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Visualizing Data Mining: Empowering Presentations with Templates (Free PPT & PDF)
Deepali Khatri
Hey, data enthusiasts!
Welcome to our blog where we're diving headfirst into the fascinating world of data mining.
If you've ever wondered how businesses uncover hidden treasures buried within their vast amounts of data, you're in for a treat. It is like being a detective, but instead of solving crimes, you're uncovering valuable insights and patterns lurking in the depths of information.
In this blog, we'll explore the ins and outs of data mining, from its importance and techniques to the tools and software that make it all possible.
So, grab your virtual shovels and get ready to dig deep into the captivating realm of data mining!
Data Mining
Data mining refers to the process of extracting valuable insights, patterns, and knowledge from large sets of data. It involves using various techniques and algorithms to explore and analyze data, aiming to uncover hidden patterns, correlations, and trends that are not readily apparent.
From the process to the modern challenges we face and their solutions, this blog covers slides that explains everything. We'll explore different techniques, so you can understand how to uncover hidden patterns and gain valuable insights from your data. Whether you're a data enthusiast, or a business professional, these slides will equip you with the knowledge to tackle data mining head-on.
Let's get started!
Cover Slide
This cover slide sets the stage for a comprehensive exploration of this powerful analytical technique. The slide features a visually engaging design that captures the essence of data mining. It may include elements such as a striking image representing data analysis or a concept related to mining. The title on the cover slide succinctly conveys the focus of the presentation, creating anticipation for what is to come. With its visually appealing design and clear messaging, the cover slide grabs the attention of the audience, creating an engaging and informative introduction to the world of data mining.
Download this PowerPoint Template Now
This slide sets the stage for a comprehensive exploration of the data mining process. The amazing slide visually depicts the essential steps involved in this analytical journey. It showcases the key phases of data mining, starting with the data source, followed by pre-processing, exploration, and transformation. It further highlights the critical stages of pattern recognition, evaluation, and interpretation. By presenting these phases, the slide emphasizes how businesses can leverage data mining techniques to uncover valuable patterns and insights within large data sets. This serves as an engaging introduction, capturing the audience's attention and setting the foundation for an informative and insightful presentation.
Modern Data Mining Challenges and Solutions
Our challenges and solution slide tackles the key obstacles encountered in contemporary data mining and provides potential remedies to assist businesses in overcoming these hurdles and making informed decisions. The slide addresses critical issues such as handling heterogeneous data, dealing with scattered data sources, and ensuring data privacy. It emphasizes the importance of leveraging advanced techniques and tools to integrate diverse data types, centralize scattered data, and implement robust privacy measures. By presenting these challenges alongside effective solutions, the slide equips organizations with the knowledge and strategies needed to navigate the complexities, optimize data-driven insights, and drive business success.
Data Mining Techniques to Optimize Business
This techniques slide focuses on essential techniques that empower businesses to harness the potential of data. It highlights how these techniques play a vital role in building data-centric organizations by providing valuable insights and guiding companies in making informed decisions. The slide covers key techniques such as tracking patterns in data, enabling businesses to identify trends and make predictive analyses. It also highlights the importance of clustering, which helps categorize data points into meaningful groups, and regression analysis, which facilitates understanding and forecasting relationships between variables. By employing these techniques, businesses can unlock hidden opportunities, enhance efficiency, and optimize their operations.
Download this PowerPoint Template Now
Solution-Oriented Data Mining Application
This editable slide focuses on the diverse use cases of data mining across various industries. This slide highlights the practical application of data mining techniques in solving business challenges. It covers key aspects such as customer relationship management, fraud and anomaly detection, and customer segmentation. By showcasing real-world examples and highlighting its purpose in each industry, this slide emphasizes the value and potential of leveraging data-driven insights for decision-making. Whether it's improving customer satisfaction, mitigating risks, or optimizing marketing strategies, data mining plays a crucial role in driving success and achieving business goals across different sectors.
