The document discusses using the data mining tool WEKA to perform linear regression and clustering analysis. It provides steps for loading the housing unit dataset and building linear regression and EM clustering models in WEKA. The linear regression output shows the attributes that predict housing unit price. The clustering analysis identifies 5 clusters in the data and provides details on the attribute means and standard deviations for each cluster.
Predictive Analytics: It's The Intervention That MattersHealth Catalyst
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In this two-part webinar, get the detailed knowledge you need to make informed decisions about adopting predictive analytics in healthcare so you can separate today's hype from reality. In part 1, you'll learn key learnings from Dale Sanders including 1) our fixation on predictive analytics in readmissions, 2) the common trap of predictions without interventions, 3) the common misconceptions of correlations verses causation, 4) examples of predictions without algorithms, and 5) the importance of putting the basics first.
In part 2, you'll hear from industry expert David Crockett, PhD in a "graduate level" crash course cover key concepts such as machine learning, algorithms, feature selection, classification, tools and more.
Predictive Analytics: It's The Intervention That MattersHealth Catalyst
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In this two-part webinar, get the detailed knowledge you need to make informed decisions about adopting predictive analytics in healthcare so you can separate today's hype from reality. In part 1, you'll learn key learnings from Dale Sanders including 1) our fixation on predictive analytics in readmissions, 2) the common trap of predictions without interventions, 3) the common misconceptions of correlations verses causation, 4) examples of predictions without algorithms, and 5) the importance of putting the basics first.
In part 2, you'll hear from industry expert David Crockett, PhD in a "graduate level" crash course cover key concepts such as machine learning, algorithms, feature selection, classification, tools and more.
I presented these slides at a meeting of ACM data mining group. I discuss using data mining to improve performance of an existing trading system. The presentation was video taped. You can see the video at:
http://fora.tv/2009/05/13/Michael_Bowles_Neural_Nets_and_Rule-Based_Trading_Systems
if you have any questions or comments contact me: mike@mbowles.com or
http://www.linkedin.com/in/mikebowles
Come for the software, stay for the community - How Drupal improves and evolvesGĂĄbor Hojtsy
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Special thanks to Robert Douglass for his source data for the second part of the presentation.
An investigation into the building blocks for Neural Networks and modern day machine learning. This investigation touches on the evolution of the most basic of neural networks to more modern day concepts, particularly in methodologies that allow better training of these networks to produce more accurate real-life models.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
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Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
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Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
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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/
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A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
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Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
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).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
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Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
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Are you looking to streamline your workflows and boost your projectsâ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, youâre in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part âEssentials of Automationâ series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Hereâs what youâll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
Weâll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Donât miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
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Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
DevOps and Testing slides at DASA ConnectKari Kakkonen
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My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
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The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties â USA
Expansion of bot farms â how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks â Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Software Delivery At the Speed of AI: Inflectra Invests In AI-Powered QualityInflectra
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In this insightful webinar, Inflectra explores how artificial intelligence (AI) is transforming software development and testing. Discover how AI-powered tools are revolutionizing every stage of the software development lifecycle (SDLC), from design and prototyping to testing, deployment, and monitoring.
Learn about:
⢠The Future of Testing: How AI is shifting testing towards verification, analysis, and higher-level skills, while reducing repetitive tasks.
⢠Test Automation: How AI-powered test case generation, optimization, and self-healing tests are making testing more efficient and effective.
⢠Visual Testing: Explore the emerging capabilities of AI in visual testing and how it's set to revolutionize UI verification.
⢠Inflectra's AI Solutions: See demonstrations of Inflectra's cutting-edge AI tools like the ChatGPT plugin and Azure Open AI platform, designed to streamline your testing process.
