Annotated Bibliography of Literature
نویسنده
چکیده
In [1], Aha et al. introduced a framework and methodology for machine learning, which is instances-based learning. They defined a set of rules for nearest neighbor algorithms and extended them. These algorithms are still in use for knearest neighbor classifier, which is very much related with my topic “similarity search”. In [2], Galper et al. described the similarity search and proposed a dynamic programming approach to solve it. The importance for that paper is that it is the first parallel approach for solving similarity search problem in parallel setting. Callahan, in his early paper, presented an optimal parallel algorithm for pair decomposition of a point set in high dimensional spaces [3]. He showed a deterministic optimal algorithm which leads to finding k-nearest neighbors of each point in the dataset. Papadoupulos, proposed a parallel algorithm for processing nearest neighbor quesries using R-tress [4]. The settings are for multiprocessing approach with no data sharing, but activation of relevant windows in parallel using an R-tree. Berchtold [5] offered a new solution to k-nearest neighbor problem and proposed to pre-calculate the nearest neighbors instead of finding them on the fly. This solution can be seen as the approximating the solution space of kNN queries by pre-calculating. It is a sequential solution but some of the ideas in this paper was used by some other parallel kNN applications.
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