Business Optimizing Data Mining Tools and Software
This slide in the PowerPoint presentation offers a comprehensive comparison of various tools, including both open-source and commercial solutions. This slide provides valuable insights into various software options available to businesses for discovering hidden relationships within their data. It highlights well-known tools such as SAS, Zoho Analytics, and Teradata. By presenting a side-by-side comparison, businesses can evaluate the features, functionalities, and benefits of each tool to make an informed decision. This slide serves as a valuable resource for organizations seeking to optimize their data mining efforts and leverage the power of sophisticated tools to gain valuable insights and drive business success.
Data mining is a powerful technique that enables businesses to extract valuable insights and make informed decisions based on their data. This blog has explored its significance and potential to uncover hidden patterns, relationships, and trends within large datasets.
Additionally, it has provided a valuable resource by offering editable PowerPoint slides specifically designed for its presentations. These slides serve as a convenient tool for professionals to showcase the concepts, methodologies, and its benefit to their audience. By utilizing these editable slides, organizations can effectively communicate the importance of data mining and leverage its potential to drive innovation, enhance decision-making, and achieve business success in today's data-driven world.
Download our professionally customizable and editable PowerPoint templates now!
Get access to Free PPT and Free PDF now !
Frequently asked questions.
1. What is data mining? It is the process of extracting valuable insights, patterns, and knowledge from large sets of data. It involves using various techniques and algorithms to discover hidden patterns, correlations, and trends that can help businesses make informed decisions and predictions.
2. Why is data mining important? It plays a crucial role in today's data-driven world. It allows businesses to uncover valuable information from vast amounts of data, which can be used to improve decision-making, identify market trends, enhance customer experiences, detect fraud, optimize processes, and gain a competitive edge.
3. What are some common data mining techniques? There are several popular techniques, including association analysis, classification, clustering, regression analysis, and anomaly detection. Association analysis helps identify relationships and patterns among variables, while classification predicts outcomes based on past data. Clustering groups similar data points together, regression analysis predicts numerical values, and anomaly detection identifies unusual patterns or outliers in the data.
4. What challenges are associated with data mining? The challenges such as data quality issues, handling large and complex datasets, selecting appropriate algorithms for analysis, ensuring privacy and security of data, and interpreting the results accurately can be presented . It requires skilled professionals and robust infrastructure to overcome these challenges effectively.
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Chapter 4: Data Mining for Business Intelligence
Published by Lesley Henderson Modified over 8 years ago
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Presentation on theme: "Chapter 4: Data Mining for Business Intelligence"— Presentation transcript:
DATA, TEXT, AND WEB MINING
Week 9 Data Mining System (Knowledge Data Discovery)
Data Mining Knowledge Discovery in Databases Data 31.
Data Mining By Archana Ketkar.
Business Intelligence: A Managerial Perspective on Analytics (3rd Edition) Chapter 4: Data Mining.
Data Mining – Intro.
Chapter 5 Data mining : A Closer Look.
Data Mining & Data Warehousing PresentedBy: Group 4 Kirk Bishop Joe Draskovich Amber Hottenroth Brandon Lee Stephen Pesavento.
TURKISH STATISTICAL INSTITUTE INFORMATION TECHNOLOGIES DEPARTMENT (Muscat, Oman) DATA MINING.
Chapter 5: Data Mining for Business Intelligence
Chapter 4 Data, Text, and Web Mining
Data Mining By Andrie Suherman. Agenda Introduction Major Elements Steps/ Processes Tools used for data mining Advantages and Disadvantages.
Decision Support and Business Intelligence Systems (9 th Ed., Prentice Hall) Chapter 5: Data Mining for Business Intelligence.
OLAM and Data Mining: Concepts and Techniques. Introduction Data explosion problem: –Automated data collection tools and mature database technology lead.
Data Mining Week 10.
Knowledge Discovery & Data Mining process of extracting previously unknown, valid, and actionable (understandable) information from large databases Data.
Data Mining Techniques
About project
© 2024 SlidePlayer.com Inc. All rights reserved.
