Improvement of the Fast Clustering Algorithm Improved by K-Means in the Big Data
Clustering as a fundamental unsupervised learning is considered an important method of data analysis, and -means is demonstrably the most popular clustering algorithm. In this paper, we consider clustering on feature space to solve the low efficiency caused in the Big Data clustering by -means. Diff...
Gespeichert in:
| Veröffentlicht in: | Applied mathematics and nonlinear sciences Jg. 5; H. 1; S. 1 - 10 |
|---|---|
| Hauptverfasser: | , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Beirut
Sciendo
01.01.2020
De Gruyter Brill Sp. z o.o., Paradigm Publishing Services |
| Schlagworte: | |
| ISSN: | 2444-8656, 2444-8656 |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Zusammenfassung: | Clustering as a fundamental unsupervised learning is considered an important method of data analysis, and
-means is demonstrably the most popular clustering algorithm. In this paper, we consider clustering on feature space to solve the low efficiency caused in the Big Data clustering by
-means. Different from the traditional methods, the algorithm guaranteed the consistency of the clustering accuracy before and after descending dimension, accelerated
-means when the clustering centeres and distance functions satisfy certain conditions, completely matched in the preprocessing step and clustering step, and improved the efficiency and accuracy. Experimental results have demonstrated the effectiveness of the proposed algorithm. |
|---|---|
| Bibliographie: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ISSN: | 2444-8656 2444-8656 |
| DOI: | 10.2478/amns.2020.1.00001 |