Apriori algorithm
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This document discusses frequent pattern mining algorithms. It describes the Apriori, AprioriTid, and FP-Growth algorithms. The Apriori algorithm uses candidate generation and database scanning to find frequent itemsets. AprioriTid tracks transaction IDs to reduce scans. FP-Growth avoids candidate generation and multiple scans by building a frequent-pattern tree. It finds frequent patterns by mining the tree.
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- Quantum Computing QRG
- Big Data Analytics QRG
- Spacecraft Guidance, Navigation & Control QRG (coming 2026)
- UK Home Computing & The Birth of ARM QRG (coming 2027)
Any questions or comments?
- Please contact Arthur Morgan at [email protected].
100% human made.
UiPath Community Berlin: Orchestrator API, Swagger, and Test Manager API
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📕 Agenda
Welcome & Introductions
Orchestrator API Overview
Exploring the Swagger Interface
Test Manager API Highlights
Streamlining Automation & Testing with APIs (Demo)
Q&A and Open Discussion
Perfect for developers, testers, and automation enthusiasts!
👉 Join our UiPath Community Berlin chapter: https://community.uipath.com/berlin/
This session streamed live on April 29, 2025, 18:00 CET.
Check out all our upcoming UiPath Community sessions at https://community.uipath.com/events/.
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• Seamless Integration with i.MX 8M Plus and i.MX 95 – Toradex SMARC solutions leverage NXP’s i.MX 8 M Plus and i.MX 95 SoCs, delivering power efficiency and AI-ready performance.
• Secure and Reliable – With Secure Boot, over-the-air (OTA) updates, and LTS kernel support, Toradex ensures industrial-grade security and longevity.
• Containerized Workflows for AI & IoT – Support for Docker, ROS, and real-time Linux enables scalable AI, ML, and IoT applications.
• Strong Ecosystem & Developer Support – Toradex offers comprehensive documentation, developer tools, and dedicated support, accelerating time-to-market.
With Toradex’s Linux support for SMARC, developers get a scalable, secure, and high-performance solution for industrial, medical, and AI-driven applications.
Do you have a specific project or application in mind where you're considering SMARC? We can help with Free Compatibility Check and help you with quick time-to-market
For more information: https://www.toradex.com/computer-on-modules/smarc-arm-family
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Big Data Analytics Quick Research Guide by Arthur Morgan
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QRGs include the following:
- A brief, high-level overview of the QRG topic.
- A milestone timeline for the QRG topic.
- Links to various free online resource materials to provide a deeper dive into the QRG topic.
- Conclusion and a recommendation for at least two books available in the SJPL system on the QRG topic.
QRGs planned for the series:
- Artificial Intelligence QRG
- Quantum Computing QRG
- Big Data Analytics QRG
- Spacecraft Guidance, Navigation & Control QRG (coming 2026)
- UK Home Computing & The Birth of ARM QRG (coming 2027)
Any questions or comments?
- Please contact Arthur Morgan at [email protected].
100% human made.
#StandardsGoals for 2025: Standards & certification roundup - Tech Forum 2025
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Link to recording, transcript, and accompanying resource: https://bnctechforum.ca/sessions/standardsgoals-for-2025-standards-certification-roundup/
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Dev Dives: Automate and orchestrate your processes with UiPath Maestro
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📕 Here's what you can expect:
- Modeling: Build end-to-end processes using BPMN.
- Implementing: Integrate agentic tasks, RPA, APIs, and advanced decisioning into processes.
- Operating: Control process instances with rewind, replay, pause, and stop functions.
- Monitoring: Use dashboards and embedded analytics for real-time insights into process instances.
This webinar is a must-attend for developers looking to enhance their agentic automation skills and orchestrate robust, mission-critical processes.
👨🏫 Speaker:
Andrei Vintila, Principal Product Manager @UiPath
This session streamed live on April 29, 2025, 16:00 CET.
Check out all our upcoming Dev Dives sessions at https://community.uipath.com/dev-dives-automation-developer-2025/.
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At S&P Global Commodity Insights, having a complete view of global energy and commodities markets enables customers to make data-driven decisions with confidence and create long-term, sustainable value. 🌍
Explore delta-rs + CDC and how these open-source innovations power lightweight, high-performance data applications beyond Spark! 🚀
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Artificial Intelligence is providing benefits in many areas of work within the heritage sector, from image analysis, to ideas generation, and new research tools. However, it is more critical than ever for people, with analogue intelligence, to ensure the integrity and ethical use of AI. Including real people can improve the use of AI by identifying potential biases, cross-checking results, refining workflows, and providing contextual relevance to AI-driven results.
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This session will explore the research findings from TrustArc’s Privacy Pulse Survey, examining consumer attitudes toward personal data collection and practical suggestions for corporate practices around purchasing third-party data.
Attendees will learn:
- Consumer awareness around data brokers and what consumers are doing to limit data collection
- How businesses assess third-party vendors and their consent management operations
- Where business preparedness needs improvement
- What these trends mean for the future of privacy governance and public trust
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https://alandix.com/academic/talks/CMet2025-AI-Changes-Everything/
Is AI just another technology, or does it fundamentally change the way we live and think?
