Active Mining
Active Mining
复制标题
活跃挖矿
DOI：
10.1007/s00354-007-0011-y
复制
发表时间：
2007
影响因子：
2.6
通讯作者：
T. Ho
中科院分区：
文献类型：
--
作者：
M. Numao;T. Ho
主动挖矿研讨会于 2004 年 12 月 4 日至 6 日在越南河内河内科技大学举行。这是第四次专注于主动挖矿的研讨会；第一次会议于 2002 年 12 月 9 日举行，作为第二届 IEEE 国际数据挖掘会议 (ICDM’02) 的一部分，第二次会议于 2003 年 10 月 28 日举行，作为第 14 届智能系统方法国际研讨会的一部分，均在日本前桥市的前桥寺寺举行，第三次会议于 2004 年 6 月 1 日在石川公生举行日本金泽市年金会馆，作为日本人工智能学会第十八届年会 (JSAI-2004) 的一部分。多场关于 Active Mining 的 SIG 会议也在韩国和日本举行。主动挖掘是现实世界应用程序处理各种用户实际需求数据的知识发现过程的新方向。我们收集数据的能力正在以惊人的速度增长，我们称之为信息洪水。然而，我们分析和理解海量数据的能力远远落后于我们收集数据的能力。数据的价值不再在于我们拥有多少数据。相反，价值在于如何快速有效地减少、探索、操纵和管理数据。为此，数据库中的知识发现 (KDD) 作为一种从数据中提取隐式的、先前未知的且可能有用的信息（或模式）的技术而出现。然而，最近的广泛研究和现实应用表明，克服信息洪流必须满足以下要求：1）从巨大的信息搜索空间中识别和收集相关数据（主动信息收集），2）高效有效地从不同形式的海量数据中挖掘有用知识（以用户为中心的主动数据挖掘），3）及时对情况变化做出反应，并对数据收集和挖掘步骤提供必要的反馈（主动用户）
The workshop on Active Mining was held on December 4-6, 2004 at Hanoi University of Technology, Hanoi, Vietnam. This is the fourth workshop that focuses on Active Mining; the first one was held on December 9, 2002, as a part of the Second IEEE International Conference on Data Mining (ICDM’02), the second one was held on October 28, 2003 as a part of the 14th International Symposium on Methodologies for Intelligent Systems, both at Maebashi TERRASA, Maebashi City, Japan, and the third one was held on June 1, 2004 at Ishikawa Kousei Nenkin Kaikan in Kanazawa City, Japan, as a part of the Eighteenth Annual Conference of the Japanese Society for Artificial Intelligence (JSAI-2004). Several SIG meetings on Active Mining have also been held in Korea and Japan. Active mining is a new direction in the knowledge discovery process for real-world applications handling various kinds of data with actual user need. Our ability to collect data has been increasing at a dramatic rate, which we call information flood. However, our ability to analyze and understand massive data lags far behind our ability to collect them. The value of data is no longer in how much of it we have. Rather, the value is in how quickly and effectively can the data be reduced, explored, manipulated and managed. For this purpose, Knowledge Discovery in Databases (KDD) emerges as a technique that extracts implicit, previously unknown, and potentially useful information (or patterns) from data. However, recent extensive studies and realworld applications show that the following requirements are indispensable to overcome the information flood: 1) identifying and collecting relevant data from a huge information search space (active information collection), 2) mining useful knowledge from different forms of massive data efficiently and effectively (usercentered active data mining), and 3) promptly reacting to situation changes and giving necessary feedback to both data collection and mining steps (active user