of data mining and its relationship to knowledge discovery in databases. It describes the types of data that can be mined, including structured, transactional, time-series and web data, as well as common data mining tasks like classification, prediction and clustering.
This Presentation covers data mining, data mining techniques,
data analysis, data mining subtypes, uses of data mining, sources of data for mining, privacy concerns.
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This Presentation covers data mining, data mining techniques,
data analysis, data mining subtypes, uses of data mining, sources of data for mining, privacy concerns.
=> Data Mining Services
We are a full service data mining company. We handle projects both large and small, with the help of competent staff which is able to address any of the data mining needs of your company.
- Web Data Mining
- Social Media Data Mining
- SQL Data Mining
- Image Data Mining
- Excel Data Mining
- Word Data Mining
- PDF Data Mining
- Open Source Data Mining
Website: http://datacleaningservices.com/
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
"Touch you future and give the perfect shape to your career by getting Training on hadoop, big data, spark scala, cloud computing and Hadoop Ecosystem combo by IT Experts"
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
"Touch you future and give the perfect shape to your career by getting Training on hadoop, big data, spark scala, cloud computing and Hadoop Ecosystem combo by IT Experts"
6 weeks summer training in data mining,jalandhardeepikakaler1
e2matrix is a leading Web Design and Development Company now in the field of Industrial training. We provide you 6 Month/6 Week Industrial training in PhP,Web Designing, Java, Dot Net, android Applications.
we also provide work for various technoligies with additional facilities-
RESEARCH PAPERS
OBJECTIVES
SYNOPSIS
IMPLEMENTATION
DOCUMENTATION
REPORT WRITING
PAPER PUBLICATION
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Pizza, Handa City Center, Phagwara,punjab
email addres-e2matrixphagwara@gmail.com
jalandhare2matrix@gmail.com
WEBSITE-www.e2matrix.com
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09041262727
07508509730
7508509709
6months industrial training in data mining,ludhianadeepikakaler1
E2marix is leading Training & Certification Company offering Corporate Training Programs, IT Education Courses in diversified areas.Since its inception, E2matrix educational Services have trained and certified many students and professionals.
TECHNOLOGIES PROVIDED -
MATLAB
NS2
IMAGE PROCESSING
.NET
SOFTWARE TESTING
DATA MINING
NEURAL networks
HFSS
WEKA
ANDROID
CLOUD computing
COMPUTER NETWORKS
FUZZY LOGIC
ARTIFICIAL INTELLIGENCE
LABVIEW
EMBEDDED
VLSI
Address
Opp. Phagwara Bus Stand, Above Bella
Pizza, Handa City Center, Phagwara
email-e2matrixphagwara@gmail.com
jalandhare2matrix@gmail.com
Web site-www.e2matrix.com
CONTACT NUMBER --
07508509730
09041262727
7508509709
6months industrial training in data mining, jalandhardeepikakaler1
e2matrix is a leading Web Design and Development Company now in the field of Industrial training. We provide you 6 Month/6 Week Industrial training in PhP,Web Designing, Java, Dot Net, android Applications.
we also provide work for various technoligies with additional facilities-
RESEARCH PAPERS
OBJECTIVES
SYNOPSIS
IMPLEMENTATION
DOCUMENTATION
REPORT WRITING
PAPER PUBLICATION
Address-Opp. Phagwara Bus Stand, Above Bella
Pizza, Handa City Center, Phagwara,punjab
email addres-e2matrixphagwara@gmail.com
jalandhare2matrix@gmail.com
WEBSITE-www.e2matrix.com
CONTACT NUMBER --
09041262727
07508509730
7508509709
6 weeks summer training in data mining,ludhianadeepikakaler1
E2marix is leading Training & Certification Company offering Corporate Training Programs, IT Education Courses in diversified areas.Since its inception, E2matrix educational Services have trained and certified many students and professionals.
