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摘要翻译：
数据挖掘技术以领域专家能够理解的新颖方式提取所需的知识，发现未知的信息，从而进行战略决策，发挥着至关重要的作用。在挖掘过程中考虑非领域专家的作用，提出了一个广义框架，以更好地理解、更好地决策和更好地发现新的模式，从而更好地选择合适的基于用户特征的数据挖掘技术。关键词：数据挖掘技术，智能代理，用户模型，多维可视化，知识发现。
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英文标题：
《A framework: Cluster detection and multidimensional visualization of
automated data mining using intelligent agents》
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作者：
R. Jayabrabu, V. Saravanan, K. Vivekanandan
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最新提交年份：
2012
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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：
Data Mining techniques plays a vital role like extraction of required knowledge, finding unsuspected information to make strategic decision in a novel way which in term understandable by domain experts. A generalized frame work is proposed by considering non - domain experts during mining process for better understanding, making better decision and better finding new patters in case of selecting suitable data mining techniques based on the user profile by means of intelligent agents. KEYWORDS: Data Mining Techniques, Intelligent Agents, User Profile, Multidimensional Visualization, Knowledge Discovery.
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PDF链接：
https://arxiv.org/pdf/1202.1945
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