| Autor: |
Munandar, Tb. Ai, Musdholifah, Aina |
| Předmět: |
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| Zdroj: |
International Journal of Advanced Research in Computer Science; May/Jun2014, Vol. 5 Issue 5, p164-169, 6p |
| Abstrakt: |
Time complexity of an algorithm is a standard test to obtain the execution time-efficient when implemented in a programming language. Asymptotic analysis approach uses the concept of the Big-O is one of the techniques commonly used to test the time complexity of an algorithm. This study will conduct a comparison test between the three clustering algorithms using time complexity analysis of primitive operations with the running time of applications when the algorithm is used in a programming language or an application. K-means clustering algorithm, Fuzzy C-Means (FCM) and the Hierarchy Agglomerative Clustering (HAC) will be compared based on the analysis of primitive operations and their implementation using MATLAB applications. The results showed that, HAC algorithm has running time that is much more stable than the K-means, although based on the analysis of Big-O, both have the same time complexity. So also between HAC and FCM, HAC is much more stable than the FCM algorithm for all testing using different data sets. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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