The document discusses computer-assisted conceptualization through clustering and classification of text documents. It notes that conceptualization relies on classification, but that clustering is challenging due to the vast number of possible categorization schemes. While fully automated algorithms can help with clustering, no single method is optimal for all applications. The goal is to develop computer-assisted, rather than fully automated, clustering methods to get human input on the appropriate conceptual categories and assignments for a given problem.
Fast matrix computations for pair-wise and column-wise Katz scores and commut...David Gleich
The document summarizes techniques for efficiently computing pairwise and column-wise Katz scores and commute times in graphs. It presents fast matrix computation methods for computing a single Katz score or commute time, or the top scores. For pairwise Katz scores and commute times, it uses quadrature rules based on the Lanczos method to approximate the matrix functions. For column-wise computations, it employs localized iterative methods that exploit the localized nature of Katz scores and commute times in large graphs.
The document discusses computer-assisted conceptualization through clustering and classification of text documents. It notes that conceptualization relies on classification, but that clustering is challenging due to the vast number of possible categorization schemes. While fully automated algorithms can help with clustering, no single method is optimal for all applications. The goal is to develop computer-assisted, rather than fully automated, clustering methods to get human input on the appropriate conceptual categories and assignments for a given problem.
The document discusses switching from fully automated clustering to computer-assisted clustering. Fully automated clustering fails in general because it is difficult to determine when different clustering models apply without additional context or domain knowledge. Computer-assisted clustering provides an organized list of all possible clusterings to help humans choose the best option, as it would be impossible for a human to manually consider every possibility. The goal is to make the search for the optimal clustering more manageable for researchers.
The document discusses computer-assisted conceptualization through clustering and classification of text documents. It notes that conceptualization relies on classification, but that clustering is challenging due to the vast number of possible categorization schemes. While fully automated algorithms can help with clustering, no single method is optimal for all applications. The goal is to develop computer-assisted, rather than fully automated, clustering methods to get human input on the appropriate conceptual categories and assignments for a given problem.
2024 State of Marketing Report – by HubspotMarius Sescu
https://www.hubspot.com/state-of-marketing
· Scaling relationships and proving ROI
· Social media is the place for search, sales, and service
· Authentic influencer partnerships fuel brand growth
· The strongest connections happen via call, click, chat, and camera.
· Time saved with AI leads to more creative work
· Seeking: A single source of truth
· TLDR; Get on social, try AI, and align your systems.
· More human marketing, powered by robots
ChatGPT is a revolutionary addition to the world since its introduction in 2022. A big shift in the sector of information gathering and processing happened because of this chatbot. What is the story of ChatGPT? How is the bot responding to prompts and generating contents? Swipe through these slides prepared by Expeed Software, a web development company regarding the development and technical intricacies of ChatGPT!
Product Design Trends in 2024 | Teenage EngineeringsPixeldarts
The realm of product design is a constantly changing environment where technology and style intersect. Every year introduces fresh challenges and exciting trends that mold the future of this captivating art form. In this piece, we delve into the significant trends set to influence the look and functionality of product design in the year 2024.
How Race, Age and Gender Shape Attitudes Towards Mental HealthThinkNow
Mental health has been in the news quite a bit lately. Dozens of U.S. states are currently suing Meta for contributing to the youth mental health crisis by inserting addictive features into their products, while the U.S. Surgeon General is touring the nation to bring awareness to the growing epidemic of loneliness and isolation. The country has endured periods of low national morale, such as in the 1970s when high inflation and the energy crisis worsened public sentiment following the Vietnam War. The current mood, however, feels different. Gallup recently reported that national mental health is at an all-time low, with few bright spots to lift spirits.
To better understand how Americans are feeling and their attitudes towards mental health in general, ThinkNow conducted a nationally representative quantitative survey of 1,500 respondents and found some interesting differences among ethnic, age and gender groups.
Technology
For example, 52% agree that technology and social media have a negative impact on mental health, but when broken out by race, 61% of Whites felt technology had a negative effect, and only 48% of Hispanics thought it did.
While technology has helped us keep in touch with friends and family in faraway places, it appears to have degraded our ability to connect in person. Staying connected online is a double-edged sword since the same news feed that brings us pictures of the grandkids and fluffy kittens also feeds us news about the wars in Israel and Ukraine, the dysfunction in Washington, the latest mass shooting and the climate crisis.
Hispanics may have a built-in defense against the isolation technology breeds, owing to their large, multigenerational households, strong social support systems, and tendency to use social media to stay connected with relatives abroad.
Age and Gender
When asked how individuals rate their mental health, men rate it higher than women by 11 percentage points, and Baby Boomers rank it highest at 83%, saying it’s good or excellent vs. 57% of Gen Z saying the same.
Gen Z spends the most amount of time on social media, so the notion that social media negatively affects mental health appears to be correlated. Unfortunately, Gen Z is also the generation that’s least comfortable discussing mental health concerns with healthcare professionals. Only 40% of them state they’re comfortable discussing their issues with a professional compared to 60% of Millennials and 65% of Boomers.
Race Affects Attitudes
As seen in previous research conducted by ThinkNow, Asian Americans lag other groups when it comes to awareness of mental health issues. Twenty-four percent of Asian Americans believe that having a mental health issue is a sign of weakness compared to the 16% average for all groups. Asians are also considerably less likely to be aware of mental health services in their communities (42% vs. 55%) and most likely to seek out information on social media (51% vs. 35%).
Fast matrix computations for pair-wise and column-wise Katz scores and commut...David Gleich
The document summarizes techniques for efficiently computing pairwise and column-wise Katz scores and commute times in graphs. It presents fast matrix computation methods for computing a single Katz score or commute time, or the top scores. For pairwise Katz scores and commute times, it uses quadrature rules based on the Lanczos method to approximate the matrix functions. For column-wise computations, it employs localized iterative methods that exploit the localized nature of Katz scores and commute times in large graphs.
The document discusses computer-assisted conceptualization through clustering and classification of text documents. It notes that conceptualization relies on classification, but that clustering is challenging due to the vast number of possible categorization schemes. While fully automated algorithms can help with clustering, no single method is optimal for all applications. The goal is to develop computer-assisted, rather than fully automated, clustering methods to get human input on the appropriate conceptual categories and assignments for a given problem.
The document discusses switching from fully automated clustering to computer-assisted clustering. Fully automated clustering fails in general because it is difficult to determine when different clustering models apply without additional context or domain knowledge. Computer-assisted clustering provides an organized list of all possible clusterings to help humans choose the best option, as it would be impossible for a human to manually consider every possibility. The goal is to make the search for the optimal clustering more manageable for researchers.
The document discusses computer-assisted conceptualization through clustering and classification of text documents. It notes that conceptualization relies on classification, but that clustering is challenging due to the vast number of possible categorization schemes. While fully automated algorithms can help with clustering, no single method is optimal for all applications. The goal is to develop computer-assisted, rather than fully automated, clustering methods to get human input on the appropriate conceptual categories and assignments for a given problem.
2024 State of Marketing Report – by HubspotMarius Sescu
https://www.hubspot.com/state-of-marketing
· Scaling relationships and proving ROI
· Social media is the place for search, sales, and service
· Authentic influencer partnerships fuel brand growth
· The strongest connections happen via call, click, chat, and camera.
· Time saved with AI leads to more creative work
· Seeking: A single source of truth
· TLDR; Get on social, try AI, and align your systems.
· More human marketing, powered by robots
ChatGPT is a revolutionary addition to the world since its introduction in 2022. A big shift in the sector of information gathering and processing happened because of this chatbot. What is the story of ChatGPT? How is the bot responding to prompts and generating contents? Swipe through these slides prepared by Expeed Software, a web development company regarding the development and technical intricacies of ChatGPT!
Product Design Trends in 2024 | Teenage EngineeringsPixeldarts
The realm of product design is a constantly changing environment where technology and style intersect. Every year introduces fresh challenges and exciting trends that mold the future of this captivating art form. In this piece, we delve into the significant trends set to influence the look and functionality of product design in the year 2024.
How Race, Age and Gender Shape Attitudes Towards Mental HealthThinkNow
Mental health has been in the news quite a bit lately. Dozens of U.S. states are currently suing Meta for contributing to the youth mental health crisis by inserting addictive features into their products, while the U.S. Surgeon General is touring the nation to bring awareness to the growing epidemic of loneliness and isolation. The country has endured periods of low national morale, such as in the 1970s when high inflation and the energy crisis worsened public sentiment following the Vietnam War. The current mood, however, feels different. Gallup recently reported that national mental health is at an all-time low, with few bright spots to lift spirits.
To better understand how Americans are feeling and their attitudes towards mental health in general, ThinkNow conducted a nationally representative quantitative survey of 1,500 respondents and found some interesting differences among ethnic, age and gender groups.
Technology
For example, 52% agree that technology and social media have a negative impact on mental health, but when broken out by race, 61% of Whites felt technology had a negative effect, and only 48% of Hispanics thought it did.
While technology has helped us keep in touch with friends and family in faraway places, it appears to have degraded our ability to connect in person. Staying connected online is a double-edged sword since the same news feed that brings us pictures of the grandkids and fluffy kittens also feeds us news about the wars in Israel and Ukraine, the dysfunction in Washington, the latest mass shooting and the climate crisis.
Hispanics may have a built-in defense against the isolation technology breeds, owing to their large, multigenerational households, strong social support systems, and tendency to use social media to stay connected with relatives abroad.
Age and Gender
When asked how individuals rate their mental health, men rate it higher than women by 11 percentage points, and Baby Boomers rank it highest at 83%, saying it’s good or excellent vs. 57% of Gen Z saying the same.