TECHNOLOGIES PROVIDED -
MATLAB
NS2
IMAGE PROCESSING
.NET
SOFTWARE TESTING
DATA MINING
NEURAL networks
HFSS
WEKA
ANDROID
CLOUD computing
COMPUTER NETWORKS
FUZZY LOGIC
ARTIFICIAL INTELLIGENCE
LABVIEW
EMBEDDED
VLSI
Address
Opp. Phagwara Bus Stand, Above Bella
Pizza, Handa City Center, Phagwara
email-e2matrixphagwara@gmail.com
jalandhare2matrix@gmail.com
Web site-www.e2matrix.com
CONTACT NUMBER --
07508509730
09041262727
7508509709
Jiawei Han, Micheline Kamber and Jian Pei
Data Mining: Concepts and Techniques, 3rd ed.
The Morgan Kaufmann Series in Data Management Systems
Morgan Kaufmann Publishers, July 2011. ISBN 978-0123814791
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Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
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Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
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After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
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Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
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Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
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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
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
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.
GraphRAG is All You need? LLM & Knowledge GraphGuy Korland
Guy Korland, CEO and Co-founder of FalkorDB, will review two articles on the integration of language models with knowledge graphs.
1. Unifying Large Language Models and Knowledge Graphs: A Roadmap.
https://arxiv.org/abs/2306.08302
2. Microsoft Research's GraphRAG paper and a review paper on various uses of knowledge graphs:
https://www.microsoft.com/en-us/research/blog/graphrag-unlocking-llm-discovery-on-narrative-private-data/
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
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The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
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Data mining and knowledge discovery
1. 1
From Data to Wisdom
Data
The raw material of
information
Information
Data organized and
presented by someone
Knowledge
Information read, heard or
seen and understood and
integrated
Wisdom
Distilled knowledge and
understanding which can
lead to decisions
Wisdom
Knowledge
Information
Data
The Information Hierarchy
2. Why Data Mining?
The Explosive Growth of Data: from terabytes to
petabytes
Data collection and data availability
Automated data collection tools, database systems, Web,
computerized society
Major sources of abundant data
Business: Web, e-commerce, transactions, stocks, …
Science: Remote sensing, bioinformatics, scientific simulation, …
Society and everyone: news, images, video, documents
Internet …
2
4. How much data?
Google: ~20-30 PB a day
Wayback Machine has ~4 PB + 100-200 TB/month
Facebook: ~3 PB of user data + 25 TB/day
eBay: ~7 PB of user data + 50 TB/day
CERN’s Large Hydron Collider generates 15 PB a year
In 2010, enterprises stored 7 Exabytes = 7,000,000,000 GB
640K ought to be
enough for anybody.
5. Big Data Growing
5
The Untapped Data Gap:
Most of the useful data will
not be tagged or analyzed –
partly due to skill shortage
IDC predicts: From 2005 to 2020, the
digital universe will double every 2
years and grow from 130 exabytes to
40,000 exabytes
or 5,200 GB / person in 2020.
6. What Is Data Mining?
We are drowning in data, but starving for knowledge!
“Necessity is the mother of invention”—Data mining—
Automated analysis of massive data sets
6
The non-trivial extraction of implicit, previously unknown and
potentially useful knowledge from data in large data repositories
Data Mining: A Definition
Non-trivial: obvious knowledge is not useful
implicit: hidden difficult to observe knowledge
previously unknown
potentially useful: actionable; easy to understand
7. 7
Data Mining: Confluence of Multiple Disciplines
Data Mining
Machine
Learning
Statistics
Applications
Algorithm
Pattern
Recognition
High-Performance
Computing
Visualization
Database
Technology
8. 8
Data Mining’s Virtuous Cycle
1. Identifying the problem
2. Mining data to transform it into actionable
information
3. Acting on the information
4. Measuring the results
9. 9
The Knowledge Discovery Process
Data Mining v. Knowledge Discovery in Databases (KDD)
DM and KDD are often used interchangeably
actually, DM is only part of the KDD process
- The KDD Process
10. 10
Types of Knowledge Discovery
Two kinds of knowledge discovery: directed and undirected
Directed Knowledge Discovery
Purpose: Explain value of some field in terms of all the others (goal-oriented)
Method: select the target field based on some hypothesis about the data; ask the
algorithm to tell us how to predict or classify new instances
Examples:
what products show increased sale when cream cheese is discounted
which banner ad to use on a web page for a given user coming to the site
Undirected Knowledge Discovery
Purpose: Find patterns in the data that may be interesting (no target field)
Method: clustering, affinity grouping
Examples:
which products in the catalog often sell together
market segmentation (find groups of customers/users with similar
characteristics or behavioral patterns)
12. 12
Data Mining: On What Kinds of Data?
