clustering and categorizing
play

CLUSTERING AND CATEGORIZING A n a l y z i n g Q u a l i t a t i ve - PowerPoint PPT Presentation

Feb 26, 2024 •247 likes •506 views

Sren Knudsen CLUSTERING AND CATEGORIZING A n a l y z i n g Q u a l i t a t i ve D a t a CODE DEVELOPMENT CODING SORTING SYTHESIZING THEORIZING Codes Categories Themes Theory Specific Abstract / General - adapted from

  1. Søren Knudsen CLUSTERING AND CATEGORIZING A n a l y z i n g Q u a l i t a t i ve D a t a

  2. CODE DEVELOPMENT CODING SORTING SYTHESIZING THEORIZING Codes Categories Themes Theory Specific Abstract / General - adapted from Saldaña, 2013

  3. CODE DEVELOPMENT CODING SORTING SYTHESIZING THEORIZING Codes Categories Themes Theory Specific Abstract / General • Categorize codes Focused coding • and build themes Axial coding • from: Theoretical coding • • The relationship Pattern coding • between codes Elaborative coding • • The common Longitudinal meaning coding between codes - adapted from Saldaña, 2013

  4. Aim To be able to say something meaningful on a topic To understand what you still know little about To describe in a paper, thesis, presentation, …

  5. Distilling the richness of the data, Focus, Relating concepts, ...

  6. Make sense of the coded data Clustering Categorizing Relating codes around a core focus

  7. Example study: Data analysts imagine collaborative analysis on large displays

  8. Wo r k s h o p s t u d y o n l a r g e d i s p l a y s a n d v i z Artistic photography Knudsen et al., 2012

  9. Excerpt of codes from this study Compare two groups [of data] Novel Compare many groups representation Working with, representing and understanding Persistency groups or segments in data Working with multiple different Representations of data representations of data simultaneously Known representation Confusing Bubble plots Knudsen et al., 2012

  10. Excerpt of codes from this study Compare two groups [of data] Novel Compare many groups representation Working with, representing and understanding Persistency groups or segments in data Working with multiple different Representations of data representations of data simultaneously Known representation Bubble plots Knudsen et al., 2012

  11. Excerpt of codes from this study Compare two groups [of data] Novel Compare many groups representation Working with, representing and Persistency understanding groups or segments in data Working with multiple different Representations of data representations of data simultaneously Known representation Bubble plots Knudsen et al., 2012

  12. Excerpt of codes from this study COMPARE GROUPS OF DATA Compare two groups [of data] Novel Compare many groups representation Working with, representing and Persistency understanding groups or segments in data Working with multiple different Representations of data representations of data simultaneously Known representation Bubble plots Knudsen et al., 2012

  13. Excerpt of codes from this study COMPARE GROUPS OF DATA Compare two groups [of data] Compare many groups Working with, representing and understanding groups or segments in data Working with multiple different representations of data simultaneously Knudsen et al., 2012

  14. Excerpt of codes from this study COMPARE GROUPS OF DATA Compare two groups [of data] Working with, representing and understanding groups or segments in data Working with multiple different representations of data simultaneously Compare many groups Knudsen et al., 2012

  15. Excerpt of codes from this study COMPARE GROUPS OF DATA Working with, representing and understanding groups or segments in data Working with multiple different representations of data simultaneously Compare two groups [of data] ? Compare many groups Knudsen et al., 2012

  16. “The purpose of axial coding is to begin the process of reassembling data that were fractured during open coding” Strauss & Corbin, 1998

  17. Empirical data can have many forms

  18. Can be Auditory Textual (e.g., interview transcripts, web-material, papers) Visual (e.g., photos, videos, drawings) Artifacts

  19. Process and approach Computer Assisted Qualitative Data Analysis Systems (CAQDAS) MaxQDA nVivo Saturate app Based on pen-and-paper Based on a large surface (e.g., whiteboard, table, floor)