Jiawei Han, Micheline Kamber and Jian Pei
Data Mining: Concepts and Techniques, 3 rd ed .
The Morgan Kaufmann Series in Data Management Systems Morgan Kaufmann Publishers, July 2011. ISBN 978-0123814791
Slides in PowerPoint
Chapter 1. Introduction
Chapter 2. Know Your Data
Chapter 3. Data Preprocessing
Chapter 4. Data Warehousing and On-Line Analytical Processing
Chapter 5. Data Cube Technology
Chapter 6. Mining Frequent Patterns, Associations and Correlations: Basic Concepts and Methods
Chapter 7. Advanced Frequent Pattern Mining
Chapter 8. Classification: Basic Concepts
Chapter 9. Classification: Advanced Methods
Chapter 10. Cluster Analysis: Basic Concepts and Methods
Chapter 11. Cluster Analysis: Advanced Methods
Chapter 12. Outlier Detection
Chapter 13. Trends and Research Frontiers in Data Mining
Updated Slides for CS, UIUC Teaching in PowerPoint form
(Note: This set of slides corresponds to the current teaching of the data mining course at CS, UIUC. In general, it takes new technical materials from recent research papers but shrinks some materials of the textbook. It has also re-arranged the order of presentation for some technical materials.)
Instructions on finding the new sets of slides are as follows:
1. Go to the homepage of the first author, Prof. Jiawei Han: http://web.engr.illinois.edu/~hanj/
2. Click the following links in the section of Teaching:
a . UIUC CS412: An Introduction to Data Warehousing and Data Mining
b . UIUC CS512: Data Mining: Principles and Algorithms
3. Download the slides of the corresponding chapters you are interested in
Back to Data Mining: Concepts and Techniques, 3 rd ed .
Back to jiawei han , data and information systems research laboratory , computer science, university of illinois at urbana-champaign.
Free Data Mining PowerPoint Template
Data Mining PowerPoint Template is a simple grey template with stain spots in the footer of the slide design and very useful for data mining projects or presentations for data mining. This free data mining PowerPoint template can be used for example in presentations where you need to explain data mining algorithms in PowerPoint presentations .
The effect in the footer of the master slide combines some circles with different colors that you can use for example to represent a scatter plot chart or data in a cluster. For example using k-means or other cluster algorithms for data mining you can enhance this free PowerPoint presentation to put your own data charts in the slide design and also apply Excel operations for example for data manipulation or data extraction. If you are really interesting on data mining templates and data mining process PowerPoints then you can download this free cluster analysis PowerPoint template or free clustering PPT template .
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Educational Data Mining Overview
Mar 17, 2012
800 likes | 1.94k Views
Educational Data Mining Overview. Ryan S.J.d . Baker PSLC Summer School 2010. Welcome to the EDM track!. Educational Data Mining.
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Presentation Transcript
Educational Data Mining Overview Ryan S.J.d. Baker PSLC Summer School 2010
Welcome to the EDM track!
Educational Data Mining • “Educational Data Mining is an emerging discipline, concerned with developing methods for exploring the unique types of data that come from educational settings, and using those methods to better understand students, and the settings which they learn in.” • www.educationaldatamining.org
Classes of EDM Method(Baker & Yacef, 2009) • Prediction • Clustering • Relationship Mining • Discovery with Models • Distillation of Data For Human Judgment
Prediction • Develop a model which can infer a single aspect of the data (predicted variable) from some combination of other aspects of the data (predictor variables) • Which students are off-task? • Which students will fail the class?
