Research on statistical discovery of a wide var i ety of patterns with low frequencies and its applications

多种低频模式的统计发现及其应用研究

• 批准号: 21650031
• 负责人: IKEDA Daisuke
• 金额: $ 2.09万
• 依托单位: Kyushu University
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Challenging Exploratory Research
• 财政年份: 2009
• 资助国家: 日本
• 起止时间: 2009 至 2011
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-21650031/
• 关键词: 知識発見とデータマイニング; テキストマイニング; パターン発見; 近似文字列照合; 例外的パターン; フーリエ変換; 畳み込み演算; 遺伝子情報処理; 時系列データ; 予兆発見

The goal of this research is to develop a framework to, given large text data, discover patterns which do not appear frequently. To achieve this goal, we review our existing researches from the following two viewpoints : Mapping of letters for pattern discovery : Using an approximate pattern matching, we have proposed a pattern discovery and evaluated by experiments. In this method, we have found that mapping from several letters into one digit plays an important role. Metric space for pattern discovery : The goal of this topic is to distinguish patterns from non用atterns. Instead of a rigid metric space, we first find usual substructures, and then we find a pattern as a combination of usual substructures. We have evaluated this method by experiments on genome sequences and Web documents.

The goal of this research is to develop a framework to, given large text data, discover patterns which do not appear frequently. To achieve this goal, we review our existing researches from the following two viewpoints : Mapping of letters for pattern discovery : Using an approximate pattern matching, we have proposed a pattern discovery and evaluated by experiments. In this method, we have found that mapping from several letters into one digit plays an important role. Metric space for pattern discovery : The goal of this topic is to distinguish patterns from non with atters. Instead of a rigid metric space, we first find usual substructures, and then we find a pattern as a combination of usual substructures. We have evaluated this method by experiments on genome sequences and Web documents.

项目成果

変化点検出を応用した時系列データからの突発現象の前兆検出アルゴリズム

使用变化点检测从时间序列数据中检测突发事件迹象的算法

• 发表时间: 2010
• 作者: 徳永旭将;池田大輔;中村和幸;樋口知之;吉川顕正;魚住禎司;藤本晶子;森岡昭;湯元清文;CPMN Group
• 通讯作者: CPMN Group

FFTを用いた不一致を許す文字列照合アルゴリズムの精度評価,査読有,情報処理学会論文誌

使用 FFT 进行允许不匹配的字符串匹配算法的准确性评估，同行评审，日本信息处理学会杂志

• 发表时间: 2009
• 期刊: データベース
• 作者: 中藤i哲也;馬場謙介;池田大輔;森雅生;廣川佐千男'
• 通讯作者: 廣川佐千男'

Graph Clustering Based on Optimization of A Macroscopic Structure of Clusters

基于簇宏观结构优化的图聚类

• 发表时间: 2011
• 期刊: Lecture Notes in Computer Science
• 作者: Xin Kang;Fuji Ren;Yuta Taniguchi and Daisuke Ikeda
• 通讯作者: Yuta Taniguchi and Daisuke Ikeda

FFTを用いた不一致を許す文字列照合アルゴリズムの精度評価

使用 FFT 允许不匹配的字符串匹配算法的精度评估

• 发表时间: 2009
• 期刊: 情報処理学会論文誌:データベース Vol.2, No.SIG4(TOD 44)
• 作者: 中藤哲也;馬場謙介;池田大輔;森雅生;廣川佐千男
• 通讯作者: 廣川佐千男

String Matching with Mismatches by Real-valued FFT

通过实值 FFT 进行不匹配的字符串匹配

• 发表时间: 2010
• 期刊: Science and Its Applications-ICCSA 2010, Part IV LNCS6019
• 作者: 比嘉由紀子;Nguyen Thi Yen;Tran Hai Son;Hoang Minh Duc;高木正洋;八田珠郎・宮脇律郎・根本清子・篠原也寸志・真木太一・山田裕久・田村堅志;Kensuke Baba
• 通讯作者: Kensuke Baba