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3 results for “fuzzy clustering”
Figure 3. Different domain-Hybridization of Fuzzy Clustering and Hierarchical Method for Link Discovery
<p>In this paper, we propose a new hybrid algorithm, which combines the features of fuzzy<br> algorithm and hierarchical algorithm. Our algorithm decreases the number of comparisons on link<br> discovery. Using hierarchical algorithm in the first level, the data is divided into two groups. In the<br> second level the worst cluster is determined by matrix memberships and then it split. This stage is<br> repeated until the optimal number of clusters is achieved.Creating typed links, between the entities<br> of different datasets is one of the key challenges on web of data .We presented the clustering<br> approach, which decreases the number of comparisons on link discovery. The results of linking the<br> movies in LinkedMDB to corresponding movies in DBpedia and also linking the places in<br> LinkedGeoData to the places of DBpedia show the it reduces the number of comparisons without<br> loss of recall and precision. Hopefully in the future, we will be able to elevate the proposed method<br> recall to 100 % using the membership matrix.</p>
Figure 1. Workflow of approach-Hybridization of Fuzzy Clustering and Hierarchical Method for Link Discovery
<p>In this section, we present our model in more detail. Figure 1 gives an overview of the<br> workflow. Our approach is organized in two phases: first, the division of data in two clusters; thenthe determination of the worst cluster and splitting. The number of clusters is unknown, but our<br> algorithms can find this parameter based on the complexity of cluster structure.</p>
Figure 2. Decrease comparisons.-Hybridization of Fuzzy Clustering and Hierarchical Method for Link Discovery
<p>In this paper, we propose a new hybrid algorithm, which combines the features of fuzzy<br> algorithm and hierarchical algorithm. Our algorithm decreases the number of comparisons on link<br> discovery. Using hierarchical algorithm in the first level, the data is divided into two groups. In the<br> second level the worst cluster is determined by matrix memberships and then it split. This stage is<br> repeated until the optimal number of clusters is achieved.Creating typed links, between the entities<br> of different datasets is one of the key challenges on web of data .We presented the clustering<br> approach, which decreases the number of comparisons on link discovery. The results of linking the<br> movies in LinkedMDB to corresponding movies in DBpedia and also linking the places in<br> LinkedGeoData to the places of DBpedia show the it reduces the number of comparisons without<br> loss of recall and precision. Hopefully in the future, we will be able to elevate the proposed method<br> recall to 100 % using the membership matrix.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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