Gen Z spends the most amount of time on social media, so the notion that social media negatively affects mental health appears to be correlated. Unfortunately, Gen Z is also the generation that’s least comfortable discussing mental health concerns with healthcare professionals. Only 40% of them state they’re comfortable discussing their issues with a professional compared to 60% of Millennials and 65% of Boomers.
Race Affects Attitudes
As seen in previous research conducted by ThinkNow, Asian Americans lag other groups when it comes to awareness of mental health issues. Twenty-four percent of Asian Americans believe that having a mental health issue is a sign of weakness compared to the 16% average for all groups. Asians are also considerably less likely to be aware of mental health services in their communities (42% vs. 55%) and most likely to seek out information on social media (51% vs. 35%).
AI Trends in Creative Operations 2024 by Artwork Flow.pdfmarketingartwork
Creative operations teams expect increased AI use in 2024. Currently, over half of tasks are not AI-enabled, but this is expected to decrease in the coming year. ChatGPT is the most popular AI tool currently. Business leaders are more actively exploring AI benefits than individual contributors. Most respondents do not believe AI will impact workforce size in 2024. However, some inhibitions still exist around AI accuracy and lack of understanding. Creatives primarily want to use AI to save time on mundane tasks and boost productivity.
Organizational culture includes values, norms, systems, symbols, language, assumptions, beliefs, and habits that influence employee behaviors and how people interpret those behaviors. It is important because culture can help or hinder a company's success. Some key aspects of Netflix's culture that help it achieve results include hiring smartly so every position has stars, focusing on attitude over just aptitude, and having a strict policy against peacocks, whiners, and jerks.
PEPSICO Presentation to CAGNY Conference Feb 2024Neil Kimberley
PepsiCo provided a safe harbor statement noting that any forward-looking statements are based on currently available information and are subject to risks and uncertainties. It also provided information on non-GAAP measures and directing readers to its website for disclosure and reconciliation. The document then discussed PepsiCo's business overview, including that it is a global beverage and convenient food company with iconic brands, $91 billion in net revenue in 2023, and nearly $14 billion in core operating profit. It operates through a divisional structure with a focus on local consumers.
Content Methodology: A Best Practices Report (Webinar)contently
This document provides an overview of content methodology best practices. It defines content methodology as establishing objectives, KPIs, and a culture of continuous learning and iteration. An effective methodology focuses on connecting with audiences, creating optimal content, and optimizing processes. It also discusses why a methodology is needed due to the competitive landscape, proliferation of channels, and opportunities for improvement. Components of an effective methodology include defining objectives and KPIs, audience analysis, identifying opportunities, and evaluating resources. The document concludes with recommendations around creating a content plan, testing and optimizing content over 90 days.
How to Prepare For a Successful Job Search for 2024Albert Qian
The document provides guidance on preparing a job search for 2024. It discusses the state of the job market, focusing on growth in AI and healthcare but also continued layoffs. It recommends figuring out what you want to do by researching interests and skills, then conducting informational interviews. The job search should involve building a personal brand on LinkedIn, actively applying to jobs, tailoring resumes and interviews, maintaining job hunting as a habit, and continuing self-improvement. Once hired, the document advises setting new goals and keeping skills and networking active in case of future opportunities.
A report by thenetworkone and Kurio.
The contributing experts and agencies are (in an alphabetical order): Sylwia Rytel, Social Media Supervisor, 180heartbeats + JUNG v MATT (PL), Sharlene Jenner, Vice President - Director of Engagement Strategy, Abelson Taylor (USA), Alex Casanovas, Digital Director, Atrevia (ES), Dora Beilin, Senior Social Strategist, Barrett Hoffher (USA), Min Seo, Campaign Director, Brand New Agency (KR), Deshé M. Gully, Associate Strategist, Day One Agency (USA), Francesca Trevisan, Strategist, Different (IT), Trevor Crossman, CX and Digital Transformation Director; Olivia Hussey, Strategic Planner; Simi Srinarula, Social Media Manager, The Hallway (AUS), James Hebbert, Managing Director, Hylink (CN / UK), Mundy Álvarez, Planning Director; Pedro Rojas, Social Media Manager; Pancho González, CCO, Inbrax (CH), Oana Oprea, Head of Digital Planning, Jam Session Agency (RO), Amy Bottrill, Social Account Director, Launch (UK), Gaby Arriaga, Founder, Leonardo1452 (MX), Shantesh S Row, Creative Director, Liwa (UAE), Rajesh Mehta, Chief Strategy Officer; Dhruv Gaur, Digital Planning Lead; Leonie Mergulhao, Account Supervisor - Social Media & PR, Medulla (IN), Aurelija Plioplytė, Head of Digital & Social, Not Perfect (LI), Daiana Khaidargaliyeva, Account Manager, Osaka Labs (UK / USA), Stefanie Söhnchen, Vice President Digital, PIABO Communications (DE), Elisabeth Winiartati, Managing Consultant, Head of Global Integrated Communications; Lydia Aprina, Account Manager, Integrated Marketing and Communications; Nita Prabowo, Account Manager, Integrated Marketing and Communications; Okhi, Web Developer, PNTR Group (ID), Kei Obusan, Insights Director; Daffi Ranandi, Insights Manager, Radarr (SG), Gautam Reghunath, Co-founder & CEO, Talented (IN), Donagh Humphreys, Head of Social and Digital Innovation, THINKHOUSE (IRE), Sarah Yim, Strategy Director, Zulu Alpha Kilo (CA).
Trends In Paid Search: Navigating The Digital Landscape In 2024Search Engine Journal
The search marketing landscape is evolving rapidly with new technologies, and professionals, like you, rely on innovative paid search strategies to meet changing demands.
It’s important that you’re ready to implement new strategies in 2024.
Check this out and learn the top trends in paid search advertising that are expected to gain traction, so you can drive higher ROI more efficiently in 2024.
You’ll learn:
- The latest trends in AI and automation, and what this means for an evolving paid search ecosystem.
- New developments in privacy and data regulation.
- Emerging ad formats that are expected to make an impact next year.
Watch Sreekant Lanka from iQuanti and Irina Klein from OneMain Financial as they dive into the future of paid search and explore the trends, strategies, and technologies that will shape the search marketing landscape.
If you’re looking to assess your paid search strategy and design an industry-aligned plan for 2024, then this webinar is for you.
5 Public speaking tips from TED - Visualized summarySpeakerHub
From their humble beginnings in 1984, TED has grown into the world’s most powerful amplifier for speakers and thought-leaders to share their ideas. They have over 2,400 filmed talks (not including the 30,000+ TEDx videos) freely available online, and have hosted over 17,500 events around the world.
With over one billion views in a year, it’s no wonder that so many speakers are looking to TED for ideas on how to share their message more effectively.
The article “5 Public-Speaking Tips TED Gives Its Speakers”, by Carmine Gallo for Forbes, gives speakers five practical ways to connect with their audience, and effectively share their ideas on stage.
Whether you are gearing up to get on a TED stage yourself, or just want to master the skills that so many of their speakers possess, these tips and quotes from Chris Anderson, the TED Talks Curator, will encourage you to make the most impactful impression on your audience.
See the full article and more summaries like this on SpeakerHub here: https://speakerhub.com/blog/5-presentation-tips-ted-gives-its-speakers
See the original article on Forbes here:
http://www.forbes.com/forbes/welcome/?toURL=http://www.forbes.com/sites/carminegallo/2016/05/06/5-public-speaking-tips-ted-gives-its-speakers/&refURL=&referrer=#5c07a8221d9b
ChatGPT and the Future of Work - Clark Boyd Clark Boyd
Everyone is in agreement that ChatGPT (and other generative AI tools) will shape the future of work. Yet there is little consensus on exactly how, when, and to what extent this technology will change our world.
Businesses that extract maximum value from ChatGPT will use it as a collaborative tool for everything from brainstorming to technical maintenance.
For individuals, now is the time to pinpoint the skills the future professional will need to thrive in the AI age.
Check out this presentation to understand what ChatGPT is, how it will shape the future of work, and how you can prepare to take advantage.
The document provides career advice for getting into the tech field, including:
- Doing projects and internships in college to build a portfolio.
- Learning about different roles and technologies through industry research.
- Contributing to open source projects to build experience and network.
- Developing a personal brand through a website and social media presence.
- Networking through events, communities, and finding a mentor.
- Practicing interviews through mock interviews and whiteboarding coding questions.
Google's Just Not That Into You: Understanding Core Updates & Search IntentLily Ray
1. Core updates from Google periodically change how its algorithms assess and rank websites and pages. This can impact rankings through shifts in user intent, site quality issues being caught up to, world events influencing queries, and overhauls to search like the E-A-T framework.
2. There are many possible user intents beyond just transactional, navigational and informational. Identifying intent shifts is important during core updates. Sites may need to optimize for new intents through different content types and sections.
3. Responding effectively to core updates requires analyzing "before and after" data to understand changes, identifying new intents or page types, and ensuring content matches appropriate intents across video, images, knowledge graphs and more.
A brief introduction to DataScience with explaining of the concepts, algorithms, machine learning, supervised and unsupervised learning, clustering, statistics, data preprocessing, real-world applications etc.
It's part of a Data Science Corner Campaign where I will be discussing the fundamentals of DataScience, AIML, Statistics etc.
Time Management & Productivity - Best PracticesVit Horky
Here's my presentation on by proven best practices how to manage your work time effectively and how to improve your productivity. It includes practical tips and how to use tools such as Slack, Google Apps, Hubspot, Google Calendar, Gmail and others.
