摘要翻译：
计算机的出现和信息技术革命使现实世界发生了巨大的变化，为智能数据分析提供了不同的维度。事实是，信息在正确的时间和地点部署了更好的知识。然而，当大量不一致的数据用于决策和知识提取时，挑战就出现了。为了处理这些不精确的数据，近年来的研究发展了一些更重要的数学工具，即模糊集、直觉模糊集、粗糙集、形式概念分析和排序规则。人们还观察到，许多信息系统都包含数值属性值，因此它们几乎是相似的，而不是完全相似的。针对这类信息系统，本文采用了前置处理和后置处理两种处理方式。在前处理过程中，我们使用带有排序规则的直觉模糊近似空间上的粗糙集来寻找知识；在后处理过程中，我们使用形式概念分析来寻找更好的知识和影响决策的重要因素。
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英文标题：
《A Knowledge Mining Model for Ranking Institutions using Rough Computing
  with Ordering Rules and Formal Concept analysis》
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作者：
D. P. Acharjya, and L. Ezhilarasi
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最新提交年份：
2011
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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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一级分类：Computer Science        计算机科学
二级分类：Information Retrieval        信息检索
分类描述：Covers indexing, dictionaries, retrieval, content and analysis. Roughly includes material in ACM Subject Classes H.3.0, H.3.1, H.3.2, H.3.3, and H.3.4.
涵盖索引，字典，检索，内容和分析。大致包括ACM主题课程H.3.0、H.3.1、H.3.2、H.3.3和H.3.4中的材料。
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英文摘要：
  Emergences of computers and information technological revolution made tremendous changes in the real world and provides a different dimension for the intelligent data analysis. