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Cluster-based collection selection for information retrieval   Bertold van Voorst

Cluster-based collection selection for information retrieval

84 страниц. 2011 год.
LAP Lambert Academic Publishing
The focus of this research is collection selection for distributed information retrieval. The collection descriptions that are necessary for selecting the most relevant collections are often created from information gathered by random sampling. Collection selection based on an incomplete index constructed by using random sampling instead of a full index leads to inferior results. We propose to use collection clustering to compensate for the incompleteness of the indexes. When collection clustering is used we do not only select the collections that are considered relevant based on their collection descriptions, but also collections that have similar content in their indexes. We describe a new clustering algorithm that allows us to specify the sizes of the produced clusters instead of the number of clusters. Our experiments show that that collection clustering can indeed improve the performance of distributed information retrieval systems that use random sampling. There is not much...
 
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