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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.

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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!

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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.

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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.

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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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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.

643 views • 51 slides

Educational Data Mining Overview. John Stamper PSLC Summer School 2011. Welcome to the EDM track!. Educational Data Mining.

547 views • 39 slides

Educational Data Mining

Educational Data Mining. March 3, 2010. Today’s Class. EDM Assignmen t#5 Mega-Survey. Educational Data Mining.

1.33k views • 107 slides

Special Topics in Educational Data Mining. HUDK5199 Spring term, 2013 March 4, 2013. Today’s Class. Reinforcement Learning and Partially Observable Markov Decision Processes

960 views • 88 slides

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...

426 views • 24 slides

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.

361 views • 25 slides

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 …

235 views • 21 slides

456 views • 45 slides

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

103 views • 9 slides

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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