Improving the Efficiency of the K-medoids Clustering Algorithm by Getting Initial Medoids
| Autor: | Joaquin Perez-Ortega, Moisés González-Gárcia, Nelva Nely Almanza-Ortega, Adriana Mexicano, Socorro Saenz-Sanchez, Jessica Adams-López, J. M. Rodríguez-Lelis |
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| Rok vydání: | 2017 |
| Předmět: |
k-medoids
Computer science k-means clustering Centroid 02 engineering and technology 01 natural sciences Hybrid algorithm Medoid Set (abstract data type) 010104 statistics & probability 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Sensitivity (control systems) 0101 mathematics Cluster analysis Algorithm |
| Zdroj: | Advances in Intelligent Systems and Computing ISBN: 9783319565347 WorldCIST (1) |
| DOI: | 10.1007/978-3-319-56535-4_13 |
| Popis: | The conventional K-medoids algorithm is one of the most used clustering algorithms, however, one of its limitations is its sensitivity to initial medoids. The generation of optimized initial medoids, which increases the efficiency and effectiveness of K-medoids is proposed. The initial medoids are obtained in two steps, in the first one the data are grouped with an efficient variant of algorithm K-means denominated Early Classification. In the second step, the centroids generated by K-means are transformed into optimized initial medoids. The proposed approach was validated by solving a set of real data sets and compared with the K-medoids algorithm solution. Based on the obtained results it was determined that our approach reduced the time an average of 68%. The quality results of our approach were compared using several well-known validation indexes, and the values were very similar. |
| Databáze: | OpenAIRE |
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