Clustering • Find points that naturally group together, splitting full data set into set of clusters • Usually used when nothing is known about the structure of the data • What behaviors are prominent in domain? • What are the main groups of students? • Related to Principal Component Analysis • Geoff Gordon’s talk tomorrow
Relationship Mining • Discover relationships between variables in a data set with many variables • Association rule mining • Correlation mining • Sequential pattern mining • Causal data mining
Discovery with Models • Pre-existing model (developed with EDM prediction methods… or clustering… or knowledge engineering) • Applied to data and used as a component in another analysis
Distillation of Data for Human Judgment • Making complex data understandable by humans to leverage their judgment • Text replays are a simple example of this
A related method
Knowledge Engineering • Creating a model by hand rather than automatically fitting model • In one comparison, leads to worse fit to gold-standard labels of construct of interest than data mining (Roll et al, 2005), but similar qualitative performance
EDM track schedule • Tuesday 10am • Educational Data Mining with DataShop (Stamper, Koedinger) • Tuesday 11am • Item Response Theory and Learning Factor Analysis (Koedinger) • Tuesday 2:15pm • Principal Component Analysis, Additive Factor Model (Gordon) • Tuesday 3:15pm (optional) • Hands-on Activity: Data Annotation for Classification (Baker) • Hands-on Activity: Learning Curves and Logistic Regression in R (Koedinger)
EDM track schedule • Wednesday 11am • Bayesian Knowledge Tracing; Prediction Models (Baker) • Wednesday 11:45am (optional) • Hands-on activity: Prediction modeling (Baker) • Wednesday 3:15pm • Machine Learning and SimStudent (Matsuda)
Comments? Questions?
PSLC DataShop • Many large-scale datasets • Tools for • exploratory data analysis • learning curves • domain model testing • Detail tomorrow morning
Microsoft Excel • Excellent tool for exploratory data analysis, and for setting up simple models
Pivot Tables
Pivot Tables • Who has used pivot tables before?
Pivot Tables • What do they allow you to do?
Pivot Tables • Facilitate aggregating data for comparison or use in further analyses
Equation Solver • Allows you to fit mathematical models in Excel • Let’s go through a simple example together
Equation Solver: Example • Let’s predict correctness from pknow, using a linear regression model • Using WEKA-CTA1Z04-examples.xlsx • You have this data set on your flashdrive • It’s from the DataShop – Hampton Algebra 2005-2006 • I have formatted it for this example
Under pred type • =O2*$W$3+$W$2 • And copy it down
Under pred type • =O2*$W$3+$W$2 • And copy it down • Does anyone know why we use the $?
Under SR type • =(G2-S2)^2 • This finds the difference between the prediction (0 right now) and the correctness value (0 or 1) • Squaring it is a way to both get the absolute value, and magnify larger differences; very common in statistics
To the right of const type • 0
To the right of weight type • 1 • Note that you now have a model that is identical to pknow
To the right of SSR type • =SUM(T2:T2888) • This is the sum of squared residuals, again a very common way of evaluating models
To the right of r type • =CORREL(S2:S2888,G2:G2888) • This is the correlation between the model and the variable being predicted
Now go into the Excel Equation Solver • And set up this model,and press solve
What changed?
What stayed the same?
We just built… • A very simple regression model • A much simpler model than what you can build in other packages
Why is this useful? • You can specify much more complex mathematical models than this • And much more quickly than you can implement them in software • For example, Excel is usually where I test variants on Bayesian Knowledge Tracing before implementing them in Java
Suite of visualizations • Scatterplots (with or without lines) • Bar graphs
Weka and RapidMiner • Data mining packages • Weka is the most popular, but personally I prefer RapidMiner
Weka .vs. RapidMiner • Weka easier to use than RapidMiner • RapidMiner significantly more powerful and flexible (from GUI, both are powerful and flexible if accessed via API)
In particular… • It is impossible to do key types of model validation for EDM within Weka’s GUI • RapidMiner can be kludged into doing so(more on this in hands-on session Wed) • No tool really tailored to the needs of EDM researchers at current time…
SPSS • SPSS is a statistical package, and therefore can do a wide variety of statistical tests • It can also do some forms of data mining, like factor analysis (a relative of clustering)
SPSS • The difference between statistical packages (like SPSS) and data mining packages (like RapidMiner and Weka) is: • Statistics packages are focused on finding models and relationships that are statistically significant (e.g. the data would be seen less than 5% of the time if the model were not true) • Data mining packages set a lower bar – are the models accurate and generalizable?
