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書目名稱Grouping Multidimensional Data影響因子(影響力)




書目名稱Grouping Multidimensional Data影響因子(影響力)學科排名




書目名稱Grouping Multidimensional Data網(wǎng)絡(luò)公開度




書目名稱Grouping Multidimensional Data網(wǎng)絡(luò)公開度學科排名




書目名稱Grouping Multidimensional Data被引頻次




書目名稱Grouping Multidimensional Data被引頻次學科排名




書目名稱Grouping Multidimensional Data年度引用




書目名稱Grouping Multidimensional Data年度引用學科排名




書目名稱Grouping Multidimensional Data讀者反饋




書目名稱Grouping Multidimensional Data讀者反饋學科排名

vasculitis

Fibrinogen

https://doi.org/10.1007/978-1-4684-7009-3ghted graph partitioning), on a variety of high dimension sparse vector data sets representing text documents as bags of words. Performance is measured based on mutual information with a human-imposed classification. Our key findings are that in the quasiorthogonal space of word frequencies: (i) Cos

Traumatic-Grief

A. K. Raychaudhuri,J. M. Peech,R. O. Pohled to the development of a number of new and novel algorithms with different complexity-quality trade-offs. Among them, a class of clustering algorithms that have relatively low computational requirements are those that treat the clustering problem as an optimization process, which seeks to maximize

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mortgage

mortgage

GAVEL

Clustering Very Large Data Sets with Principal Direction Divisive Partitioning,ginal data in a factored form with much less memory, while preserving the individuality of each of the original samples. The scalable clustering algorithm Principal Direction Divisive Partitioning (PDDP) can use the factored form in a natural way to obtain a clustering of the original dataset..The r

STALL

Spina-Bifida

Sampling Methods for Building Initial Partitions, two new approaches for building such initial partitions. The first approach applies a procedure for selecting appropriate samples in the spirit of the Cross-Entropy (CE) method, and the second is based on a sequential summarizing schema. In the first approach, we use a sequential sample clustering