  20. EXERCISE: CLUSTERING DATA Analyzing Qualitative Data

  21. VISUAL DATA Collection of postcards by G. Lupi and S. Posavec from the “Dear Data” Project

  22. PROCESS 1. Familiarize yourself with the data set spread out the postcards look at them individually 2. Group them in clusters find postcards that you think share a commonality group these postcards spatially create a label for these groups 3. Look for potential spectrum based on the groups, consider spectrums/axes in the data

  23. EXTRA INFORMATION

  24. List of CAQDAS’s ATLAS.ti: www.atlasti.com HyperRESEARCH: www.researchware.com MAXQDA: www.maxqda.com NVivo: www.qsrinternational.com QDA Miner: www.provalisresearch.com Qualrus: www.qualrus.com Transana: www.transana.org (for audio and video data materials) Weft QDA: www.pressure.to/qda/ Noldus Observer: http://www.noldus.com/human-behavior-research/products/ Saturate app: http://www.saturateapp.com/

Download Presentation
Download Policy: The content available on the website is offered to you 'AS IS' for your personal information and use only. It cannot be commercialized, licensed, or distributed on other websites without prior consent from the author. To download a presentation, simply click this link. If you encounter any difficulties during the download process, it's possible that the publisher has removed the file from their server.

Recommend

CLUSTERING AND CATEGORIZING A n a l y z i n g Q u a l i t a t i ve D a t a CODE DEVELOPMENT

CLUSTERING AND CATEGORIZING A n a l y z i n g Q u a l i t a t i ve D a t a CODE DEVELOPMENT

Sren Knudsen CLUSTERING AND CATEGORIZING A n a l y z i n g Q u a l i t a t i ve D a t a CODE DEVELOPMENT CODING SORTING SYTHESIZING THEORIZING Codes Categories Themes Theory Specific Abstract / General - adapted from

364 views • 25 slides
Lecture 23: Spectral clustering Hierarchical clustering What is a good clustering?

Lecture 23: Spectral clustering Hierarchical clustering What is a good clustering?

Lecture 23: Spectral clustering Hierarchical clustering What is a good clustering? Aykut Erdem May 2016 Hacettepe University Last time K-Means An iterative clustering algorithm - Initialize: Pick K random points as cluster

932 views • 64 slides
Clustering Hierarchical clustering, k-mean clustering Genome 559: Introduction to Statistical and

Clustering Hierarchical clustering, k-mean clustering Genome 559: Introduction to Statistical and

Clustering Hierarchical clustering, k-mean clustering Genome 559: Introduction to Statistical and Computational Genomics Elhanan Borenstein A quick review The clustering problem: partition genes into distinct sets with high homogeneity

772 views • 40 slides
Graph Clustering Graph Clustering What is clustering? What is clustering? Finding patterns

Graph Clustering Graph Clustering What is clustering? What is clustering? Finding patterns

Graph Clustering Graph Clustering What is clustering? What is clustering? Finding patterns in data, or grouping similar groups of data-points together into clusters . Clustering algorithms for numeric data: Lloyds K-means, EM

688 views • 19 slides
Natural Language Processing with Python CS372: Spring, 20 15 Lecture 12 Categorizing and

Natural Language Processing with Python CS372: Spring, 20 15 Lecture 12 Categorizing and

Natural Language Processing with Python CS372: Spring, 20 15 Lecture 12 Categorizing and Tagging Words Jong C. Park Department of Computer Science Korea Advanced Institute of Science and Technology CATEGORIZING AND TAGGING WORDS Using a

688 views • 34 slides
Clustering Hierarchical clustering and k-mean clustering Genome 373 Genomic Informatics

Clustering Hierarchical clustering and k-mean clustering Genome 373 Genomic Informatics

Clustering Hierarchical clustering and k-mean clustering Genome 373 Genomic Informatics Elhanan Borenstein A quick review The clustering problem: partition genes into distinct sets with high homogeneity and high separation Many

768 views • 43 slides
Clustering DSE 210 Clustering in R d Two common uses of clustering: Vector quantization Find

Clustering DSE 210 Clustering in R d Two common uses of clustering: Vector quantization Find

Clustering DSE 210 Clustering in R d Two common uses of clustering: Vector quantization Find a finite set of representatives that provides good coverage of a complex, possibly infinite, high-dimensional space. Finding meaningful

