Data Mining is newly technology and it's very useful for Data analytics for business analysis purpose and decision making data. This PPT described Data Mining in very easy way.
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.
Data Mining, KDD Process, Data mining functionalities, Characterization,
Discrimination ,
Association,
Classification,
Prediction,
Clustering,
Outlier analysis, Data Cleaning as a Process
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.
Data Mining, KDD Process, Data mining functionalities, Characterization,
Discrimination ,
Association,
Classification,
Prediction,
Clustering,
Outlier analysis, Data Cleaning as a Process
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
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.
The seminar is about Data warehousing, in here we are gonna discuss about what is data warehousing, comparison b/w database and data warehouse, different data warehouse models.about Data mart, and disadvantages of data warehousing.
This presentation gives the idea about Data Preprocessing in the field of Data Mining. Images, examples and other things are adopted from "Data Mining Concepts and Techniques by Jiawei Han, Micheline Kamber and Jian Pei "
It is an introduction to Data Analytics, its applications in different domains, the stages of Analytics project and the different phases of Data Analytics life cycle.
I deeply acknowledge the sources from which I could consolidate the material.
This presentation will help you understand the basic building blocks of Business Intelligence. Learn how decisions are triggered, the complete decision process and who makes decisions in the corporate world.
More importantly, understand core components of a Business Intelligence architecture such as a data warehouse, data mining, OLAP (Online analytical procession) , OLTP (Online Transaction Processing) and data reporting. Each component plays an integral part which enables today's managers and decision makers collect, analyze and interpret data to make it actionable for decision making.
Business intelligence has become an integral part that needs to be incorporated to ensure business survival. It is a tool that helps analyze historical data and forecast future so that your are always one step ahead in your business.
Please feel free to like, share and comment as you please!
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
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.
The seminar is about Data warehousing, in here we are gonna discuss about what is data warehousing, comparison b/w database and data warehouse, different data warehouse models.about Data mart, and disadvantages of data warehousing.
This presentation gives the idea about Data Preprocessing in the field of Data Mining. Images, examples and other things are adopted from "Data Mining Concepts and Techniques by Jiawei Han, Micheline Kamber and Jian Pei "
It is an introduction to Data Analytics, its applications in different domains, the stages of Analytics project and the different phases of Data Analytics life cycle.
I deeply acknowledge the sources from which I could consolidate the material.
This presentation will help you understand the basic building blocks of Business Intelligence. Learn how decisions are triggered, the complete decision process and who makes decisions in the corporate world.
More importantly, understand core components of a Business Intelligence architecture such as a data warehouse, data mining, OLAP (Online analytical procession) , OLTP (Online Transaction Processing) and data reporting. Each component plays an integral part which enables today's managers and decision makers collect, analyze and interpret data to make it actionable for decision making.
Business intelligence has become an integral part that needs to be incorporated to ensure business survival. It is a tool that helps analyze historical data and forecast future so that your are always one step ahead in your business.
Please feel free to like, share and comment as you please!
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.
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.
Analytics thought-leader Thomas Davenport and leading industry experts discuss how—and why—organizations like yours use business analytics to empower more timely and precise decisions by bringing new insights into daily operations.
This presentation slide introduces Data Science to Maketing Professionals. This intent to explain how to think like a data scientist in term of marketing concept which focus on consumer behaviors and new set of big data (web, social, location.. etc). The reference books are at the end of the slides.
What is Data Science, applications of machine learning, how to start a career in this exciting field. As presented at a session for Delaware Tech Meetup.
Operationalizing Customer Analytics with Azure and Power BICCG
Many organizations fail to realize the value of data science teams because they are not effectively translating the analytic findings produced by these teams into quantifiable business results. This webinar demonstrates how to visualize analytic models like churn and turn their output into action. Senior Business Solution Architect, Mike Druta, presents methods for operationalizing analytic models produced by data science teams into a repeatable process that can be automated and applied continuously using Azure.
Expert data analytics prove to be highly transformative when applied in context to corporate business strategies.
This webinar covers various approaches and strategies that will give you a detailed insight into planning and executing your Data Analytics projects.
Smarter Analytics: Supporting the Enterprise with AutomationInside Analysis
The Briefing Room with Barry Devlin and WhereScape
Live Webcast on June 10, 2014
Watch the archive:
https://bloorgroup.webex.com/bloorgroup/lsr.php?RCID=5230c31ab287778c73b56002bc2c51a
The data warehouse is intended to support analysis by making the right data available to the right people in a timely fashion. But conditions change all the time, and when data doesn’t keep up with the business, analysts quickly turn to workarounds. This leads to ungoverned and largely un-managed side projects, which trade short-term wins for long-term trouble. One way to keep everyone happy is by creating an integrated environment that pulls data from all sources, and is capable of automating both the model development and delivery of analyst-ready data.
