An adaptive distributed approach of a self organizing map model for document clustering using ring topology

M. Ajeissh, Sandhya Harikumar
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引用次数: 3

Abstract

Document clustering aims at grouping the documents that are coherent internally with substantial difference amongst different groups. Due to huge availability of documents, the clustering face scalability and accuracy issues. Moreover, there is a dearth for a tool that performs clustering of such voluminous data efficiently. Conventional models focus either on fully centralized or fully distributed approach for document clustering. Hence, this paper proposes a novel approach to perform document clustering by modifying the conventional Self Organizing Map (SOM). The contribution of this work is threefold. The first is a distributed approach to pre-process the documents; the second being an adaptive bottom-up approach towards document clustering and the third being a neighbourhood model suitable for Ring Topology for document clustering. Experimentation on real datasets and comparison with traditional SOM show the efficacy of the proposed approach.
基于环拓扑的自组织地图模型的自适应分布式聚类方法
文档聚类的目的是将内部连贯但不同组之间差异较大的文档进行分组。由于文档的大量可用性,集群面临着可伸缩性和准确性问题。此外,缺乏有效地对如此大量的数据进行聚类的工具。传统模型关注于文档聚类的完全集中式或完全分布式方法。因此,本文提出了一种通过修改传统的自组织映射(SOM)来实现文档聚类的新方法。这项工作的贡献是三重的。第一种是对文档进行预处理的分布式方法;第二种是自适应自底向上的文档聚类方法,第三种是适用于环形拓扑的文档聚类邻域模型。在实际数据集上的实验和与传统SOM的比较表明了该方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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