Trend-based Document Clustering for Sensitive and Stable Topic Detection

被引：0

作者：

Sato, Yoshihide ^{[1
]}

Kawashima, Harumi ^{[2
]}

Okuda, Hidenori ^{[2
]}

Oku, Masahiro ^{[2
]}

机构：

[1] NTT Corp, NTT West Corp, 1-1 Hikarino Oka, Yokosuka, Kanagawa 2390847, Japan

[2] NTT Corp, NTT Cyber Solut Labs, Yokosuka, Kanagawa 2390847, Japan

来源：

PACLIC 22: PROCEEDINGS OF THE 22ND PACIFIC ASIA CONFERENCE ON LANGUAGE, INFORMATION AND COMPUTATION | 2008年

关键词：

trend; clustering; gradient model; word frequency;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The ability to detect new topics and track them is important given the huge amounts of documents. This paper introduces a trend-based document clustering algorithm for analyzing them. Its key characteristic; is that it gives scores to words on the basis of the fluctuation in word frequency. The algorithm generates clusters in a practical time, with O(n) processing cost due to preliminary calculation of document distances. The attribute allows the user to settle on the best level of granularity for identifying topics. Experiments prove that our algorithm can gather relevant documents with F measure of 63.0% on average from the beginning to the end of topic lifetime and it largely surpasses other algorithms.

引用

页码：331 / +

页数：2

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