Search Result Clustering Using Fuzzy C-Mean and Gustafon Kessel Algorithms: A Comparative Study
| Autor: | Nesar Ahmad, Shawki A. Al-Dubaee |
|---|---|
| Rok vydání: | 2010 |
| Předmět: |
Fuzzy clustering
Brown clustering business.industry Correlation clustering computer.software_genre Machine learning Biclustering ComputingMethodologies_PATTERNRECOGNITION CURE data clustering algorithm Canopy clustering algorithm FLAME clustering Artificial intelligence Data mining Cluster analysis business Algorithm computer Mathematics |
| Zdroj: | 2010 First International Conference on Integrated Intelligent Computing. |
| DOI: | 10.1109/iciic.2010.50 |
| Popis: | During the last few years, the search result clustering has attracted a substantial amount of research. In this paper, we present a comparative study of the performance of fuzzy clustering algorithms, namely Fuzzy C-Means (FCM), and Gustafson-Kessel (GK) algorithms with clustering search results. Therefore, there is a need to reduce the information, help filtering out irrelevant items, and favors exploration of unknown or dynamic domains in a better way by clustering the search results. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|