Clustering: Applied to Data Structuring and Retrieval
| Autor: | Ogechukwu N. Iloanusi, Charles C. Osuagwu |
|---|---|
| Rok vydání: | 2011 |
| Předmět: |
Clustering high-dimensional data
DBSCAN Fuzzy clustering General Computer Science Computer science Correlation clustering computer.software_genre Biclustering Data retrieval CURE data clustering algorithm Consensus clustering Cluster analysis Brown clustering business.industry k-means clustering Pattern recognition Document clustering Data stream clustering Canopy clustering algorithm Affinity propagation FLAME clustering Data mining Artificial intelligence Hierarchical clustering of networks business computer |
| Zdroj: | International Journal of Advanced Computer Science and Applications. 2 |
| ISSN: | 2156-5570 2158-107X |
| DOI: | 10.14569/ijacsa.2011.021116 |
| Popis: | Clustering is a very useful scheme for data structuring and retrieval behuhcause it can handle large volumes of multi-dimensional data and employs a very fast algorithm. Other forms of data structuring techniques include hashing and binary tree structures. However, clustering has the advantage of employing little computational storage requirements and a fast speed algorithm. In this paper, clustering, k-means clustering and the approaches to effective clustering are extensively discussed. Clustering was employed as a data grouping and retrieval strategy in the filtering of fingerprints in the Fingerprint Verification Competition 2000 database 4(a). An average penetration of 7.41% obtained from the experiment shows clearly that the clustering scheme is an effective retrieval strategy for the filtering of fingerprints. |
| Databáze: | OpenAIRE |
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