Register for this episode of The Briefing Room to hear data warehousing pioneer and Analyst Barry Devlin as he explains the critical components of a successful data warehouse environment, and how traditional approaches must be augmented to keep up with the times. He’ll be briefed by WhereScape CEO Michael Whitehead, who will showcase his company’s data warehousing automation solutions. He’ll discuss how a fast, well-managed and automated infrastructure is the key to empowering faster, smarter, repeatable decision making.
Visit InsideAnlaysis.com for more information.
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.
Explainability for Natural Language ProcessingYunyao Li
Final deck for our popular tutorial on "Explainability for Natural Language Processing" at KDD'2021. See links below for downloadable version (with higher resolution) and recording of the live tutorial.
Title: Explainability for Natural Language Processing
Presenter: Marina Danilevsky, Shipi Dhanorkar, Yunyao Li and Lucian Popa and Kun Qian and Anbang Xu
Website: http://xainlp.github.io/
Recording: https://www.youtube.com/watch?v=PvKOSYGclPk&t=2s
Downloadable version with higher resolution: https://drive.google.com/file/d/1_gt_cS9nP9rcZOn4dcmxc2CErxrHW9CU/view?usp=sharing
@article{kdd2021xaitutorial,
title={Explainability for Natural Language Processing},
author= {Marina Danilevsky, Shipi Dhanorkar and Yunyao Li and Lucian Popa and Kun Qian and Anbang Xu},
journal={KDD},
year={2021}
}
Abstract:
This lecture-style tutorial, which mixes in an interactive literature browsing component, is intended for the many researchers and practitioners working with text data and on applications of natural language processing (NLP) in data science and knowledge discovery. The focus of the tutorial is on the issues of transparency and interpretability as they relate to building models for text and their applications to knowledge discovery. As black-box models have gained popularity for a broad range of tasks in recent years, both the research and industry communities have begun developing new techniques to render them more transparent and interpretable.Reporting from an interdisciplinary team of social science, human-computer interaction (HCI), and NLP/knowledge management researchers, our tutorial has two components: an introduction to explainable AI (XAI) in the NLP domain and a review of the state-of-the-art research; and findings from a qualitative interview study of individuals working on real-world NLP projects as they are applied to various knowledge extraction and discovery at a large, multinational technology and consulting corporation. The first component will introduce core concepts related to explainability inNLP. Then, we will discuss explainability for NLP tasks and reporton a systematic literature review of the state-of-the-art literaturein AI, NLP and HCI conferences. The second component reports on our qualitative interview study, which identifies practical challenges and concerns that arise in real-world development projects that require the modeling and understanding of text data.
This talk is an introduction to Data Science. It explains Data Science from two perspectives - as a profession and as a descipline. While covering the benefits of Data Science for business, It explaints how to get started for embracing data science in business.
Big Data is the lastest cashcow. Data Analytics has now a crucial role for industries. This article describes as to what is Big Data and Analytics and how a Chartered Accountant will be able to provide value in this field.
Similar to Introduction to data mining technique (20)
Venture capital in India is a big action by the Indian government in the term of industry development. Venture capital having more problem and also denoted what will be scenario of Venture capital in future !!
This PPT provide a huge information about services support process. In this PPT I described all steps of services support process from the beginning to end.
Online Payment a highly used medium of making transaction. In this PPT very briefly described about how online payment medium works and what steps follows in online payment method.
This PPT teach and provide a information how write a latter in corporate world and inform your officers like business deal, memorandum of meting and Etc.
A very big trend of newly marketing era of Digital Marketing. Facebook marketing will help you for making advertise your product on Facebook for business Leads and awareness.
A Introduction and brief knowledge about big data and its types and uses in corporate industry. This PPT will provide a basic information about Big data specification and uses.
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
StarCompliance is a leading firm specializing in the recovery of stolen cryptocurrency. Our comprehensive services are designed to assist individuals and organizations in navigating the complex process of fraud reporting, investigation, and fund recovery. We combine cutting-edge technology with expert legal support to provide a robust solution for victims of crypto theft.
Our Services Include:
Reporting to Tracking Authorities:
We immediately notify all relevant centralized exchanges (CEX), decentralized exchanges (DEX), and wallet providers about the stolen cryptocurrency. This ensures that the stolen assets are flagged as scam transactions, making it impossible for the thief to use them.
