摘要翻译：
本文讨论了动态数据挖掘提出的两个主要挑战：1）稳定性挑战：我们实现了一个严格的基于密度增量的聚类算法，与任何初始条件和数据向量流的顺序无关；2）认知性挑战：我们在t-1和t时刻实现了一个严格的簇间关联规则选择过程，以直接产生关于数据流动力学的主要结论，并以一个两年2600篇文献的科学信息数据库为例说明了这一点。
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英文标题：
《Document stream clustering: experimenting an incremental algorithm and
  AR-based tools for highlighting dynamic trends》
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作者：
Alain Lelu (LASELDI), Martine Cadot, Pascal Cuxac (INIST)
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最新提交年份：
2008
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Artificial Intelligence        人工智能
分类描述：Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
涵盖了人工智能的所有领域，除了视觉、机器人、机器学习、多智能体系统以及计算和语言（自然语言处理），这些领域有独立的学科领域。特别地，包括专家系统，定理证明（尽管这可能与计算机科学中的逻辑重叠），知识表示，规划，和人工智能中的不确定性。大致包括ACM学科类I.2.0、I.2.1、I.2.3、I.2.4、I.2.8和I.2.11中的材料。
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英文摘要：
  We address here two major challenges presented by dynamic data mining: 1) the stability challenge: we have implemented a rigorous incremental density-based clustering algorithm, independent from any initial conditions and ordering of the data-vectors stream, 2) the cognitive challenge: we have implemented a stringent selection process of association rules between clusters at time t-1 and time t for directly generating the main conclusions about the dynamics of a data-stream. We illustrate these points with an application to a two years and 2600 documents scientific information database.
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PDF链接：
https://arxiv.org/pdf/0811.0340