| Título: | Hybridization of Fuzzy Clustering and Hierarchical Method for Link Discovery |
| Autores: |
Enseih Davoodi Jam; University of Isfahan, Isfahan Mohammadali Nematbakhsh; University of Isfahan, Isfahan Mojgan Askarizade; University of Isfahan, Isfahan |
| Fecha: | 2012-11-05 |
| Publicador: | BRAIN: Broad Research in Artificial Intelligence and Neuroscience |
| Fuente: |
 |
| Tipo: | Works in progress, Discussions |
| Tema: |
Artificial Intelligence Hierarchical method, Fuzzy Clustering, similarity measure, Linked Data |
| Descripción: | Clustering is an active research topic in data mining and different methods have been proposed in the literature. Most of these methods are based on numerical attributes. Recently, there have been several proposals to develop clustering methods that support mixed attributes. There arethree basic groups of clustering methods: partitional methods, hierarchical methods and densitybased methods. This paper proposes a hybrid clustering algorithm that combines the advantages of hierarchical clustering and fuzzy clustering techniques and considers mixed attributes. The proposed algorithms improve the fuzzy algorithm by making it less dependent on the initial parameters such as randomly chosen initial cluster centers, and it can determine the number of clusters based on the complexity of cluster structure. Our approach is organized in two phases: first, the division of data in two clusters; then the determination of the worst cluster and splitting. The number of clusters is unknown, but our algorithms can find this parameter based on the complexity of cluster structure. We demonstrate the effectiveness of the clustering approach by evaluating datasets of linked data. We applied the proposed algorithms on three different datasets. Experimental results the proposed algorithm is suitable for link discovery between datasets of linked data. Clustering can decrease the number of comparisons before link discovery. |
| Idioma: | Inglés |
1 Some Considerations on Seriality and Synchronicity por Elena Nechita; "Vasile Alecsandri" University of Bacau | 6 Intelligent Agent for Acquisition of the Mother Tongue Vocabulary por Bogdan Patrut; "Vasile Alecsandri" University of Bacau,Grigor Moldovan; "Babes-Bolyai" University of Cluj-Napoca |
2 Questions Regarding Alterity in Social Collaborative Networks por Cosmin Ion Tomozei; "Vasile Alecsandri" University of Bacau, Romania,Florinela Floria; "Vasile Alecsandri" University of Bacau | 7 Cognitive Medical Multiagent Systems por Barna Iantovics; "Petru Maior" University of Targu Mures |
3 Computational Methods in Medicine por Angel Garrido; National University of Distance Education, Madrid | 8 Connectionism vs. Computational Theory of Mind por Angel Garrido; National University of Distance Education, Madrid |
4 Towards a Metaphorical Type of Architecture: The Inside of the Victorian House por Ioana Boghian; "Vasile Alecsandri" University of Bacau | 9 ADX – Agent for Morphologic Analysis of Lexical Entries in a Dictionary por Bogdan Patrut; "Vasile Alecsandri" University of Bacau |
5 Improving Tools in Artificial Intelligence por Angel Garrido; National University of Distance Education, Madrid | 10 A System for Learning Financial Accounting Bases por Bogdan Patrut; "Vasile Alecsandri" University of Bacau,Iulian Marius Furdu; "Vasile Alecsandri" University of Bacau |