396 views • 36 slides
Clustering: Hierarchical Clustering and K- Means Clustering Machine

Clustering: Hierarchical Clustering and K- Means Clustering Machine

Clustering: Hierarchical Clustering and K- Means Clustering Machine Learning 10-601B Seyoung Kim Many of these slides are derived from William Cohen,

660 views • 46 slides
Clustering and Dimensionality Reduction Preview Clustering K -means clustering

Clustering and Dimensionality Reduction Preview Clustering K -means clustering

Clustering and Dimensionality Reduction Preview Clustering K -means clustering Mixture models Hierarchical clustering Dimensionality reduction Principal component analysis Multidimensional scaling Isomap

1.61k views • 17 slides
Clustering A Categorization of Major Clustering Methods Partitioning Methods

Clustering A Categorization of Major Clustering Methods Partitioning Methods

Clustering What is Clustering? Types of Data in Cluster Analysis Clustering A Categorization of Major Clustering Methods Partitioning Methods Hierarchical Methods 1 2 What is Clustering? What is Clustering? Typical

647 views • 18 slides
Subspace Clustering Ensemble Clustering Subspace Clustering, Ensemble Clustering, Alternative

Subspace Clustering Ensemble Clustering Subspace Clustering, Ensemble Clustering, Alternative

LUDWIG- MAXIMILIANS- INSTITUTE DATABASE UNIVERSITT FOR SYSTEMS MNCHEN MNCHEN INFORMATICS INFORMATICS GROUP GROUP Subspace Clustering Ensemble Clustering Subspace Clustering, Ensemble Clustering, Alternative Clustering, Multiview

513 views • 9 slides
Statements of Functional Expenses for Non-Profits Best Practices for Categorizing and Reporting

Statements of Functional Expenses for Non-Profits Best Practices for Categorizing and Reporting

Presenting a live 110-minute teleconference with interactive Q&A Statements of Functional Expenses for Non-Profits Best Practices for Categorizing and Reporting Expenses Under Accounting Standards TUESDAY, DECEMBER 18, 2012 1pm Eastern |

480 views • 30 slides
Finding Clusters Types of Clustering Approaches: Linkage Based, e.g. Hierarchical Clustering

Finding Clusters Types of Clustering Approaches: Linkage Based, e.g. Hierarchical Clustering

Finding Clusters Types of Clustering Approaches: Linkage Based, e.g. Hierarchical Clustering Clustering by Partitioning, e.g. k-Means Density Based Clustering, e.g. DBScan Grid Based Clustering Compendium slides for Guide to Intelligent

1.32k views • 82 slides
Introduction to Machine Learning, Clustering and EM Barnab s P czos Contents Clustering

Introduction to Machine Learning, Clustering and EM Barnab s P czos Contents Clustering

Introduction to Machine Learning, Clustering and EM Barnab s P czos Contents Clustering K-means Mixture of Gaussians Expectation Maximization Variational Methods 2 Clustering 3 K- means clustering What is clustering?

1.32k views • 66 slides
Behavioral Knowledge from GPS Traces for Categorizing Mobile Users S O U R S E : W W W 2 0 1 7

Behavioral Knowledge from GPS Traces for Categorizing Mobile Users S O U R S E : W W W 2 0 1 7

Modeling of Human Movement Behavioral Knowledge from GPS Traces for Categorizing Mobile Users S O U R S E : W W W 2 0 1 7 A D V I S O R : J I A - L I N G KO H S P E A K E R : H S I U - Y I , C H U D AT E : 2 0 1 7 / 1 1 / 7 Outline

596 views • 28 slides
PAC-Bayesian Analysis of Co-clustering, Graph Clustering and Pairwise Clustering Yevgeny Seldin

PAC-Bayesian Analysis of Co-clustering, Graph Clustering and Pairwise Clustering Yevgeny Seldin

PAC-Bayesian Analysis of Co-clustering, Graph Clustering and Pairwise Clustering Yevgeny Seldin Motivation Example Clustering cannot be analyzed without specifying what it will be used for! be used for! Example Cluster then pack

293 views • 28 slides
Four different ways to represent rotation my head is spinning... The space of rotations { } (