The six step guide to practical project managementMindGenius
The six step guide to practical project management
If you think managing projects is too difficult, think again.
We’ve stripped back project management processes to the
basics – to make it quicker and easier, without sacrificing
the vital ingredients for success.
“If you’re looking for some real-world guidance, then The Six Step Guide to Practical Project Management will help.”
Dr Andrew Makar, Tactical Project Management
Unlocking the Power of ChatGPT and AI in Testing - A Real-World Look, present...Applitools
During this webinar, Anand Bagmar demonstrates how AI tools such as ChatGPT can be applied to various stages of the software development life cycle (SDLC) using an eCommerce application case study. Find the on-demand recording and more info at https://applitools.info/b59
Key takeaways:
• Learn how to use ChatGPT to add AI power to your testing and test automation
• Understand the limitations of the technology and where human expertise is crucial
• Gain insight into different AI-based tools
• Adopt AI-based tools to stay relevant and optimize work for developers and testers
* ChatGPT and OpenAI belong to OpenAI, L.L.C.
The document discusses various AI tools from OpenAI like GPT-3 and DALL-E 2, as well as ChatGPT. It explores how search engines are using AI and things to consider around AI-generated content. Potential SEO uses of ChatGPT are also presented, such as generating content at scale, conducting topic research, and automating basic coding tasks. The document encourages further reading on using ChatGPT for SEO purposes.
More than Just Lines on a Map: Best Practices for U.S Bike Routes
This session highlights best practices and lessons learned for U.S. Bike Route System designation, as well as how and why these routes should be integrated into bicycle planning at the local and regional level.
Presenters:
Presenter: Kevin Luecke Toole Design Group
Co-Presenter: Virginia Sullivan Adventure Cycling Association
AI Trends in Creative Operations 2024 by Artwork Flow.pdfmarketingartwork
Creative operations teams expect increased AI use in 2024. Currently, over half of tasks are not AI-enabled, but this is expected to decrease in the coming year. ChatGPT is the most popular AI tool currently. Business leaders are more actively exploring AI benefits than individual contributors. Most respondents do not believe AI will impact workforce size in 2024. However, some inhibitions still exist around AI accuracy and lack of understanding. Creatives primarily want to use AI to save time on mundane tasks and boost productivity.
Organizational culture includes values, norms, systems, symbols, language, assumptions, beliefs, and habits that influence employee behaviors and how people interpret those behaviors. It is important because culture can help or hinder a company's success. Some key aspects of Netflix's culture that help it achieve results include hiring smartly so every position has stars, focusing on attitude over just aptitude, and having a strict policy against peacocks, whiners, and jerks.
PEPSICO Presentation to CAGNY Conference Feb 2024Neil Kimberley
PepsiCo provided a safe harbor statement noting that any forward-looking statements are based on currently available information and are subject to risks and uncertainties. It also provided information on non-GAAP measures and directing readers to its website for disclosure and reconciliation. The document then discussed PepsiCo's business overview, including that it is a global beverage and convenient food company with iconic brands, $91 billion in net revenue in 2023, and nearly $14 billion in core operating profit. It operates through a divisional structure with a focus on local consumers.
Content Methodology: A Best Practices Report (Webinar)contently
This document provides an overview of content methodology best practices. It defines content methodology as establishing objectives, KPIs, and a culture of continuous learning and iteration. An effective methodology focuses on connecting with audiences, creating optimal content, and optimizing processes. It also discusses why a methodology is needed due to the competitive landscape, proliferation of channels, and opportunities for improvement. Components of an effective methodology include defining objectives and KPIs, audience analysis, identifying opportunities, and evaluating resources. The document concludes with recommendations around creating a content plan, testing and optimizing content over 90 days.
How to Prepare For a Successful Job Search for 2024Albert Qian
The document provides guidance on preparing a job search for 2024. It discusses the state of the job market, focusing on growth in AI and healthcare but also continued layoffs. It recommends figuring out what you want to do by researching interests and skills, then conducting informational interviews. The job search should involve building a personal brand on LinkedIn, actively applying to jobs, tailoring resumes and interviews, maintaining job hunting as a habit, and continuing self-improvement. Once hired, the document advises setting new goals and keeping skills and networking active in case of future opportunities.
A report by thenetworkone and Kurio.
The contributing experts and agencies are (in an alphabetical order): Sylwia Rytel, Social Media Supervisor, 180heartbeats + JUNG v MATT (PL), Sharlene Jenner, Vice President - Director of Engagement Strategy, Abelson Taylor (USA), Alex Casanovas, Digital Director, Atrevia (ES), Dora Beilin, Senior Social Strategist, Barrett Hoffher (USA), Min Seo, Campaign Director, Brand New Agency (KR), Deshé M. Gully, Associate Strategist, Day One Agency (USA), Francesca Trevisan, Strategist, Different (IT), Trevor Crossman, CX and Digital Transformation Director; Olivia Hussey, Strategic Planner; Simi Srinarula, Social Media Manager, The Hallway (AUS), James Hebbert, Managing Director, Hylink (CN / UK), Mundy Álvarez, Planning Director; Pedro Rojas, Social Media Manager; Pancho González, CCO, Inbrax (CH), Oana Oprea, Head of Digital Planning, Jam Session Agency (RO), Amy Bottrill, Social Account Director, Launch (UK), Gaby Arriaga, Founder, Leonardo1452 (MX), Shantesh S Row, Creative Director, Liwa (UAE), Rajesh Mehta, Chief Strategy Officer; Dhruv Gaur, Digital Planning Lead; Leonie Mergulhao, Account Supervisor - Social Media & PR, Medulla (IN), Aurelija Plioplytė, Head of Digital & Social, Not Perfect (LI), Daiana Khaidargaliyeva, Account Manager, Osaka Labs (UK / USA), Stefanie Söhnchen, Vice President Digital, PIABO Communications (DE), Elisabeth Winiartati, Managing Consultant, Head of Global Integrated Communications; Lydia Aprina, Account Manager, Integrated Marketing and Communications; Nita Prabowo, Account Manager, Integrated Marketing and Communications; Okhi, Web Developer, PNTR Group (ID), Kei Obusan, Insights Director; Daffi Ranandi, Insights Manager, Radarr (SG), Gautam Reghunath, Co-founder & CEO, Talented (IN), Donagh Humphreys, Head of Social and Digital Innovation, THINKHOUSE (IRE), Sarah Yim, Strategy Director, Zulu Alpha Kilo (CA).
Trends In Paid Search: Navigating The Digital Landscape In 2024Search Engine Journal
The search marketing landscape is evolving rapidly with new technologies, and professionals, like you, rely on innovative paid search strategies to meet changing demands.
It’s important that you’re ready to implement new strategies in 2024.
Check this out and learn the top trends in paid search advertising that are expected to gain traction, so you can drive higher ROI more efficiently in 2024.
You’ll learn:
- The latest trends in AI and automation, and what this means for an evolving paid search ecosystem.
- New developments in privacy and data regulation.
- Emerging ad formats that are expected to make an impact next year.
Watch Sreekant Lanka from iQuanti and Irina Klein from OneMain Financial as they dive into the future of paid search and explore the trends, strategies, and technologies that will shape the search marketing landscape.
If you’re looking to assess your paid search strategy and design an industry-aligned plan for 2024, then this webinar is for you.
5 Public speaking tips from TED - Visualized summarySpeakerHub
From their humble beginnings in 1984, TED has grown into the world’s most powerful amplifier for speakers and thought-leaders to share their ideas. They have over 2,400 filmed talks (not including the 30,000+ TEDx videos) freely available online, and have hosted over 17,500 events around the world.
With over one billion views in a year, it’s no wonder that so many speakers are looking to TED for ideas on how to share their message more effectively.
The article “5 Public-Speaking Tips TED Gives Its Speakers”, by Carmine Gallo for Forbes, gives speakers five practical ways to connect with their audience, and effectively share their ideas on stage.
Whether you are gearing up to get on a TED stage yourself, or just want to master the skills that so many of their speakers possess, these tips and quotes from Chris Anderson, the TED Talks Curator, will encourage you to make the most impactful impression on your audience.
See the full article and more summaries like this on SpeakerHub here: https://speakerhub.com/blog/5-presentation-tips-ted-gives-its-speakers
See the original article on Forbes here:
http://www.forbes.com/forbes/welcome/?toURL=http://www.forbes.com/sites/carminegallo/2016/05/06/5-public-speaking-tips-ted-gives-its-speakers/&refURL=&referrer=#5c07a8221d9b
ChatGPT and the Future of Work - Clark Boyd Clark Boyd
Everyone is in agreement that ChatGPT (and other generative AI tools) will shape the future of work. Yet there is little consensus on exactly how, when, and to what extent this technology will change our world.
Businesses that extract maximum value from ChatGPT will use it as a collaborative tool for everything from brainstorming to technical maintenance.
For individuals, now is the time to pinpoint the skills the future professional will need to thrive in the AI age.
Check out this presentation to understand what ChatGPT is, how it will shape the future of work, and how you can prepare to take advantage.
The document provides career advice for getting into the tech field, including:
- Doing projects and internships in college to build a portfolio.
- Learning about different roles and technologies through industry research.
- Contributing to open source projects to build experience and network.
- Developing a personal brand through a website and social media presence.
- Networking through events, communities, and finding a mentor.
- Practicing interviews through mock interviews and whiteboarding coding questions.
