“Show Me the Crowds!” Revealing Cluster Structures Through AMTICS
Abstract OPTICS is a popular tool to analyze the clustering structure of a dataset visually. The created two-dimensional plots indicate very dense areas and cluster candidates in the data as troughs. Each horizontal slice represents an outcome of a density-based clustering specified by the height as...
Main Authors: | , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
SpringerOpen
2020-08-01
|
Series: | Data Science and Engineering |
Subjects: | |
Online Access: | https://doi.org/10.1007/s41019-020-00137-x |
id |
doaj-7c5031f405f446c684602f3c38e3ffb3 |
---|---|
record_format |
Article |
spelling |
doaj-7c5031f405f446c684602f3c38e3ffb32021-08-15T11:17:20ZengSpringerOpenData Science and Engineering2364-11852364-15412020-08-015436037410.1007/s41019-020-00137-x“Show Me the Crowds!” Revealing Cluster Structures Through AMTICSFlorian Richter0Yifeng Lu1Daniyal Kazempour2Thomas Seidl3LMU MunichLMU MunichLMU MunichLMU MunichAbstract OPTICS is a popular tool to analyze the clustering structure of a dataset visually. The created two-dimensional plots indicate very dense areas and cluster candidates in the data as troughs. Each horizontal slice represents an outcome of a density-based clustering specified by the height as the density threshold for clusters. However, in very dynamic and rapidly changing applications, a complex and finely detailed visualization slows down the knowledge discovery. Instead, a framework that provides fast but coarse insights is required to point out structures in the data quickly. The user can then control the direction he wants to put emphasize on for refinement. We develop AMTICS as a novel and efficient divide-and-conquer approach to pre-cluster data in distributed instances and align the results in a hierarchy afterward. An interactive online phase ensures a low complexity while giving the user full control over the partial cluster instances. The offline phase reveals the current data clustering structure with low complexity and at any time.https://doi.org/10.1007/s41019-020-00137-xData streamsHierarchical clusteringDensity-basedVisual analysis |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Florian Richter Yifeng Lu Daniyal Kazempour Thomas Seidl |
spellingShingle |
Florian Richter Yifeng Lu Daniyal Kazempour Thomas Seidl “Show Me the Crowds!” Revealing Cluster Structures Through AMTICS Data Science and Engineering Data streams Hierarchical clustering Density-based Visual analysis |
author_facet |
Florian Richter Yifeng Lu Daniyal Kazempour Thomas Seidl |
author_sort |
Florian Richter |
title |
“Show Me the Crowds!” Revealing Cluster Structures Through AMTICS |
title_short |
“Show Me the Crowds!” Revealing Cluster Structures Through AMTICS |
title_full |
“Show Me the Crowds!” Revealing Cluster Structures Through AMTICS |
title_fullStr |
“Show Me the Crowds!” Revealing Cluster Structures Through AMTICS |
title_full_unstemmed |
“Show Me the Crowds!” Revealing Cluster Structures Through AMTICS |
title_sort |
“show me the crowds!” revealing cluster structures through amtics |
publisher |
SpringerOpen |
series |
Data Science and Engineering |
issn |
2364-1185 2364-1541 |
publishDate |
2020-08-01 |
description |
Abstract OPTICS is a popular tool to analyze the clustering structure of a dataset visually. The created two-dimensional plots indicate very dense areas and cluster candidates in the data as troughs. Each horizontal slice represents an outcome of a density-based clustering specified by the height as the density threshold for clusters. However, in very dynamic and rapidly changing applications, a complex and finely detailed visualization slows down the knowledge discovery. Instead, a framework that provides fast but coarse insights is required to point out structures in the data quickly. The user can then control the direction he wants to put emphasize on for refinement. We develop AMTICS as a novel and efficient divide-and-conquer approach to pre-cluster data in distributed instances and align the results in a hierarchy afterward. An interactive online phase ensures a low complexity while giving the user full control over the partial cluster instances. The offline phase reveals the current data clustering structure with low complexity and at any time. |
topic |
Data streams Hierarchical clustering Density-based Visual analysis |
url |
https://doi.org/10.1007/s41019-020-00137-x |
work_keys_str_mv |
AT florianrichter showmethecrowdsrevealingclusterstructuresthroughamtics AT yifenglu showmethecrowdsrevealingclusterstructuresthroughamtics AT daniyalkazempour showmethecrowdsrevealingclusterstructuresthroughamtics AT thomasseidl showmethecrowdsrevealingclusterstructuresthroughamtics |
_version_ |
1721206981472026624 |