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The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
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Clients donât know what they donât know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clientsâ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
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91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
To Graph or Not to Graph Knowledge Graph Architectures and LLMs
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Weka
1. Data Mining Using WEKA
Submitted to
Prof. Prithwis Mukerjee
Submitted By
Shikha Jayaswal
19th April, 2012
2. Table of Contents
Objective ................................................................................................................................................4
WEKA......................................................................................................................................................4
Running WEKA....................................................................................................................................4
Loading Datasets:...................................................................................................................................5
Linear Regression...................................................................................................................................7
Model.................................................................................................................................................7
Interpreting the Output......................................................................................................................7
Clustering................................................................................................................................................8
Model.................................................................................................................................................8
Interpreting the Output......................................................................................................................9
3. List of Figures:
Figure 1: Weka GUI Chooser...................................................................................................................4
Figure 2: Weka Explorer.........................................................................................................................5
Figure 3: Load Dataset............................................................................................................................6
Figure 4: Linear Regression.....................................................................................................................7
4. Objective
Exhibit the use of WEKA in performing the following data mining tasks:
⢠Linear Regression.
⢠Clustering
WEKA
Weka is a data mining tool developed at the University of Waikato. It uses GNU general public
licenses and is freely available. It is implemented in the java programming language and has GUI for
loading data, running analysis and producing visualizations.
The software could be downloaded from: http://www.cs.waikato.ac.nz/~ml/weka/
The version being used in the current analysis is 3.6.6.
Running WEKA
The following Weka GUI Chooser pops up on running weka:
Figure 1: Weka GUI Chooser
The Explorer button leads to the Weka Explorer window through which data could be loaded and be
used further for analysis.
5. Figure 2: Weka Explorer
Loading Datasets:
The file types supported are:
⢠Arff data files
⢠C4.5 data files
⢠Csv data files
⢠Libsvm data file
⢠Svm ligt data files
⢠Binary serialized data files
⢠Xrff data files
The data file being used for the study is:
7. Linear Regression
Model
Steps for creating the regression model:
1. Click on the Classify tab.
2. Click on the Choose button, in the window that opens up expand classifiers and then
functions, select LinearRegression.
3. Click on the LinearRegression text area, one could see GenericObjectEditor pop-up, in the
dropdown attributeSelectionMethod select No Attribute Selection, Click on OK.
4. Check Use Training Set to use the loaded dataset.
5. In the dropdown select Price/Unit as the dependent variable and click on the Start button.
Figure 4: Linear Regression
Interpreting the Output
Price/Unit = -0.0012 * BTU/Hr + 0.5806 * Weight lbs + 3.7411 * EER + 0 * Unit volume
-1.2524 * Region -2.1025 * Type + 24.8058
8. Clustering
Model
Steps for creating the clustering model:
1. Click on the Cluster tab.
2. Click on the Choose button, in the window that opens up expand clusterers, select EM.
3. Click on the EM text area, one could see GenericObjectEditor pop-up, Fill in the cluster
attributes, Click on OK.
a. -V Verbose.
b. -N The number of clusters to generate. If omitted, EM will use cross validation to
select the number of clusters automatically.
c. -I Terminate after this many iterations if EM has not converged.
d. -S Specify random number seed.
e. -M Set the minimum allowable standard deviation for normal density calculation.
4. Check Use Training Set to use the loaded dataset and click on the Start button.
9. Interpreting the Output
The Clustered Instances:
Cluster Instances
0 7(16%)
1 14(31%)
2 10(22%)
3 3(%)
4 11(24%)
The attributes of the clusters are:
Cluster 0 1 2 3 4
Attribute 0.16 0.3 0.2 0.07 0.27
mean 34.1022 32.5883 39.1963 38.0867 30.9768
Price/Unit std. dev. 4.1176 1.2413 2.2264 1.0193 2.8369
mean 912.8122 499.9553 496.4343 856.6667 347.0964
BTU/Hr std. dev. 105.4301 159.6201 178.5667 57.9272 140.3392
mean 10.4966 5.6066 5.6444 9.5967 3.9301
Weight lbs. std. dev. 1.3785 1.848 2.0181 0.7312 1.559
mean 3.3643 3.9673 4.9873 4.8533 4.4754
EER std. dev 0.2773 0.3885 0.3347 0.1586 0.3313
mean 180985.9 129223.9 71417.94 74000 92473.04
Unit Volume std. dev 239037.4 135545.2 45108.85 44639.3 85150.53
mean 3 3.1226 4 5 4.8882
Region std. dev 0.8848 0.4794 0 0.8848 0.365
mean 1.1427 2 2 1.3333 2
Type std. dev 0.3497 0.3866 0.3866 0.4714 0.3866