Google's Just Not That Into You: Understanding Core Updates & Search IntentLily Ray
1. Core updates from Google periodically change how its algorithms assess and rank websites and pages. This can impact rankings through shifts in user intent, site quality issues being caught up to, world events influencing queries, and overhauls to search like the E-A-T framework.
2. There are many possible user intents beyond just transactional, navigational and informational. Identifying intent shifts is important during core updates. Sites may need to optimize for new intents through different content types and sections.
3. Responding effectively to core updates requires analyzing "before and after" data to understand changes, identifying new intents or page types, and ensuring content matches appropriate intents across video, images, knowledge graphs and more.
A brief introduction to DataScience with explaining of the concepts, algorithms, machine learning, supervised and unsupervised learning, clustering, statistics, data preprocessing, real-world applications etc.
It's part of a Data Science Corner Campaign where I will be discussing the fundamentals of DataScience, AIML, Statistics etc.
Time Management & Productivity - Best PracticesVit Horky
Here's my presentation on by proven best practices how to manage your work time effectively and how to improve your productivity. It includes practical tips and how to use tools such as Slack, Google Apps, Hubspot, Google Calendar, Gmail and others.
The six step guide to practical project managementMindGenius
The six step guide to practical project management
If you think managing projects is too difficult, think again.
We’ve stripped back project management processes to the
basics – to make it quicker and easier, without sacrificing
the vital ingredients for success.
“If you’re looking for some real-world guidance, then The Six Step Guide to Practical Project Management will help.”
Dr Andrew Makar, Tactical Project Management
Unlocking the Power of ChatGPT and AI in Testing - A Real-World Look, present...Applitools
During this webinar, Anand Bagmar demonstrates how AI tools such as ChatGPT can be applied to various stages of the software development life cycle (SDLC) using an eCommerce application case study. Find the on-demand recording and more info at https://applitools.info/b59
Key takeaways:
• Learn how to use ChatGPT to add AI power to your testing and test automation
• Understand the limitations of the technology and where human expertise is crucial
• Gain insight into different AI-based tools
• Adopt AI-based tools to stay relevant and optimize work for developers and testers
* ChatGPT and OpenAI belong to OpenAI, L.L.C.
The document discusses various AI tools from OpenAI like GPT-3 and DALL-E 2, as well as ChatGPT. It explores how search engines are using AI and things to consider around AI-generated content. Potential SEO uses of ChatGPT are also presented, such as generating content at scale, conducting topic research, and automating basic coding tasks. The document encourages further reading on using ChatGPT for SEO purposes.
More than Just Lines on a Map: Best Practices for U.S Bike Routes
This session highlights best practices and lessons learned for U.S. Bike Route System designation, as well as how and why these routes should be integrated into bicycle planning at the local and regional level.
Presenters:
Presenter: Kevin Luecke Toole Design Group
Co-Presenter: Virginia Sullivan Adventure Cycling Association
More than Just Lines on a Map: Best Practices for U.S Bike Routes
discov-uga.pdf
1. Computer-Assisted Clustering and Conceptualization
Gary King
Institute for Quantitative Social Science
Harvard University
Parthemos Lecture at University of Georgia, 3/4/2011
1
Based on joint work with Justin Grimmer (Harvard Stanford)
Parthemos Lecture at University of
Gary King (Harvard IQSS) Quantitative Discovery / 20
2. A Method for Computer Assisted Conceptualization
Conceptualization through Classification: “one of the most central
and generic of all our conceptual exercises. . . . the foundation not
only for conceptualization, language, and speech, but also for
mathematics, statistics, and data analysis. . . . Without classification,
there could be no advanced conceptualization, reasoning, language,
data analysis or,for that matter, social science research.” (Bailey,
1994).
Parthemos Lecture at University of
Gary King (Harvard IQSS) Quantitative Discovery / 20
3. A Method for Computer Assisted Conceptualization
Conceptualization through Classification: “one of the most central
and generic of all our conceptual exercises. . . . the foundation not
only for conceptualization, language, and speech, but also for
mathematics, statistics, and data analysis. . . . Without classification,
there could be no advanced conceptualization, reasoning, language,
data analysis or,for that matter, social science research.” (Bailey,
1994).
Cluster Analysis: simultaneously (1) invents categories and (2)
assigns documents to categories
Parthemos Lecture at University of
Gary King (Harvard IQSS) Quantitative Discovery / 20
4. A Method for Computer Assisted Conceptualization
Conceptualization through Classification: “one of the most central
and generic of all our conceptual exercises. . . . the foundation not
only for conceptualization, language, and speech, but also for
mathematics, statistics, and data analysis. . . . Without classification,
there could be no advanced conceptualization, reasoning, language,
data analysis or,for that matter, social science research.” (Bailey,
1994).
Cluster Analysis: simultaneously (1) invents categories and (2)
assigns documents to categories
We focus on unstructured text; methods apply more broadly.
Parthemos Lecture at University of
Gary King (Harvard IQSS) Quantitative Discovery / 20
5. A Method for Computer Assisted Conceptualization
Conceptualization through Classification: “one of the most central
and generic of all our conceptual exercises. . . . the foundation not
only for conceptualization, language, and speech, but also for
mathematics, statistics, and data analysis. . . . Without classification,
there could be no advanced conceptualization, reasoning, language,
data analysis or,for that matter, social science research.” (Bailey,
1994).
Cluster Analysis: simultaneously (1) invents categories and (2)
assigns documents to categories
We focus on unstructured text; methods apply more broadly.
Main goal: Switch from Fully Automated to Computer Assisted
Parthemos Lecture at University of
Gary King (Harvard IQSS) Quantitative Discovery / 20
6. What’s Hard about Clustering?
Parthemos Lecture at University of
Gary King (Harvard IQSS) Quantitative Discovery / 20
7. What’s Hard about Clustering?
(aka Why Johnny Can’t Classify)
Parthemos Lecture at University of
Gary King (Harvard IQSS) Quantitative Discovery / 20
8. What’s Hard about Clustering?
(aka Why Johnny Can’t Classify)
Clustering seems easy; its not!
Parthemos Lecture at University of
Gary King (Harvard IQSS) Quantitative Discovery / 20
9. What’s Hard about Clustering?
(aka Why Johnny Can’t Classify)
Clustering seems easy; its not!
Bell(n) = number of ways of partitioning n objects
Parthemos Lecture at University of
Gary King (Harvard IQSS) Quantitative Discovery / 20
10. What’s Hard about Clustering?
(aka Why Johnny Can’t Classify)
Clustering seems easy; its not!
Bell(n) = number of ways of partitioning n objects
Bell(2) = 2 (AB, A B)
Parthemos Lecture at University of
Gary King (Harvard IQSS) Quantitative Discovery / 20
11. What’s Hard about Clustering?
(aka Why Johnny Can’t Classify)
Clustering seems easy; its not!
Bell(n) = number of ways of partitioning n objects
Bell(2) = 2 (AB, A B)
Bell(3) = 5 (ABC, AB C, A BC, AC B, A B C)
Parthemos Lecture at University of
Gary King (Harvard IQSS) Quantitative Discovery / 20
12. What’s Hard about Clustering?
(aka Why Johnny Can’t Classify)
Clustering seems easy; its not!
Bell(n) = number of ways of partitioning n objects
Bell(2) = 2 (AB, A B)
Bell(3) = 5 (ABC, AB C, A BC, AC B, A B C)
Bell(5) = 52
Parthemos Lecture at University of
Gary King (Harvard IQSS) Quantitative Discovery / 20
13. What’s Hard about Clustering?
(aka Why Johnny Can’t Classify)
Clustering seems easy; its not!
Bell(n) = number of ways of partitioning n objects
Bell(2) = 2 (AB, A B)
Bell(3) = 5 (ABC, AB C, A BC, AC B, A B C)
Bell(5) = 52
Bell(100) ≈
Parthemos Lecture at University of
Gary King (Harvard IQSS) Quantitative Discovery / 20
14. What’s Hard about Clustering?
(aka Why Johnny Can’t Classify)
Clustering seems easy; its not!
Bell(n) = number of ways of partitioning n objects
Bell(2) = 2 (AB, A B)
Bell(3) = 5 (ABC, AB C, A BC, AC B, A B C)
Bell(5) = 52
Bell(100) ≈ 1028 × Number of elementary particles in the universe
Parthemos Lecture at University of
Gary King (Harvard IQSS) Quantitative Discovery / 20
15. What’s Hard about Clustering?
(aka Why Johnny Can’t Classify)
Clustering seems easy; its not!
Bell(n) = number of ways of partitioning n objects
Bell(2) = 2 (AB, A B)
Bell(3) = 5 (ABC, AB C, A BC, AC B, A B C)
Bell(5) = 52
Bell(100) ≈ 1028 × Number of elementary particles in the universe
Now imagine choosing the optimal classification scheme by hand!
Parthemos Lecture at University of
Gary King (Harvard IQSS) Quantitative Discovery / 20
16. What’s Hard about Clustering?
(aka Why Johnny Can’t Classify)
Clustering seems easy; its not!
Bell(n) = number of ways of partitioning n objects
Bell(2) = 2 (AB, A B)
Bell(3) = 5 (ABC, AB C, A BC, AC B, A B C)
Bell(5) = 52
Bell(100) ≈ 1028 × Number of elementary particles in the universe
Now imagine choosing the optimal classification scheme by hand!
Fully automated algorithms can help, but which ones?