Database-oriented data sets and applications
Relational database, data warehouse, transactional database
Object-relational databases, Heterogeneous databases and legacy databases
Advanced data sets and advanced applications
Data streams and sensor data
Time-series data, temporal data, sequence data (incl. bio-sequences)
Structure data, graphs, social networks and information networks
Spatial data and spatiotemporal data
Multimedia database
Text databases
The World-Wide Web
13. 13
Data Mining: What Kind of Data?
Structured Databases
relational, object-relational, etc.
can use SQL to perform parts of the process
e.g., SELECT count(*) FROM Items WHERE
type=video GROUP BY category
14. 14
Data Mining: What Kind of Data?
Flat Files
most common data source
can be text (or HTML) or binary
may contain transactions, statistical data, measurements, etc.
Transactional databases
set of records each with a transaction id, time stamp, and a set of items
may have an associated “description” file for the items
typical source of data used in market basket analysis
15. 15
Data Mining: What Kind of Data?
Other Types of Databases
legacy databases
multimedia databases (usually very high-dimensional)
spatial databases (containing geographical information, such as maps, or
satellite imaging data, etc.)
Time Series Temporal Data (time dependent information such as stock market
data; usually very dynamic)
World Wide Web
basically a large, heterogeneous, distributed database
need for new or additional tools and techniques
information retrieval, filtering and extraction
agents to assist in browsing and filtering
Web content, usage, and structure (linkage) mining tools
The “social Web”
User generated meta-data, social networks, shared resources, etc.
16. 16
What Can Data Mining Do
Many Data Mining Tasks
often inter-related
often need to try different techniques/algorithms for each task
each tasks may require different types of knowledge discovery
What are some of data mining tasks
Classification
Prediction
Clustering
Affinity Grouping / Association discovery
Sequence Analysis
Characterization
Discrimination
17. 17
Some Applications of Data mining
Business data analysis and decision support
Marketing focalization
Recognizing specific market segments that respond to particular
characteristics
Return on mailing campaign (target marketing)
Customer Profiling
Segmentation of customer for marketing strategies and/or product
offerings
Customer behavior understanding
Customer retention and loyalty
Mass customization / personalization
18. 18
Some Applications of Data mining
Business data analysis and decision support (cont.)
Market analysis and management
Provide summary information for decision-making
Market basket analysis, cross selling, market segmentation.
Resource planning
Risk analysis and management
"What if" analysis
Forecasting
Pricing analysis, competitive analysis
Time-series analysis (Ex. stock market)
19. 19
Some Applications of Data mining
Fraud detection
Detecting telephone fraud:
Telephone call model: destination of the call, duration, time of day or week
Analyze patterns that deviate from an expected norm
British Telecom identified discrete groups of callers with frequent intra-group calls,
especially mobile phones, and broke a multimillion dollar fraud scheme
Detection of credit-card fraud
Detecting suspicious money transactions (money laundering)
Text mining:
Message filtering (e-mail, newsgroups, etc.)
Newspaper articles analysis
Text and document categorization
Web Mining
Mining patterns from the content, usage, and structure of Web resources
20. Types of Web Mining
Web Content
Mining
Web Structure
Mining
Web Usage
Mining
Web Mining
20
21. Types of Web Mining
Web Content
Mining
Web Structure
Mining
Web Usage
Mining
Web Mining
21
Applications:
• document clustering or
categorization
• topic identification / tracking
• concept discovery
• focused crawling
• content-based personalization
• intelligent search tools
22. Types of Web Mining
Web Content
Mining
Web Structure
Mining
Web Usage
Mining
Web Mining
Applications:
• user and customer behavior modeling
• Web site optimization
• e-customer relationship management
• Web marketing
• targeted advertising
• recommender systems
22
23. Types of Web Mining
Web Content
Mining
Web Structure
Mining
Web Usage
Mining
Web Mining
Applications:
• document retrieval and
ranking (e.g., Google)
• discovery of “hubs” and
“authorities”
• discovery of Web
communities
• social network analysis
23
24. 24
The Knowledge Discovery Process
- The KDD Process
Next: We first focus on understanding the data and data
preparation/transformation