Reference list of 265 sources used for the discovery of relationships between data clusters and metadata properties
Visual cluster analysis provides valuable tools that help analysts to understand large data sets in terms of representative clusters and relationships thereof. Often, the found clusters are to be understood in context of belonging categorical, numerical or textual metadata which are given for the da...
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ftpangaea:oai:pangaea.de:doi:10.1594/PANGAEA.785666 2024-09-15T17:47:37+00:00 Reference list of 265 sources used for the discovery of relationships between data clusters and metadata properties Bernard, Jürgen Ruppert, Tobias Scherer, Maximilian Schreck, Tobias Kohlhammer, Jörn MEDIAN LATITUDE: 17.058452 * MEDIAN LONGITUDE: 6.705145 * SOUTH-BOUND LATITUDE: -89.983000 * WEST-BOUND LONGITUDE: -156.607000 * NORTH-BOUND LATITUDE: 78.922700 * EAST-BOUND LONGITUDE: 167.731000 * DATE/TIME START: 2006-01-01T00:00:00 * DATE/TIME END: 2006-12-31T23:59:00 2012 265 datasets https://doi.pangaea.de/10.1594/PANGAEA.785666 https://doi.org/10.1594/PANGAEA.785666 en eng PANGAEA Bernard, Jürgen; Ruppert, Tobias; Scherer, Maximilian; Schreck, Tobias; Kohlhammer, Jörn (2012): Guided discovery of interesting relationships between time series clusters and metadata properties. Special Track on Theory and Applications of Visual Analytics, i-KNOW 2012 conference proceedings, https://doi.org/10.1145/2362456.2362485 https://doi.pangaea.de/10.1594/PANGAEA.785666 https://doi.org/10.1594/PANGAEA.785666 CC-BY-3.0: Creative Commons Attribution 3.0 Unported Access constraints: unrestricted info:eu-repo/semantics/openAccess Alaska USA Antarctica Australia AWIPEV AWIPEV_based BAR Barrow BER Bermuda BOU Boulder Brasilia Brasilia City Distrito Federal Brazil BRB CAB Cabauw Canada CAR Carpentras Chesapeake Light CLH Colorado United States of America Cosmonauts Sea DAR Darwin Dronning Maud Land E13 France Georg von Neumayer Germany GVN Israel Japan KWA Kwajalein LIN Lindenberg MAN Momote Monitoring station MONS NAU Nauru Nauru Island dataset bibliography 2012 ftpangaea https://doi.org/10.1594/PANGAEA.78566610.1145/2362456.2362485 2024-08-21T00:02:27Z Visual cluster analysis provides valuable tools that help analysts to understand large data sets in terms of representative clusters and relationships thereof. Often, the found clusters are to be understood in context of belonging categorical, numerical or textual metadata which are given for the data elements. While often not part of the clustering process, such metadata play an important role and need to be considered during the interactive cluster exploration process. Traditionally, linked-views allow to relate (or loosely speaking: correlate) clusters with metadata or other properties of the underlying cluster data. Manually inspecting the distribution of metadata for each cluster in a linked-view approach is tedious, specially for large data sets, where a large search problem arises. Fully interactive search for potentially useful or interesting cluster to metadata relationships may constitute a cumbersome and long process. To remedy this problem, we propose a novel approach for guiding users in discovering interesting relationships between clusters and associated metadata. Its goal is to guide the analyst through the potentially huge search space. We focus in our work on metadata of categorical type, which can be summarized for a cluster in form of a histogram. We start from a given visual cluster representation, and compute certain measures of interestingness defined on the distribution of metadata categories for the clusters. These measures are used to automatically score and rank the clusters for potential interestingness regarding the distribution of categorical metadata. Identified interesting relationships are highlighted in the visual cluster representation for easy inspection by the user. We present a system implementing an encompassing, yet extensible, set of interestingness scores for categorical metadata, which can also be extended to numerical metadata. Appropriate visual representations are provided for showing the visual correlations, as well as the calculated ranking scores. Focusing on ... Other/Unknown Material Antarc* Antarctica Barrow Cosmonauts sea Dronning Maud Land Alaska PANGAEA - Data Publisher for Earth & Environmental Science ENVELOPE(-156.607000,167.731000,78.922700,-89.983000) |
institution |
Open Polar |
collection |
PANGAEA - Data Publisher for Earth & Environmental Science |
op_collection_id |
ftpangaea |
language |
English |
topic |
Alaska USA Antarctica Australia AWIPEV AWIPEV_based BAR Barrow BER Bermuda BOU Boulder Brasilia Brasilia City Distrito Federal Brazil BRB CAB Cabauw Canada CAR Carpentras Chesapeake Light CLH Colorado United States of America Cosmonauts Sea DAR Darwin Dronning Maud Land E13 France Georg von Neumayer Germany GVN Israel Japan KWA Kwajalein LIN Lindenberg MAN Momote Monitoring station MONS NAU Nauru Nauru Island |
spellingShingle |