Parthemos Lecture at University of
Gary King (Harvard IQSS) Quantitative Discovery / 20
17. The Problem with Fully Automated Clustering
Parthemos Lecture at University of
Gary King (Harvard IQSS) Quantitative Discovery / 20
18. The Problem with Fully Automated Clustering
The (Impossible) Goal: optimal, fully automated,
application-independent cluster analysis
Parthemos Lecture at University of
Gary King (Harvard IQSS) Quantitative Discovery / 20
19. The Problem with Fully Automated Clustering
The (Impossible) Goal: optimal, fully automated,
application-independent cluster analysis
No free lunch theorem: every possible clustering method performs
equally well on average over all possible substantive applications
Parthemos Lecture at University of
Gary King (Harvard IQSS) Quantitative Discovery / 20
20. The Problem with Fully Automated Clustering
The (Impossible) Goal: optimal, fully automated,
application-independent cluster analysis
No free lunch theorem: every possible clustering method performs
equally well on average over all possible substantive applications
Existing methods:
Parthemos Lecture at University of
Gary King (Harvard IQSS) Quantitative Discovery / 20
21. The Problem with Fully Automated Clustering
The (Impossible) Goal: optimal, fully automated,
application-independent cluster analysis
No free lunch theorem: every possible clustering method performs
equally well on average over all possible substantive applications
Existing methods:
Many choices: model-based, subspace, spectral, grid-based, graph-
based, fuzzy k-modes, affinity propagation, self-organizing maps,. . .
Parthemos Lecture at University of
Gary King (Harvard IQSS) Quantitative Discovery / 20
22. The Problem with Fully Automated Clustering
The (Impossible) Goal: optimal, fully automated,
application-independent cluster analysis
No free lunch theorem: every possible clustering method performs
equally well on average over all possible substantive applications
Existing methods:
Many choices: model-based, subspace, spectral, grid-based, graph-
based, fuzzy k-modes, affinity propagation, self-organizing maps,. . .
Well-defined statistical, data analytic, or machine learning foundations
Parthemos Lecture at University of
Gary King (Harvard IQSS) Quantitative Discovery / 20
23. The Problem with Fully Automated Clustering
The (Impossible) Goal: optimal, fully automated,
application-independent cluster analysis
No free lunch theorem: every possible clustering method performs
equally well on average over all possible substantive applications
Existing methods:
Many choices: model-based, subspace, spectral, grid-based, graph-
based, fuzzy k-modes, affinity propagation, self-organizing maps,. . .
Well-defined statistical, data analytic, or machine learning foundations
How to add substantive knowledge: With few exceptions, unclear
Parthemos Lecture at University of
Gary King (Harvard IQSS) Quantitative Discovery / 20
24. The Problem with Fully Automated Clustering
The (Impossible) Goal: optimal, fully automated,
application-independent cluster analysis
No free lunch theorem: every possible clustering method performs
equally well on average over all possible substantive applications
Existing methods:
Many choices: model-based, subspace, spectral, grid-based, graph-
based, fuzzy k-modes, affinity propagation, self-organizing maps,. . .
Well-defined statistical, data analytic, or machine learning foundations
How to add substantive knowledge: With few exceptions, unclear
The literature: little guidance on when methods apply
Parthemos Lecture at University of
Gary King (Harvard IQSS) Quantitative Discovery / 20
25. The Problem with Fully Automated Clustering
The (Impossible) Goal: optimal, fully automated,
application-independent cluster analysis
No free lunch theorem: every possible clustering method performs
equally well on average over all possible substantive applications
Existing methods:
Many choices: model-based, subspace, spectral, grid-based, graph-
based, fuzzy k-modes, affinity propagation, self-organizing maps,. . .
Well-defined statistical, data analytic, or machine learning foundations
How to add substantive knowledge: With few exceptions, unclear
The literature: little guidance on when methods apply
Deriving such guidance: difficult or impossible
Parthemos Lecture at University of
Gary King (Harvard IQSS) Quantitative Discovery / 20
26. The Problem with Fully Automated Clustering
The (Impossible) Goal: optimal, fully automated,
application-independent cluster analysis
No free lunch theorem: every possible clustering method performs
equally well on average over all possible substantive applications
Existing methods:
Many choices: model-based, subspace, spectral, grid-based, graph-
based, fuzzy k-modes, affinity propagation, self-organizing maps,. . .
Well-defined statistical, data analytic, or machine learning foundations
How to add substantive knowledge: With few exceptions, unclear
The literature: little guidance on when methods apply
Deriving such guidance: difficult or impossible
Deep problem: full automation requires more information
Parthemos Lecture at University of
Gary King (Harvard IQSS) Quantitative Discovery / 20
27. The Problem with Fully Automated Clustering
The (Impossible) Goal: optimal, fully automated,
application-independent cluster analysis
No free lunch theorem: every possible clustering method performs
equally well on average over all possible substantive applications
Existing methods:
Many choices: model-based, subspace, spectral, grid-based, graph-
based, fuzzy k-modes, affinity propagation, self-organizing maps,. . .
Well-defined statistical, data analytic, or machine learning foundations
How to add substantive knowledge: With few exceptions, unclear
The literature: little guidance on when methods apply
Deriving such guidance: difficult or impossible
Deep problem: full automation requires more information
No surprise: everyone’s tried cluster analysis; very few are satisfied
Parthemos Lecture at University of
Gary King (Harvard IQSS) Quantitative Discovery / 20
28. Switch from Fully Automated to Computer Assisted
Parthemos Lecture at University of
Gary King (Harvard IQSS) Quantitative Discovery / 20
29. Switch from Fully Automated to Computer Assisted
Fully Automated Clustering may succeed sometimes, but fails in
general: too hard to understand when each model applies
Parthemos Lecture at University of
Gary King (Harvard IQSS) Quantitative Discovery / 20
30. Switch from Fully Automated to Computer Assisted
Fully Automated Clustering may succeed sometimes, but fails in
general: too hard to understand when each model applies
An alternative: Computer-Assisted Clustering
Parthemos Lecture at University of
Gary King (Harvard IQSS) Quantitative Discovery / 20
31. Switch from Fully Automated to Computer Assisted
Fully Automated Clustering may succeed sometimes, but fails in
general: too hard to understand when each model applies
An alternative: Computer-Assisted Clustering
Easy in theory: list all clusterings; choose the best
Parthemos Lecture at University of
Gary King (Harvard IQSS) Quantitative Discovery / 20
32. Switch from Fully Automated to Computer Assisted
Fully Automated Clustering may succeed sometimes, but fails in
general: too hard to understand when each model applies
An alternative: Computer-Assisted Clustering
Easy in theory: list all clusterings; choose the best
Impossible in practice: Too hard for us mere humans!
Parthemos Lecture at University of
Gary King (Harvard IQSS) Quantitative Discovery / 20
33. Switch from Fully Automated to Computer Assisted
Fully Automated Clustering may succeed sometimes, but fails in
general: too hard to understand when each model applies
An alternative: Computer-Assisted Clustering
Easy in theory: list all clusterings; choose the best
Impossible in practice: Too hard for us mere humans!
An organized list will make the search possible
Parthemos Lecture at University of
Gary King (Harvard IQSS) Quantitative Discovery / 20
34. Switch from Fully Automated to Computer Assisted
Fully Automated Clustering may succeed sometimes, but fails in
general: too hard to understand when each model applies
An alternative: Computer-Assisted Clustering
Easy in theory: list all clusterings; choose the best
Impossible in practice: Too hard for us mere humans!
An organized list will make the search possible
Insight: Many clusterings are perceptually identical
Parthemos Lecture at University of
Gary King (Harvard IQSS) Quantitative Discovery / 20
35. Switch from Fully Automated to Computer Assisted
Fully Automated Clustering may succeed sometimes, but fails in
general: too hard to understand when each model applies
An alternative: Computer-Assisted Clustering
Easy in theory: list all clusterings; choose the best
Impossible in practice: Too hard for us mere humans!
An organized list will make the search possible
Insight: Many clusterings are perceptually identical
E.g.,: consider two clusterings that differ only because one document
(of 10,000) moves from category 5 to 6
Parthemos Lecture at University of
Gary King (Harvard IQSS) Quantitative Discovery / 20
36. Switch from Fully Automated to Computer Assisted
Fully Automated Clustering may succeed sometimes, but fails in
general: too hard to understand when each model applies
An alternative: Computer-Assisted Clustering
Easy in theory: list all clusterings; choose the best
Impossible in practice: Too hard for us mere humans!
An organized list will make the search possible
Insight: Many clusterings are perceptually identical
E.g.,: consider two clusterings that differ only because one document
(of 10,000) moves from category 5 to 6
Question: How to organize clusterings so humans can understand?