Four different ways to represent rotation my head is spinning... The space of rotations { } (

Four different ways to represent rotation my head is spinning... The space of rotations { } ( ) 3 3 T = = = + SO 3 R R | RR I , det( R ) 1 Special orthogonal group(3): = det( R ) 1 Why

427 views • 28 slides
Epipolar Geometry: Essential Matrix Various slides from previous courses by: D.A. Forsyth

Epipolar Geometry: Essential Matrix Various slides from previous courses by: D.A. Forsyth

CS4501: Introduction to Computer Vision Epipolar Geometry: Essential Matrix Various slides from previous courses by: D.A. Forsyth (Berkeley / UIUC), I. Kokkinos (Ecole Centrale / UCL). S. Lazebnik (UNC / UIUC), S. Seitz (MSR / Facebook), J. Hays

746 views • 49 slides
AXES @ TRECVid MED 2013 Matthijs Douze 1 , Zaid Harchaoui 1 , Dan Oneat a 1 , Danila Potapov

AXES @ TRECVid MED 2013 Matthijs Douze 1 , Zaid Harchaoui 1 , Dan Oneat a 1 , Danila Potapov

AXES @ TRECVid MED 2013 Matthijs Douze 1 , Zaid Harchaoui 1 , Dan Oneat a 1 , Danila Potapov 1 , J ome Revaud 1 , Cordelia Schmid 1 , er Jochen Schwenninger 2 , Jakob Verbeek 1 , Heng Wang 1 1 INRIALEAR, Grenoble, France 2 Fraunhofer

716 views • 44 slides
AXES KIS/INS Interactive 2011 System Overview and Evaluation Kevin McGuinness Dublin City

AXES KIS/INS Interactive 2011 System Overview and Evaluation Kevin McGuinness Dublin City

AXES KIS/INS Interactive 2011 System Overview and Evaluation Kevin McGuinness Dublin City University Robin Aly University Twente Overview System Overview User interface System design Experiments Future System Overview

935 views • 46 slides
The Nucleon Axial-Vector Form Factor at the Physical Point with the HISQ Ensembles Aaron Meyer

The Nucleon Axial-Vector Form Factor at the Physical Point with the HISQ Ensembles Aaron Meyer

The Nucleon Axial-Vector Form Factor at the Physical Point with the HISQ Ensembles Aaron Meyer (asmeyer2012@uchicago.edu) University of Chicago/Fermilab May 1-2, 2015 USQCD All-Hands Meeting Fermilab Lattice/MILC Collaborations + Richard

472 views • 19 slides
Overview of two-photon and two-boson exchange Peter Blunden University of Manitoba

Overview of two-photon and two-boson exchange Peter Blunden University of Manitoba

Overview of two-photon and two-boson exchange Peter Blunden University of Manitoba Electroweak Box Workshop, September 28, 2017 in collaboration with Wally Melnitchouk and AJM Collaboration Outline Recent advances in TPE theory

624 views • 41 slides
Recent CLEO Results on Tau Hadronic Decays J.E. Duboscq Cornell University Tau04, Nara Japan

Recent CLEO Results on Tau Hadronic Decays J.E. Duboscq Cornell University Tau04, Nara Japan

Recent CLEO Results on Tau Hadronic Decays J.E. Duboscq Cornell University Tau04, Nara Japan CLEO Hadronic Tau Results The CLEO3 Detector Tau Decays to 3 Charged Hadrons + PRL90:181802,2003 Structure of KK and Wess-Zumino

601 views • 19 slides
Aerodynamics of Compressors and Turbines (AE 651) Autumn Semester 2009 Instructor : Bhaskar

Aerodynamics of Compressors and Turbines (AE 651) Autumn Semester 2009 Instructor : Bhaskar

Aerodynamics of Compressors and Turbines (AE 651) Autumn Semester 2009 Instructor : Bhaskar Roy Professor, Aerospace Engineering Departm ent I .I .T., Bom bay e-m ail : aeroyia@aero.iitb.ac.in 1 Quiz - 3 1. Gas Turbines Rotors are

305 views • 26 slides

More recommend