Assistance with Filing Police Reports:
We guide you through the process of filing a valid police report. Our support team provides detailed instructions on which police department to contact and helps you complete the necessary paperwork within the critical 72-hour window.
Launching the Refund Process:
Our team of experienced lawyers can initiate lawsuits on your behalf and represent you in various jurisdictions around the world. They work diligently to recover your stolen funds and ensure that justice is served.
At StarCompliance, we understand the urgency and stress involved in dealing with cryptocurrency theft. Our dedicated team works quickly and efficiently to provide you with the support and expertise needed to recover your assets. Trust us to be your partner in navigating the complexities of the crypto world and safeguarding your investments.
2. WHAT IS DATA MINING?
Data mining is also called knowledge discovery and data
mining (KDD)
Data mining is
extraction of useful patterns from data sources, e.g.,
databases, texts, web, image.
Patterns must be:
valid, novel, potentially useful, understandable
This PPT presented By - Pawneshwar Datt Rai
3. EXAMPLE OF DISCOVERED PATTERNS
Association rules:
“80% of customers who buy cheese and milk also buy
bread, and 5% of customers buy all of them together”
Cheese, Milk Bread [sup =5%, confid=80%]
This PPT presented By - Pawneshwar Datt Rai
4. MAIN DATA MINING TASKS
Classification:
mining patterns that can classify future data into known
classes.
Association rule mining
mining any rule of the form X Y, where X and Y are
sets of data items.
Clustering
identifying a set of similarity groups in the data
This PPT presented By - Pawneshwar Datt Rai
5. MAIN DATA MINING TASKS
Sequential pattern mining:
A sequential rule: A B, says that event A will be
immediately followed by event B with a certain confidence
Deviation detection:
discovering the most significant changes in data
Data visualization: using graphical methods to show
patterns in data.
This PPT presented By - Pawneshwar Datt Rai
6. WHY IS DATA MINING IMPORTANT?
Rapid computerization of businesses produce huge
amount of data
How to make best use of data?
A growing realization: knowledge discovered from
data can be used for competitive advantage.
This PPT presented By - Pawneshwar Datt Rai
7. WHY IS DATA MINING NECESSARY?
Make use of your data assets
There is a big gap from stored data to knowledge; and
the transition won’t occur automatically.
Many interesting things you want to find cannot be found
using database queries
“find me people likely to buy my products”
“Who are likely to respond to my promotion”
This PPT presented By - Pawneshwar Datt Rai
8. WHY DATA MINING NOW?
The data is abundant.
The data is being warehoused.
The computing power is affordable.
The competitive pressure is strong.
Data mining tools have become available
This PPT presented By - Pawneshwar Datt Rai
9. RELATED FIELDS
Data mining is an emerging multi-disciplinary field:
Statistics
Machine learning
Databases
Information retrieval
Visualization
etc.
This PPT presented By - Pawneshwar Datt Rai
10. DATA MINING (KDD) PROCESS
Understand the application domain
Identify data sources and select target data
Pre-process: cleaning, attribute selection
Data mining to extract patterns or models
Post-process: identifying interesting or useful patterns
Incorporate patterns in real world tasks
This PPT presented By - Pawneshwar Datt Rai
11. DATA MINING APPLICATIONS
Marketing, customer profiling and retention,
identifying potential customers, market
segmentation.
Fraud detection
identifying credit card fraud, intrusion detection
Scientific data analysis
Text and web mining
Any application that involves a large amount of data.
This PPT presented By - Pawneshwar Datt Rai
13. OPINION ANALYSIS
Word-of-mouth on the Web
The Web has dramatically changed the way that
consumers express their opinions.
One can post reviews of products at merchant
sites, Web forums, discussion groups, blogs
Techniques are being developed to exploit these
sources.
Benefits of Review Analysis
Potential Customer: No need to read many reviews
Product manufacturer: market intelligence, product
benchmarking
This PPT presented By - Pawneshwar Datt Rai
14. FEATURE BASED ANALYSIS &
SUMMARIZATION
Extracting product features (called Opinion Features) that
have been commented on by customers.
Identifying opinion sentences in each review and
deciding whether each opinion sentence is positive or
negative.
Summarizing and comparing results.
This PPT presented By - Pawneshwar Datt Rai
15. A Happy and Prosperous day to all friends.
This PPT presented By – Pawneshwar Datt Rai
ThisPPTpresentedBy-PawneshwarDattRai