R • R is an open-source competitor to SPSS • More powerful and flexible than SPSS • But much harder to use – I find it easy to accidentally do very, very incorrect things in R • Ken will demo R in a hands-on session
Matlab • A powerful tool for building complex mathematical models • Beck and Chang’s Bayes Net Toolkit – Student Modeling is built in Matlab • Geoff Gordon will give a hands-on demo of Matlab
Pre-processing • Tomorrow morning, John and Ken will talk about some of the great data available in DataShop
Wherever you get your data from • You’ll need to process it into a form that software can easily analyze, and which builds successful models
Common approach • Flat data file • Even if you store your data in databases, most data mining techniques require a flat data file • Like the one we looked at in Excel
Some useful features to distill for educational software • Type of interface widget • “Pknow”: The probability that the student knew the skill before answering (using Bayesian Knowledge-Tracingor PFA or your favorite approach) • Assessment of progress student is making towards correct answer (how many fewer constraints violated) • Whether this action is the first time a student attempts a given problem step • “Optoprac”: How many problem steps involving this skill that the student has encountered
Some useful features to distill for educational software • “timeSD”: time taken in terms of standard deviations above (+) or below (-) average for this skill across all actions and students • “time3SD”: sum of timeSD for the last 3 actions (or 5, or 4, etc. etc.) • Action type counts or percents • Total number of action so far • Total number of action on this skill, divided by optoprac • Number of action in last N actions • Could be assessment of action (wrong, right), or type of action (help request, making hypothesis, plotting point)
- More by User
Special Topics in Educational Data Mining
Special Topics in Educational Data Mining. HUDK5199 Spring term, 2013 January 23, 2013. Wow. Welcome! There’s a lot of you It’s great to see so much interest in EDM. Administrative Stuff. Is everyone signed up for class?
723 views • 58 slides
Educational data mining overview & Introduction to Exploratory Data Analysis with DataShop
Educational data mining overview & Introduction to Exploratory Data Analysis with DataShop. Ken Koedinger CMU Director of PSLC Professor of Human-Computer Interaction & Psychology Carnegie Mellon University. Overview. DataShop Overview Logging model DataShop Features
635 views • 45 slides
Educational Data Mining Overview. Ryan S.J.d . Baker PSLC Summer School 2012. Welcome to the EDM track!. On behalf of the track lead, John Stamper, and all of our colleagues. Educational Data Mining.
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Educational Data Mining Overview. John Stamper PSLC Summer School 2011. Welcome to the EDM track!. Educational Data Mining.
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Educational Data Mining
Educational Data Mining. March 3, 2010. Today’s Class. EDM Assignmen t#5 Mega-Survey. Educational Data Mining.
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Special Topics in Educational Data Mining. HUDK5199 Spring term, 2013 March 4, 2013. Today’s Class. Reinforcement Learning and Partially Observable Markov Decision Processes
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Data Mining Course Overview
Data Mining Course Overview. About the course – Administrivia. Instructor: George Kollios, [email protected] MCS 288, Mon 2:30-4:00PM and Tue 10:25-11:55AM Home Page: http://www.cs.bu.edu/fac/gkollios/dm07 Check frequently! Syllabus, schedule, assignments, announcements…. Grading.
484 views • 38 slides
Educational Data Mining and DataShop
Educational Data Mining and DataShop. John Stamper Carnegie Mellon University. The Classroom of the Future. Which picture represents the “Classroom of the Future”?. 9/12/2012. The Classroom of the Future. The answer is both! Depends of how much money you have...
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Special Topics in Educational Data Mining. HUDK5199 Spring term, 2013 February 25, 2013. Today’s Class. Feature Engineering and Distillation - What. Special Rules for Today. Everyone Votes Everyone Participates. Feature Engineering.