Parthemos Lecture at University of
Gary King (Harvard IQSS) Quantitative Discovery / 20
37. Our Idea: Meaning Through Geography
Set of clusterings
Parthemos Lecture at University of
Gary King (Harvard IQSS) Quantitative Discovery / 20
38. Our Idea: Meaning Through Geography
Set of clusterings ≈
A list of unconnected addresses
Parthemos Lecture at University of
Gary King (Harvard IQSS) Quantitative Discovery / 20
39. Our Idea: Meaning Through Geography
Set of clusterings ≈
A list of unconnected addresses
Parthemos Lecture at University of
Gary King (Harvard IQSS) Quantitative Discovery / 20
40. Our Idea: Meaning Through Geography
Set of clusterings ≈
A list of unconnected addresses
We develop a (conceptual) geography of clusterings
Parthemos Lecture at University of
Gary King (Harvard IQSS) Quantitative Discovery / 20
41. A New Strategy
Make it easy to choose best clustering from millions of choices
Parthemos Lecture at University of
Gary King (Harvard IQSS) Quantitative Discovery / 20
42. A New Strategy
Make it easy to choose best clustering from millions of choices
1 Code text as numbers (in one or more of several ways)
Parthemos Lecture at University of
Gary King (Harvard IQSS) Quantitative Discovery / 20
43. A New Strategy
Make it easy to choose best clustering from millions of choices
1 Code text as numbers (in one or more of several ways)
2 Apply all clustering methods we can find to the data — each
representing different (unstated) substantive assumptions (<15 mins)
Parthemos Lecture at University of
Gary King (Harvard IQSS) Quantitative Discovery / 20
44. A New Strategy
Make it easy to choose best clustering from millions of choices
1 Code text as numbers (in one or more of several ways)
2 Apply all clustering methods we can find to the data — each
representing different (unstated) substantive assumptions (<15 mins)
3 (Too much for a person to understand, but organization will help)
Parthemos Lecture at University of
Gary King (Harvard IQSS) Quantitative Discovery / 20
45. A New Strategy
Make it easy to choose best clustering from millions of choices
1 Code text as numbers (in one or more of several ways)
2 Apply all clustering methods we can find to the data — each
representing different (unstated) substantive assumptions (<15 mins)
3 (Too much for a person to understand, but organization will help)
4 Develop an application-independent distance metric between
clusterings, a metric space of clusterings, and a 2-D projection
Parthemos Lecture at University of
Gary King (Harvard IQSS) Quantitative Discovery / 20
46. A New Strategy
Make it easy to choose best clustering from millions of choices
1 Code text as numbers (in one or more of several ways)
2 Apply all clustering methods we can find to the data — each
representing different (unstated) substantive assumptions (<15 mins)
3 (Too much for a person to understand, but organization will help)
4 Develop an application-independent distance metric between
clusterings, a metric space of clusterings, and a 2-D projection
5 “Local cluster ensemble” creates a new clustering at any point, based
on weighted average of nearby clusterings
Parthemos Lecture at University of
Gary King (Harvard IQSS) Quantitative Discovery / 20
47. A New Strategy
Make it easy to choose best clustering from millions of choices
1 Code text as numbers (in one or more of several ways)
2 Apply all clustering methods we can find to the data — each
representing different (unstated) substantive assumptions (<15 mins)
3 (Too much for a person to understand, but organization will help)
4 Develop an application-independent distance metric between
clusterings, a metric space of clusterings, and a 2-D projection
5 “Local cluster ensemble” creates a new clustering at any point, based
on weighted average of nearby clusterings
6 A new animated visualization to explore the space of clusterings
(smoothly morphing from one into others)
Parthemos Lecture at University of
Gary King (Harvard IQSS) Quantitative Discovery / 20
48. A New Strategy
Make it easy to choose best clustering from millions of choices
1 Code text as numbers (in one or more of several ways)
2 Apply all clustering methods we can find to the data — each
representing different (unstated) substantive assumptions (<15 mins)
3 (Too much for a person to understand, but organization will help)
4 Develop an application-independent distance metric between
clusterings, a metric space of clusterings, and a 2-D projection
5 “Local cluster ensemble” creates a new clustering at any point, based
on weighted average of nearby clusterings
6 A new animated visualization to explore the space of clusterings
(smoothly morphing from one into others)
7 Millions of clusterings, easily comprehended
Parthemos Lecture at University of
Gary King (Harvard IQSS) Quantitative Discovery / 20
49. Many Thousands of Clusterings, Sorted & Organized
You choose one (or more), based on insight, discovery, useful information,. . .
Obama Space of mixvmf
Clusterings Clustering 2
Ford Clustering 1
affprop info.costs Carter
Nixon
kmedoids stand.euc Johnson
Carter Eisenhower
rock affprop maximum Ford Roosevelt
kmeans correlation hclust correlation single
hclust pearson single
Eisenhower
Truman Truman
Johnson Roosevelt hclust maximum single
hclust correlation median
hclust binary hclust correlationmedian
centroidpearson centroid
hclust pearson centroid
hclust spec_max Nixon
``Other hclust canberra centroid
``Roosevelt
hclust correlationaverage
average
hclust pearson mcquitty
mcquitty
hclust kendall single hclust maximum ward
Presidents '' hclust euclidean centroid To Carter''
hclust canberra mcquitty binary median
hclust
hclust canberra median
kmeans kendall hclust canberra single mspec_max
hclust binary single biclust_spectral
affprop manhattan
affprop cosine
q
Clinton hclust manhattan centroid
hclust manhattanmedian
hclust maximum single
hclust spearman centroid
hclust maximum centroid
kmedoids manhattan
kendall centroid
mspec_canb hclust euclidean median
hclust canberra average hclust correlation complete
hclust pearson complete
divisive stand.euc
mspec_cos hclust kendall average
hclust manhattan median
hclust spearman median
hclust kendall median kmeans maximum
hclust euclideanaverage
single
hclust maximum mcquitty
hclust maximum complete
kmeans pearson affprop euclidean hclust mcquitty average
hclust manhattan average
euclidean
Kennedy
Kennedy hclust spearman single q divisive euclidean
Bushkmeans binary
hclust binary average kmedoids euclidean
som
hclust spearman average spec_mink
mspec_euc
mspec_mink
hclust binary complete
hclust binary mcquitty divisive manhattan
mspec_man
hclust euclidean mcquitty
hclust euclidean complete hclust kendall complete
hclust correlation ward complete
hclust canberra Bush
clust_convex
hclust euclidean ward
hclust spearman mcquitty
hclust kendall mcquitty dismea Obama
hclust binary ward
hclust canberra ward hclust spearman complete
hclust manhattan complete
spec_canb hclust kendall ward
mixvmfVA
spec_cos spec_euc
hclust manhattan ward kmeans euclidean
kmeans manhattan
spec_man
hclust pearson ward
``Reagan `` Reagan To
Republicans'' hclust spearman ward Obama ''
kmeans spearman
Reagan
kmeans canberra
HWBush
HWBush
Clinton
Reagan
mult_dirproc
Parthemos Lecture at University of
Gary King (Harvard IQSS) Quantitative Discovery / 20
50. Software Screenshot
Parthemos Lecture at University of
Gary King (Harvard IQSS) Quantitative Discovery / 20
51. Evaluating Performance
Parthemos Lecture at University of
Gary King (Harvard IQSS) Quantitative Discovery / 20
52. Evaluating Performance
Goals:
Parthemos Lecture at University of
Gary King (Harvard IQSS) Quantitative Discovery / 20
53. Evaluating Performance
Goals:
Validate Claim: computer-assisted conceptualization outperforms
human conceptualization
Parthemos Lecture at University of
Gary King (Harvard IQSS) Quantitative Discovery / 20
54. Evaluating Performance
Goals:
Validate Claim: computer-assisted conceptualization outperforms
human conceptualization
Demonstrate: new experimental designs for cluster evaluation
Parthemos Lecture at University of
Gary King (Harvard IQSS) Quantitative Discovery / 20
55. Evaluating Performance
Goals:
Validate Claim: computer-assisted conceptualization outperforms
human conceptualization
Demonstrate: new experimental designs for cluster evaluation
Inject human judgement: relying on insights from survey research
Parthemos Lecture at University of
Gary King (Harvard IQSS) Quantitative Discovery / 20
56. Evaluating Performance
Goals:
Validate Claim: computer-assisted conceptualization outperforms
human conceptualization
Demonstrate: new experimental designs for cluster evaluation
Inject human judgement: relying on insights from survey research
We now present three evaluations
Parthemos Lecture at University of
Gary King (Harvard IQSS) Quantitative Discovery / 20
57. Evaluating Performance
Goals:
Validate Claim: computer-assisted conceptualization outperforms
human conceptualization
Demonstrate: new experimental designs for cluster evaluation
Inject human judgement: relying on insights from survey research
We now present three evaluations
Cluster Quality ⇒ RA coders
Parthemos Lecture at University of
Gary King (Harvard IQSS) Quantitative Discovery / 20
58. Evaluating Performance
Goals:
Validate Claim: computer-assisted conceptualization outperforms
human conceptualization
Demonstrate: new experimental designs for cluster evaluation
Inject human judgement: relying on insights from survey research
We now present three evaluations
Cluster Quality ⇒ RA coders
Informative discoveries ⇒ Experienced scholars analyzing texts
Parthemos Lecture at University of
Gary King (Harvard IQSS) Quantitative Discovery / 20
59. Evaluating Performance
Goals:
Validate Claim: computer-assisted conceptualization outperforms
human conceptualization
Demonstrate: new experimental designs for cluster evaluation
Inject human judgement: relying on insights from survey research
We now present three evaluations
Cluster Quality ⇒ RA coders
Informative discoveries ⇒ Experienced scholars analyzing texts
Discovery ⇒ You’re the judge
Parthemos Lecture at University of
Gary King (Harvard IQSS) Quantitative Discovery / 20
60. Evaluation 1: Cluster Quality
Parthemos Lecture at University of
Gary King (Harvard IQSS) Quantitative Discovery / 20
61. Evaluation 1: Cluster Quality
What Are Humans Good For?
Parthemos Lecture at University of
Gary King (Harvard IQSS) Quantitative Discovery / 20
62. Evaluation 1: Cluster Quality
What Are Humans Good For?
They can’t: keep many documents & clusters in their head
Parthemos Lecture at University of
Gary King (Harvard IQSS) Quantitative Discovery / 20
63. Evaluation 1: Cluster Quality
What Are Humans Good For?