Every technology has a direct impact with micro-ethical consequences, some good, some bad. However more profound are the ways in which some technologies reshape the very fabric of society with macro-ethical impacts. The invention of the stirrup revolutionised mounted combat, but as a side effect gave rise to the feudal system, which still shapes politics today. The internal combustion engine offers personal freedom and creates pollution, but has also transformed the nature of urban planning and international trade. When we look at AI the micro-ethical issues, such as bias, are most obvious, but the macro-ethical challenges may be greater.
At a micro-ethical level AI has the potential to deepen social, ethnic and gender bias, issues I have warned about since the early 1990s! It is also being used increasingly on the battlefield. However, it also offers amazing opportunities in health and educations, as the recent Nobel prizes for the developers of AlphaFold illustrate. More radically, the need to encode ethics acts as a mirror to surface essential ethical problems and conflicts.
At the macro-ethical level, by the early 2000s digital technology had already begun to undermine sovereignty (e.g. gambling), market economics (through network effects and emergent monopolies), and the very meaning of money. Modern AI is the child of big data, big computation and ultimately big business, intensifying the inherent tendency of digital technology to concentrate power. AI is already unravelling the fundamentals of the social, political and economic world around us, but this is a world that needs radical reimagining to overcome the global environmental and human challenges that confront us. Our challenge is whether to let the threads fall as they may, or to use them to weave a better future.
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What is Model Context Protocol(MCP) - The new technology for communication bw...Vishnu Singh Chundawat
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Apriori algorithm
- 1. Jung Hoon Kim N5, Room 2239 E-mail: [email protected] 2014.01.07 KAIST Knowledge Service Engineering Data Mining Lab. 1
- 2. Introduction Frequent pattern and association rule mining is one of the few exceptions to emerge from machine learning Apriori algorithm AprioriTid algorithm AprioriAll algorithm FP-Tree algorithm KAIST Knowledge Service Engineering Data Mining Lab. 2
- 3. Notation KAIST Knowledge Service Engineering Data Mining Lab. 3
- 4. Principle downward closure property. If an itemset is frequenct, then all of its subsets must also be frequent if an itemset is not frequent, any of its superset is never frequent KAIST Knowledge Service Engineering Data Mining Lab. 4
- 5. Apriori algorithm Pseudo code KAIST Knowledge Service Engineering Data Mining Lab. 5
- 6. Example KAIST Knowledge Service Engineering Data Mining Lab. 6
- 7. Discussion Too many database scanning makes high computation Need minsup & minconf to be specified in advance. Use hash-tree to store the candidate itemsets. Sometimes it adapt trie-structure to store sets. KAIST Knowledge Service Engineering Data Mining Lab. 7
- 8. AprioriTid KAIST Knowledge Service Engineering Data Mining Lab. 8
- 9. AprioriTid KAIST Knowledge Service Engineering Data Mining Lab. 9
- 10. AprioriTid KAIST Knowledge Service Engineering Data Mining Lab. 10
- 11. AprioriTid KAIST Knowledge Service Engineering Data Mining Lab. 11
- 12. FP-Growth To avoid scanning multiple database the cost of database is too high !! To avoid making lots of candidates in apriori algorithm, the bottleneck is generation of candidate How can solve these problems? KAIST Knowledge Service Engineering Data Mining Lab. 12
- 13. FP-Growth Algorithm was too simple 1. Scan the database once, find frequent 1-itemsets (single item patterns) 2. Sort the frequent items in frequency descending order, f-list(F-list = f-c-a-b-m-p) 3. Scan the DB again, construct the FP-tree KAIST Knowledge Service Engineering Data Mining Lab. 13
- 14. FP-Growth Algorithm KAIST Knowledge Service Engineering Data Mining Lab. 14
- 15. FP-Tree Scanning the transaction with TID=100 KAIST Knowledge Service Engineering Data Mining Lab. 15
- 16. FP-Tree Scanning the transaction with TID=200 KAIST Knowledge Service Engineering Data Mining Lab. 16
- 17. FP-Tree Final FP-Tree KAIST Knowledge Service Engineering Data Mining Lab. 17
- 18. Mine a FP-Tree forming conditional pattern bases II. constructing conditional FP-trees III. recursively mining conditional FP-trees I. KAIST Knowledge Service Engineering Data Mining Lab. 18
- 19. Conditional pattern base frequent itemset as a co-occurring suffix pattern for example m : <f, c, a> : support / 2 m : <f,c,a,b> : support / 1 KAIST Knowledge Service Engineering Data Mining Lab. 19
- 20. Conditional pattern tree {m}’s conditional pattern tree KAIST Knowledge Service Engineering Data Mining Lab. 20
- 21. Pseudo Code KAIST Knowledge Service Engineering Data Mining Lab. 21
- 22. Conclusion In data mining, association rules are useful for analyzing and predicting customer behavior. They play an important part in shopping basket data analysis, product clustering, catalog design and store layout. KAIST Knowledge Service Engineering Data Mining Lab. 22
- 23. Thank you KAIST Knowledge Service Engineering Data Mining Lab. 23