Clusterbased collection selection for information retrieval,Used

Clusterbased collection selection for information retrieval,Used

SKU: DADAX3844318852 In Stock
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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 difference in retrieval performance between our clustering algorithm and the wellknown kmeans algorithm. We suggest to use the algorithm we proposed because it is more scalable.

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We provide a 2-year limited warranty, from the date of purchase for all our products.

If you believe you have received a defective product, or are experiencing any problems with your product, please contact us.

This warranty strictly does not cover damages that arose from negligence, misuse, wear and tear, or not in accordance with product instructions (dropping the product, etc.).

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