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Overview of Data Mining
Overview of Data Mining. Mehedy Masud Lecture slides modified from: Jiawei Han ( http://www-sal.cs.uiuc.edu/~hanj/DM_Book.html ) Vipin Kumar ( http://www-users.cs.umn.edu/~kumar/csci5980/index.html ) Ad Feelders ( http://www.cs.uu.nl/docs/vakken/adm/ )
891 views • 75 slides
Overview of Data Mining. Lecture slides modified from: Jiawei Han ( http://www-sal.cs.uiuc.edu/~hanj/DM_Book.html ) Vipin Kumar ( http://www-users.cs.umn.edu/~kumar/csci5980/index.html ) Ad Feelders ( http://www.cs.uu.nl/docs/vakken/adm/ )
905 views • 75 slides
Educational data mining overview & Introduction to Exploratory Data Analysis
Educational data mining overview & Introduction to Exploratory Data Analysis. Ken Koedinger CMU Director of PSLC Professor of Human-Computer Interaction & Psychology Carnegie Mellon University. Plan. Because it is technical, will start with learning curve formulas …
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Educational Data Mining Success Stories
Educational Data Mining Success Stories. Jack Mostow Project LISTEN ( www.cs.cmu.edu/~listen ) “Home run”: demonstrable increase in learning “Base hit”: likely to improve learning by informing: Educational researchers Teachers Students Tutor developers Automated tutors
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IERI educational data mining panel
IERI educational data mining panel. Joseph E. Beck Project LISTEN Center for Automated Learning and Discovery Carnegie Mellon University Funding: National Science Foundation. Discussion question.
237 views • 23 slides
Data mining is the process of discovering patterns in large data sets involving methods at the intersection of machine learning, statistics, and database systems. 1 Data mining is an interdisciplinary sub field of computer science and statistics with an overall goal to extract from a data set and transform the information into a comprehensible structure for further use. 1 2 3 4 The process of digging through data to discover hidden connections and predict future trends has a long history. Sometimes referred to as 'knowledge discovery' in databases, the term data mining wasn't coined until the 1990s. What was old is new again, as data mining technology keeps evolving to keep pace with the limitless potential of big data and affordable computing power. Over the last decade, advances in processing power and speed have enabled us to move beyond manual, tedious and time consuming practices to quick, easy and automated data analysis. The more complex the data sets collected, the more potential there is to uncover relevant insights. Rupashi Koul "Overview of Data Mining" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-4 , June 2020, URL: https://www.ijtsrd.com/papers/ijtsrd31368.pdf Paper Url :https://www.ijtsrd.com/engineering/computer-engineering/31368/overview-of-data-mining/rupashi-koul
47 views • 4 slides
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Descriptive vs. predictive data mining • Multiple/integrated functions and mining at multiple levels • Techniques utilized • Data-intensive, data warehouse (OLAP), machine learning, statistics, pattern recognition, visualization, high- performance, etc. • Applications adapted • Retail, telecommunication, banking, fraud analysis, bio ...
Slide 1: This slide introduces Data Mining.State Your Company Name and begin. Slide 2: This is an Agenda slide.State your agendas here. Slide 3: This slide shows Table of Content for the presentation. Slide 4: This is another slide continuing Table of Content for the presentation. Slide 5: This slide highlights title for topics that are to be covered next in the template.
Summary 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 Mining can be performed in a ...
Template 1: Data Mining PowerPoint Presentation Slides. Our content-ready Data Mining Presentation Template is the perfect way to convey your message about data mining. This presentation template includes well-designed graphics, informative slides, and an organized layout to help you explain the process and the statistics.
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TEXT MINING PROCESS A set of linguistic, statistical, and machine learning techniques that model and structure the information content of textual sources for business intelligence, exploratory data analysis, research, or investigation Data Mining and Machine Learning in a nutshell An Introduction to Data Mining 80. 75.
the slides contain: Pattern Mining: A Road Map Pattern Mining in Multi-Level, Multi-Dimensional Space Constraint-Based Frequent Pattern Mining Mining High-Dimensional Data and Colossal Patterns Mining Compressed or Approximate Patterns Sequential Pattern Mining Graph Pattern Mining by Jiawei Han, Micheline Kamber, and Jian Pei, University of Illinois at Urbana-Champaign & Simon Fraser ...
