A Study of Bayesian Clustering of a Document Set Based on GA

机译：基于GA的文档集的贝叶斯聚类研究

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In this paper, we propose new approximate clustering algorithm that improves the precision of a top-down clustering. Top-down clustering is proposed to improve the clustering speed by Iwayama et al, where the cluster tree is generated by sampling some documents, making a cluster from these, assigning other documents to the nearest node and if the number of assigned documents is large, continuing sampling and clustering from top to down. To improve precision of the top-down clustering method, we propose selecting documents by applying a GA to decide a quasi-optimum layer and using a MDL criteria for evaluating the layer structure of a cluster tree.

机译：在本文中，我们提出了新的近似聚类算法，提高了自上而下的聚类的精度。预上群集建议通过iWayama等人提高群集速度，其中群集树是通过采样一些文档来生成的，使群集从这些文档中，将其他文档分配给最近的节点，如果分配的文档的数量很大，则从上到下继续抽样和聚类。为了提高自上而下的聚类方法的精度，我们通过应用GA来确定准决定准最佳层并使用MDL标准来评估群集树的层结构来提出选择文档。

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《Asia-Pacific conference on simulated evolution and learning》|1999年||共8页
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作者
Keiko Aoki; Kazunori Matsumoto; Keiichiro Hoashi; Kazuo Hashimoto;
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正文语种
中图分类人工智能理论;
关键词
document retrieval; beysian clustering; minimum description length criteria; genetic algorithm;

机译：文档检索;Beysian聚类;最小描述长度标准;遗传算法;

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4. A Study of Bayesian Clustering of a Document Set Based on GA [C] . Keiko Aoki, Kazunori Matsumoto, Keiichiro Hoashi, Asia-Pacific conference on simulated evolution and learning . 1999

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7. Report drawn up on behalf of the Committee on Relations with African States and Madagascar on the proposal of the Commission of the European Communities to the Council (Doc. 236/71) relating to a regulation amending the tariff nomenclature set out in EEC Regulations 522/70 and 653/71, laying down arrangements applicable to processed products based on cereals and rice originating in the Associated African States and Madagascar or the Overseas States and Territories and originating in the United Republic of Tanzania, the Republic of Uganda and the Republic of Kenya. Working Documents 1971-1972. Document 275/71. 13 March 1975 [O] . 1972

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A Study of Bayesian Clustering of a Document Set Based on GA

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