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Computational Methodology for Knowledge Discovery

知识发现的计算方法

基本信息

批准号：
10143101
负责人：
MARUOKA Akira
金额：
$ 51.01万
依托单位：
Tohoku University
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research on Priority Areas (A)
财政年份：
1998
资助国家：
日本
起止时间：
1998 至 2000
项目状态：
已结题

项目摘要

The amount of data collected from various fields is growing exponentially and the task of analyzing data to extract useful information behind it is becoming more and more difficult accordingly. To extract useful information from data, there must be certain appropriate interaction between the extraction process and data. Through the interaction various processes, such as memorizing certain information, Iearning, evolution, and possibly discovering knowledge will be performed. The major hurdles to automatically extracting knowledge from huge amount of data is the limitations on computational resources. Group A03 aims to propose and develop computational models and methodologies for knowledge discovery. To achieve the purpose we explore various topics including algorithms dealing with heterogeneous data which may be strongly structured or poorly structured.Among the results of this project, the ones concerning computational mechanisms to find efficiently effective rules from very large databases are as follows : Efficient mining from large databases by query learning ; A modification of AdaBoost for adaptive sampling methods ; Tree-based boosting using linear classifier ; The minimax strategy for Gaussian density estimation. Furthermore, algorithms to solve certain concrete problems are developed ; A practical algorithm to find the best subsequence patterns ; Biological sequence compression algorithms - Learning via compression schemes ; Effect of sample size in text categorization ; Knowledge discovery by using both experimental and theoretical methods ; Discovery of commonality among definition sentences by MDL-based compression.

从各个领域收集的数据量呈指数级增长，因此分析数据以提取其背后的有用信息的任务变得越来越困难。为了从数据中提取有用的信息，在提取过程和数据之间必须有某种适当的相互作用。通过互动，各种过程，如记忆某些信息，学习，进化，并可能发现知识将被执行。从大量数据中自动提取知识的主要障碍是计算资源的限制。A03组旨在提出和开发知识发现的计算模型和方法。为了达到这个目的，我们探索了各种各样的主题，包括处理异构数据的算法，这些数据可能是强结构化的或结构不良的。在这个项目的结果中，关于从非常大的数据库中有效地发现有效规则的计算机制的结果如下：通过查询学习从大数据库中有效地挖掘; AdaBoost的自适应采样方法的修改;使用线性分类器的基于树的提升;高斯密度估计的最小最大策略。此外，算法来解决某些具体问题的开发;一个实用的算法，以找到最好的子序列模式;生物序列压缩算法-通过压缩方案学习;样本大小在文本分类的影响;知识发现使用实验和理论方法;发现的共性之间的定义句子基于MDL的压缩。