Projection-Based Clustering through Self-Organization and Swarm Intelligence
It covers aspects of unsupervised machine learning used for knowledge discovery in data science and introduces a data-driven approach to cluster analysis, the Databionic swarm (DBS). DBS consists of the 3D landscape visualization and clustering of data. The 3D landscape enables 3D printing of high-d...
שמור ב:
מחבר ראשי: | |
---|---|
מחברים אחרים: | |
פורמט: | Dissertation |
שפה: | אנגלית |
יצא לאור: |
Philipps-Universität Marburg
2018
|
נושאים: | |
גישה מקוונת: | PDF-Volltext |
תגים: |
הוספת תג
אין תגיות, היה/י הראשונ/ה לתייג את הרשומה!
|
סיכום: | It covers aspects of unsupervised machine learning used for knowledge discovery in data science and introduces a data-driven approach to cluster analysis, the Databionic swarm (DBS). DBS consists of the 3D landscape visualization and clustering of data. The 3D landscape enables 3D printing of high-dimensional data structures. The clustering and number of clusters or an absence of cluster structure are verified by the 3D landscape at a glance. DBS is the first swarm-based technique that shows emergent properties while exploiting concepts of swarm intelligence, self-organization and the Nash equilibrium concept from game theory. It results in the elimination of a global objective function and the setting of parameters. By downloading the R package DBS can be applied to data drawn from diverse research fields and used even by non-professionals in the field of data mining. |
---|---|
תיאור פיזי: | 210 Seiten |
ISBN: | 978-3-658-20540-9 |
DOI: | 10.17192/es2018.0002 |