Alaska USA Antarctica Australia AWIPEV AWIPEV_based BAR Barrow BER Bermuda BOU Boulder Brasilia Brasilia City Distrito Federal Brazil BRB CAB Cabauw Canada CAR Carpentras Chesapeake Light CLH Colorado United States of America Cosmonauts Sea DAR Darwin Dronning Maud Land E13 France Georg von Neumayer Germany GVN Israel Japan KWA Kwajalein LIN Lindenberg MAN Momote Monitoring station MONS NAU Nauru Nauru Island Bernard, Jürgen Ruppert, Tobias Scherer, Maximilian Schreck, Tobias Kohlhammer, Jörn Reference list of 265 sources used for the discovery of relationships between data clusters and metadata properties |
topic_facet |
Alaska USA Antarctica Australia AWIPEV AWIPEV_based BAR Barrow BER Bermuda BOU Boulder Brasilia Brasilia City Distrito Federal Brazil BRB CAB Cabauw Canada CAR Carpentras Chesapeake Light CLH Colorado United States of America Cosmonauts Sea DAR Darwin Dronning Maud Land E13 France Georg von Neumayer Germany GVN Israel Japan KWA Kwajalein LIN Lindenberg MAN Momote Monitoring station MONS NAU Nauru Nauru Island |
description |
Visual cluster analysis provides valuable tools that help analysts to understand large data sets in terms of representative clusters and relationships thereof. Often, the found clusters are to be understood in context of belonging categorical, numerical or textual metadata which are given for the data elements. While often not part of the clustering process, such metadata play an important role and need to be considered during the interactive cluster exploration process. Traditionally, linked-views allow to relate (or loosely speaking: correlate) clusters with metadata or other properties of the underlying cluster data. Manually inspecting the distribution of metadata for each cluster in a linked-view approach is tedious, specially for large data sets, where a large search problem arises. Fully interactive search for potentially useful or interesting cluster to metadata relationships may constitute a cumbersome and long process. To remedy this problem, we propose a novel approach for guiding users in discovering interesting relationships between clusters and associated metadata. Its goal is to guide the analyst through the potentially huge search space. We focus in our work on metadata of categorical type, which can be summarized for a cluster in form of a histogram. We start from a given visual cluster representation, and compute certain measures of interestingness defined on the distribution of metadata categories for the clusters. These measures are used to automatically score and rank the clusters for potential interestingness regarding the distribution of categorical metadata. Identified interesting relationships are highlighted in the visual cluster representation for easy inspection by the user. We present a system implementing an encompassing, yet extensible, set of interestingness scores for categorical metadata, which can also be extended to numerical metadata. Appropriate visual representations are provided for showing the visual correlations, as well as the calculated ranking scores. Focusing on ... |
format |
Other/Unknown Material |
author |
Bernard, Jürgen Ruppert, Tobias Scherer, Maximilian Schreck, Tobias Kohlhammer, Jörn |
author_facet |
Bernard, Jürgen Ruppert, Tobias Scherer, Maximilian Schreck, Tobias Kohlhammer, Jörn |
author_sort |
Bernard, Jürgen |
title |
Reference list of 265 sources used for the discovery of relationships between data clusters and metadata properties |
title_short |
Reference list of 265 sources used for the discovery of relationships between data clusters and metadata properties |
title_full |
Reference list of 265 sources used for the discovery of relationships between data clusters and metadata properties |
title_fullStr |
Reference list of 265 sources used for the discovery of relationships between data clusters and metadata properties |
title_full_unstemmed |
Reference list of 265 sources used for the discovery of relationships between data clusters and metadata properties |
title_sort |
reference list of 265 sources used for the discovery of relationships between data clusters and metadata properties |
publisher |
PANGAEA |
publishDate |
2012 |
url |
https://doi.pangaea.de/10.1594/PANGAEA.785666 https://doi.org/10.1594/PANGAEA.785666 |
op_coverage |
MEDIAN LATITUDE: 17.058452 * MEDIAN LONGITUDE: 6.705145 * SOUTH-BOUND LATITUDE: -89.983000 * WEST-BOUND LONGITUDE: -156.607000 * NORTH-BOUND LATITUDE: 78.922700 * EAST-BOUND LONGITUDE: 167.731000 * DATE/TIME START: 2006-01-01T00:00:00 * DATE/TIME END: 2006-12-31T23:59:00 |
long_lat |
ENVELOPE(-156.607000,167.731000,78.922700,-89.983000) |
genre |
Antarc* Antarctica Barrow Cosmonauts sea Dronning Maud Land Alaska |
genre_facet |
Antarc* Antarctica Barrow Cosmonauts sea Dronning Maud Land Alaska |
op_relation |
Bernard, Jürgen; Ruppert, Tobias; Scherer, Maximilian; Schreck, Tobias; Kohlhammer, Jörn (2012): Guided discovery of interesting relationships between time series clusters and metadata properties. Special Track on Theory and Applications of Visual Analytics, i-KNOW 2012 conference proceedings, https://doi.org/10.1145/2362456.2362485 https://doi.pangaea.de/10.1594/PANGAEA.785666 https://doi.org/10.1594/PANGAEA.785666 |
op_rights |
CC-BY-3.0: Creative Commons Attribution 3.0 Unported Access constraints: unrestricted info:eu-repo/semantics/openAccess |
op_doi |
https://doi.org/10.1594/PANGAEA.78566610.1145/2362456.2362485 |
_version_ |
1810497082310524928 |