An Automated Clustering Algorithm Based on Agglomerative Clustering
| Autor: | Erdal Kilic, Armagan Karabina |
|---|---|
| Přispěvatelé: | Ondokuz Mayıs Üniversitesi |
| Jazyk: | turečtina |
| Rok vydání: | 2016 |
| Předmět: |
0301 basic medicine
DBSCAN Clustering high-dimensional data Fuzzy clustering Correlation coefficient Computer science Correlation clustering Single-linkage clustering computer.software_genre unsupervised learning Biclustering 03 medical and health sciences CURE data clustering algorithm Consensus clustering correct k value selection Cluster analysis k-medians clustering agglomerative clustering business.industry Pattern recognition Hierarchical clustering Determining the number of clusters in a data set 030104 developmental biology Data stream clustering Canopy clustering algorithm Affinity propagation FLAME clustering Algorithm design Artificial intelligence Data mining Hierarchical clustering of networks business computer k clustering |
| Zdroj: | SIU |
| Popis: | 24th Signal Processing and Communication Application Conference (SIU) -- MAY 16-19, 2016 -- Zonguldak, TURKEY WOS: 000391250900427 The most important one of main problems for K-based clustering algorithm is randomly selected k parameter when running the algorithm. In this study, an automated clustering algorithm based on agglomerative clustering and clusters without taking k parameter from user have been proposed. The main objective of the study is to select correct k value by using Spearman's Correlation Coefficient. This newly proposed automatic k parameter selection method's performance was examined in the study. IEEE, Bulent Ecevit Univ, Dept Elect & Elect Engn, Bulent Ecevit Univ, Dept Biomed Engn, Bulent Ecevit Univ, Dept Comp Engn |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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