CrystalGraphics creates templates designed to make even average presentations look incredible. Below you'll see thumbnail sized previews of the title slides of a few of our 149 best data mining templates for PowerPoint and Google Slides. The text you'll see in in those slides is just example text.
Premium Google Slides theme, PowerPoint template, and Canva presentation template. This template for a data mining project proposal is what you need to make your presentation excel visually as well as in its content. All of its graphic elements are related to the subject of data mining. Photos of people using computers, icons depicting data ...
Data mining (knowledge discovery from data) Extraction of interesting (non-trivial, implicit, previously unknown and potentially useful) patterns or knowledge from huge amount of data. 3 What is Data Mining By definition is the process of extracting previously unknown data from large databases and using it to make orgnisational decisions.
1.9 Major Issues in Data Mining • Performance Issues • Efficiency and scalability • Huge amount of data • Running time must be predictable and acceptable • Parallel, distributed and incremental mining algorithms • Divide the data into partitions and processed in parallel • Incorporate database updates without having to mine the ...
Data Mining: Concepts and Techniques, 4th ed. Morgan Kaufmann Publishers 2023. ISBN 978--12-811760-6. Slides in PowerPoint. Chapter 1: Introduction. Chapter 2: Data, measurements, and data preprocessing. Chapter 3: Data warehousing and online analytical processing. Chapter 4: Pattern mining: basic concepts and methods. Chapter 5: Pattern ...
Data mining refers to the process of extracting valuable insights, patterns, and knowledge from large sets of data. ... This slide in the PowerPoint presentation offers a comprehensive comparison of various tools, including both open-source and commercial solutions. This slide provides valuable insights into various software options available ...
DM environment is usually a client-server or a Web-based information systems architecture. Data is the most critical ingredient for DM which may include soft/unstructured data. The miner is often an end user. Striking it rich requires creative thinking. Data mining tools' capabilities and ease of use are essential (Web, Parallel processing ...
Trends and Research Frontiers in Data Mining . Updated Slides for CS, UIUC Teaching in PowerPoint form (Note: This set of slides corresponds to the current teaching of the data mining course at CS, UIUC. In general, it takes new technical materials from recent research papers but shrinks some materials of the textbook.
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PPT circuitous flow data mining process powerpoint presentation Templates-Use this data flow diagram to show the logical flow of data through a set of processes or procedures. It represents processing requirements of a program and the information flows. It helps to focus the minds of your team-PPT circuitous flow data mining process powerpoint ...
Data mining & data warehousing (ppt) Harish Chand. Introduction to Machine Learning. Introduction to Machine Learning. Lior Rokach. 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.
If you are really interesting on data mining templates and data mining process PowerPoints then you can download this free cluster analysis PowerPoint template or free clustering PPT template. PPT Size: 304.8 KiB | Downloads: 24,170. Free Data Mining PowerPoint Template is saved under Business / Finance templates and use the following tags:
Mining Association Rules—An Example Min. support 50% Min. confidence 50% For rule A C: support = support ( {A C}) = 50% confidence = support ( {A C})/support ( {A}) = 66.6% The Apriori principle: Any subset of a frequent itemset must be frequent. Mining Frequent Itemsets: the Key Step • Find the frequent itemsets: the sets of items that ...
Variables of Mixed Types • A database may contain all the six types of variables • symmetric binary, asymmetric binary, nominal, ordinal, interval and ratio. • One may use a weighted formula to combine their effects. • f is binary or nominal: dij (f) = 0 if xif = xjf , or dij (f) = 1 o.w. • f is interval-based: use the normalized ...
Educational Data Mining Overview Ryan S.J.d. Baker PSLC Summer School 2010. Welcome to the EDM track! Educational Data Mining • "Educational Data Mining is an emerging discipline, concerned with developing methods for exploring the unique types of data that come from educational settings, and using those methods to better understand ...
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