They can’t: keep many documents & clusters in their head
They can: compare two documents at a time
Parthemos Lecture at University of
Gary King (Harvard IQSS) Quantitative Discovery / 20
64. Evaluation 1: Cluster Quality
What Are Humans Good For?
They can’t: keep many documents & clusters in their head
They can: compare two documents at a time
=⇒ Cluster quality evaluation: human judgement of document pairs
Parthemos Lecture at University of
Gary King (Harvard IQSS) Quantitative Discovery / 20
65. Evaluation 1: Cluster Quality
What Are Humans Good For?
They can’t: keep many documents & clusters in their head
They can: compare two documents at a time
=⇒ Cluster quality evaluation: human judgement of document pairs
Experimental Design to Assess Cluster Quality
Parthemos Lecture at University of
Gary King (Harvard IQSS) Quantitative Discovery / 20
66. Evaluation 1: Cluster Quality
What Are Humans Good For?
They can’t: keep many documents & clusters in their head
They can: compare two documents at a time
=⇒ Cluster quality evaluation: human judgement of document pairs
Experimental Design to Assess Cluster Quality
automated visualization to choose one clustering
Parthemos Lecture at University of
Gary King (Harvard IQSS) Quantitative Discovery / 20
67. Evaluation 1: Cluster Quality
What Are Humans Good For?
They can’t: keep many documents & clusters in their head
They can: compare two documents at a time
=⇒ Cluster quality evaluation: human judgement of document pairs
Experimental Design to Assess Cluster Quality
automated visualization to choose one clustering
many pairs of documents
Parthemos Lecture at University of
Gary King (Harvard IQSS) Quantitative Discovery / 20
68. Evaluation 1: Cluster Quality
What Are Humans Good For?
They can’t: keep many documents & clusters in their head
They can: compare two documents at a time
=⇒ Cluster quality evaluation: human judgement of document pairs
Experimental Design to Assess Cluster Quality
automated visualization to choose one clustering
many pairs of documents
for coders: (1) unrelated, (2) loosely related, (3) closely related
Parthemos Lecture at University of
Gary King (Harvard IQSS) Quantitative Discovery / 20
69. Evaluation 1: Cluster Quality
What Are Humans Good For?
They can’t: keep many documents & clusters in their head
They can: compare two documents at a time
=⇒ Cluster quality evaluation: human judgement of document pairs
Experimental Design to Assess Cluster Quality
automated visualization to choose one clustering
many pairs of documents
for coders: (1) unrelated, (2) loosely related, (3) closely related
Quality = mean(within cluster) - mean(between clusters)
Parthemos Lecture at University of
Gary King (Harvard IQSS) Quantitative Discovery / 20
70. Evaluation 1: Cluster Quality
What Are Humans Good For?
They can’t: keep many documents & clusters in their head
They can: compare two documents at a time
=⇒ Cluster quality evaluation: human judgement of document pairs
Experimental Design to Assess Cluster Quality
automated visualization to choose one clustering
many pairs of documents
for coders: (1) unrelated, (2) loosely related, (3) closely related
Quality = mean(within cluster) - mean(between clusters)
Bias results against ourselves by not letting evaluators choose clustering
Parthemos Lecture at University of
Gary King (Harvard IQSS) Quantitative Discovery / 20
71. Evaluation 1: Cluster Quality
What Are Humans Good For?
They can’t: keep many documents & clusters in their head
They can: compare two documents at a time
=⇒ Cluster quality evaluation: human judgement of document pairs
Experimental Design to Assess Cluster Quality
automated visualization to choose one clustering
many pairs of documents
for coders: (1) unrelated, (2) loosely related, (3) closely related
Quality = mean(within cluster) - mean(between clusters)
Bias results against ourselves by not letting evaluators choose clustering
Parthemos Lecture at University of
Gary King (Harvard IQSS) Quantitative Discovery / 20
72. Evaluation 1: Cluster Quality
−0.3 −0.2 −0.1 0.1 0.2 0.3
(Our Method) − (Human Coders)
Parthemos Lecture at University of
Gary King (Harvard IQSS) Quantitative Discovery / 20
73. Evaluation 1: Cluster Quality
Lautenberg Press Releases
q
−0.3 −0.2 −0.1 0.1 0.2 0.3
(Our Method) − (Human Coders)
Lautenberg: 200 Senate Press Releases (appropriations, economy,
education, tax, veterans, . . . )
Parthemos Lecture at University of
Gary King (Harvard IQSS) Quantitative Discovery / 20
74. Evaluation 1: Cluster Quality
Lautenberg Press Releases
q
Policy Agendas Project
q
−0.3 −0.2 −0.1 0.1 0.2 0.3
(Our Method) − (Human Coders)
Policy Agendas: 213 quasi-sentences from Bush’s State of the Union
(agriculture, banking & commerce, civil rights/liberties, defense, . . . )
Parthemos Lecture at University of
Gary King (Harvard IQSS) Quantitative Discovery / 20
75. Evaluation 1: Cluster Quality
Lautenberg Press Releases
q
Policy Agendas Project
q
Reuter's Gold Standard
q
−0.3 −0.2 −0.1 0.1 0.2 0.3
(Our Method) − (Human Coders)
Reuter’s: financial news (trade, earnings, copper, gold, coffee, . . . ); “gold
standard” for supervised learning studies
Parthemos Lecture at University of
Gary King (Harvard IQSS) Quantitative Discovery / 20
76. Evaluation 2: More Informative Discoveries
Parthemos Lecture at University of
Gary King (Harvard IQSS) Quantitative Discovery / 20
77. Evaluation 2: More Informative Discoveries
Found 2 scholars analyzing lots of textual data for their work
Parthemos Lecture at University of
Gary King (Harvard IQSS) Quantitative Discovery / 20
78. Evaluation 2: More Informative Discoveries
Found 2 scholars analyzing lots of textual data for their work
Created 6 clusterings:
Parthemos Lecture at University of
Gary King (Harvard IQSS) Quantitative Discovery / 20
79. Evaluation 2: More Informative Discoveries
Found 2 scholars analyzing lots of textual data for their work
Created 6 clusterings:
2 clusterings selected with our method (biased against us)
Parthemos Lecture at University of
Gary King (Harvard IQSS) Quantitative Discovery / 20
80. Evaluation 2: More Informative Discoveries
Found 2 scholars analyzing lots of textual data for their work
Created 6 clusterings:
2 clusterings selected with our method (biased against us)
2 clusterings from each of 2 other methods (varying tuning parameters)
Parthemos Lecture at University of
Gary King (Harvard IQSS) Quantitative Discovery / 20
81. Evaluation 2: More Informative Discoveries
Found 2 scholars analyzing lots of textual data for their work
Created 6 clusterings:
2 clusterings selected with our method (biased against us)
2 clusterings from each of 2 other methods (varying tuning parameters)
Created info packet on each clustering (for each cluster: exemplar
document, automated content summary)
Parthemos Lecture at University of
Gary King (Harvard IQSS) Quantitative Discovery / 20
82. Evaluation 2: More Informative Discoveries
Found 2 scholars analyzing lots of textual data for their work
Created 6 clusterings:
2 clusterings selected with our method (biased against us)
2 clusterings from each of 2 other methods (varying tuning parameters)
Created info packet on each clustering (for each cluster: exemplar
document, automated content summary)
6
Asked for 2 =15 pairwise comparisons
Parthemos Lecture at University of
Gary King (Harvard IQSS) Quantitative Discovery / 20
83. Evaluation 2: More Informative Discoveries
Found 2 scholars analyzing lots of textual data for their work
Created 6 clusterings:
2 clusterings selected with our method (biased against us)
2 clusterings from each of 2 other methods (varying tuning parameters)
Created info packet on each clustering (for each cluster: exemplar
document, automated content summary)
6
Asked for 2 =15 pairwise comparisons
User chooses ⇒ only care about the one clustering that wins
Parthemos Lecture at University of
Gary King (Harvard IQSS) Quantitative Discovery / 20
84. Evaluation 2: More Informative Discoveries
Found 2 scholars analyzing lots of textual data for their work
Created 6 clusterings:
2 clusterings selected with our method (biased against us)
2 clusterings from each of 2 other methods (varying tuning parameters)
Created info packet on each clustering (for each cluster: exemplar
document, automated content summary)
6
Asked for 2 =15 pairwise comparisons
User chooses ⇒ only care about the one clustering that wins
Both cases a Condorcet winner:
Parthemos Lecture at University of
Gary King (Harvard IQSS) Quantitative Discovery / 20
85. Evaluation 2: More Informative Discoveries
Found 2 scholars analyzing lots of textual data for their work
Created 6 clusterings:
2 clusterings selected with our method (biased against us)
2 clusterings from each of 2 other methods (varying tuning parameters)
Created info packet on each clustering (for each cluster: exemplar
document, automated content summary)
6
Asked for 2 =15 pairwise comparisons
User chooses ⇒ only care about the one clustering that wins
Both cases a Condorcet winner:
“Immigration”:
Our Method 1 → vMF 1 → vMF 2 → Our Method 2 → K-Means 1 → K-Means 2
Parthemos Lecture at University of
Gary King (Harvard IQSS) Quantitative Discovery / 20
86. Evaluation 2: More Informative Discoveries
Found 2 scholars analyzing lots of textual data for their work
Created 6 clusterings:
2 clusterings selected with our method (biased against us)
2 clusterings from each of 2 other methods (varying tuning parameters)
Created info packet on each clustering (for each cluster: exemplar
document, automated content summary)
6
Asked for 2 =15 pairwise comparisons
User chooses ⇒ only care about the one clustering that wins
Both cases a Condorcet winner:
“Immigration”:
Our Method 1 → vMF 1 → vMF 2 → Our Method 2 → K-Means 1 → K-Means 2
“Genetic testing”:
Our Method 1 → {Our Method 2, K-Means 1, K-means 2} → Dir Proc. 1 → Dir Proc. 2
Parthemos Lecture at University of
Gary King (Harvard IQSS) Quantitative Discovery / 20
87. Evaluation 3: What Do Members of Congress Do?
Parthemos Lecture at University of
Gary King (Harvard IQSS) Quantitative Discovery / 20
88. Evaluation 3: What Do Members of Congress Do?
- David Mayhew’s (1974) famous typology
Parthemos Lecture at University of
Gary King (Harvard IQSS) Quantitative Discovery / 20
89. Evaluation 3: What Do Members of Congress Do?
- David Mayhew’s (1974) famous typology
- Advertising
Parthemos Lecture at University of
Gary King (Harvard IQSS) Quantitative Discovery / 20
90. Evaluation 3: What Do Members of Congress Do?
- David Mayhew’s (1974) famous typology
- Advertising
- Credit Claiming
Parthemos Lecture at University of
Gary King (Harvard IQSS) Quantitative Discovery / 20
91. Evaluation 3: What Do Members of Congress Do?
- David Mayhew’s (1974) famous typology
- Advertising
- Credit Claiming
- Position Taking
Parthemos Lecture at University of
Gary King (Harvard IQSS) Quantitative Discovery / 20
92. Evaluation 3: What Do Members of Congress Do?
- David Mayhew’s (1974) famous typology
- Advertising
- Credit Claiming
- Position Taking
- Data: 200 press releases from Frank Lautenberg’s office (D-NJ)
Parthemos Lecture at University of
Gary King (Harvard IQSS) Quantitative Discovery / 20
93. Evaluation 3: What Do Members of Congress Do?
- David Mayhew’s (1974) famous typology
- Advertising
- Credit Claiming
- Position Taking
- Data: 200 press releases from Frank Lautenberg’s office (D-NJ)
- Apply our method
Parthemos Lecture at University of
Gary King (Harvard IQSS) Quantitative Discovery / 20
94. Example Discovery
mult_dirproc
kmeans correlation
hclust canberra ward sot_cor
divisive stand.euc
mixvmf
hclust correlationmixvmfVAbinary complete
hclust
mcquitty
hclust pearson single affprop cosine
hclust pearson median
hclust correlation single hclust pearson mcquitty
hclust correlation median
mec hclust pearson average hclust correlation complete
hclust correlation averagehclust pearson complete
hclust binary single
hclust binary average kmeans pearson
som
hclust correlation centroid rock
hclust pearson centroid
hclust binary median hclust binary mcquitty
hclust canberra single
biclust_spectral hclust spearman complete
spec_man
spec_cos
kmeans kendall median
hclust canberra
hclust canberra average
spec_mink
spec_euc
spec_max
mspec_minkspec_canb
mspec_man
affprop maximum kmeans spearman kmeans manhattan mspec_max
mspec_cos
mspec_canb
mspec_euc
kmeansbinary centroid
hclust canberra hclust kendall single
hclust spearman centroid
hclusthclust kendall centroid
kendall medianaverage
average
spearmankendall mcquitty
hclust median
hclust spearman single
hclust spearman mcquitty kendall complete
hclust canberra centroid hclust
hclust manhattan medianmanhattan
euclideankmedoids
hclusthclustmanhattan centroid
hclust manhattan single
hclust manhattan average
single manhattan
affprop
hclust euclidean median
hclust maximum single manhattan
divisive
hclust euclidean centroid
hclust euclidean average
hclust manhattan mcquitty clust_convex hclust correlation ward
hclust euclidean mcquitty
kmedoids euclidean hclust pearson wardstand.euc
kmedoids
hclust maximummedian
divisive centroid
hclust maximumeuclideanaffprop euclidean hclust canberra mcquitty
hclust maximum average
hclust maximum complete
hclust euclidean complete
hclust manhattan maximum mcquitty
hclust complete
dist_ebinary
dist_binary
dist_fbinary
dist_minkowski
dist_canb
dist_max
dist_cos
dismea
hclust manhattan ward affprop info.costs
kmeanshclust euclidean ward
euclidean hclust canberra complete
sot_euc
hclust binary ward
hclusthclust spearman ward
kendall ward
hclust maximum ward kmeans binary
kmeans maximum
Parthemos Lecture at University of
Gary King (Harvard IQSS) Quantitative Discovery / 20
95. Example Discovery
mult_dirproc
kmeans correlation
hclust canberra ward sot_cor
divisive stand.euc
mixvmf
hclust correlationmixvmfVAbinary complete
hclust
mcquitty
hclust pearson single affprop cosine
hclust pearson median
hclust correlation single hclust pearson mcquitty
hclust correlation median
mec hclust pearson average hclust correlation complete
hclust correlation averagehclust pearson complete
hclust binary single
hclust binary average
som
hclust correlation centroid rock
hclust pearson centroid
hclust binary median
hclust canberra single
biclust_spectral
affprop cosine
hclust spearman complete
hclust binary mcquitty
kmeans pearson
spec_man
spec_cos
kmeans kendall median
hclust canberra
hclust canberra average
spec_mink
spec_euc
spec_max
mspec_minkspec_canb
mspec_man
affprop maximum kmeans spearman kmeans manhattan mspec_max
mspec_cos
mspec_canb
mspec_euc
kmeansbinary centroid
hclust canberra hclust kendall single
hclust spearman centroid
hclusthclust kendall centroid
kendall medianaverage
average
spearmankendall mcquitty
hclust median
hclust spearman single
hclust spearman mcquitty kendall complete
hclust canberra centroid hclust
hclust manhattan medianmanhattan
euclideankmedoids
hclusthclustmanhattan centroid
hclust manhattan single
hclust manhattan average
single manhattan
affprop
hclust euclidean median
hclust maximum single manhattan
divisive
hclust euclidean centroid
hclust euclidean average
hclust manhattan mcquitty clust_convex hclust correlation ward
hclust euclidean mcquitty
kmedoids euclidean hclust pearson wardstand.euc
kmedoids
hclust maximummedian
divisive centroid
hclust maximumeuclideanaffprop euclidean
hclust maximum average
hclust maximum complete
hclust euclidean complete
hclust manhattan maximum mcquitty
hclust complete
dist_ebinary
dist_binary
dist_fbinary
dist_minkowski
dist_canb
dist_max
hclust manhattan ward
dist_cos
dismea
hclust canberra mcquitty
Red point: a clustering by
affprop info.costs
kmeanshclust euclidean ward
sot_euc
euclidean hclust canberra complete
hclust binary ward Affinity Propagation-Cosine
hclust maximum ward
hclusthclust spearman ward
kendall ward
kmeans binary (Dueck and Frey 2007)
kmeans maximum
Parthemos Lecture at University of
Gary King (Harvard IQSS) Quantitative Discovery / 20
96. Example Discovery
mult_dirproc
mixvmf kmeans correlation
hclust canberra ward sot_cor
divisive stand.euc
mixvmf
hclust correlationmixvmfVAbinary complete
hclust
mcquitty
hclust pearson single affprop cosine
hclust pearson median
hclust correlation single hclust pearson mcquitty
hclust correlation median
mec hclust pearson average hclust correlation complete
hclust correlation averagehclust pearson complete
hclust binary single
hclust binary average
som
hclust correlation centroid rock
hclust pearson centroid
hclust binary median
hclust canberra single
biclust_spectral
affprop cosine
hclust spearman complete
hclust binary mcquitty
kmeans pearson
spec_man
spec_cos
kmeans kendall median
hclust canberra
hclust canberra average
spec_mink
spec_euc
spec_max
mspec_minkspec_canb
mspec_man
affprop maximum kmeans spearman kmeans manhattan mspec_max
mspec_cos
mspec_canb
mspec_euc
kmeansbinary centroid
hclust canberra hclust kendall single
hclust spearman centroid
hclusthclust kendall centroid
kendall medianaverage
average
spearmankendall mcquitty
hclust median
hclust spearman single
hclust spearman mcquitty kendall complete
hclust canberra centroid hclust
hclust manhattan medianmanhattan
euclideankmedoids
hclusthclustmanhattan centroid
hclust manhattan single
hclust manhattan average
single manhattan
affprop
hclust euclidean median
hclust maximum single manhattan
divisive
hclust euclidean centroid
hclust euclidean average
hclust manhattan mcquitty clust_convex hclust correlation ward
hclust euclidean mcquitty
kmedoids euclidean hclust pearson wardstand.euc
kmedoids
hclust maximummedian
divisive centroid
hclust maximumeuclideanaffprop euclidean
hclust maximum average
hclust maximum complete
hclust euclidean complete
hclust manhattan maximum mcquitty
hclust complete
dist_ebinary
dist_binary
dist_fbinary
dist_minkowski
dist_canb
dist_max
hclust manhattan ward
dist_cos
dismea
hclust canberra mcquitty
Red point: a clustering by
affprop info.costs
kmeanshclust euclidean ward
sot_euc
euclidean hclust canberra complete
hclust binary ward Affinity Propagation-Cosine
hclust maximum ward
hclusthclust spearman ward
kendall ward
kmeans binary (Dueck and Frey 2007)
kmeans maximum
Close to:
Mixture of von Mises-Fisher
distributions (Banerjee et. al.
2005)
Parthemos Lecture at University of
Gary King (Harvard IQSS) Quantitative Discovery / 20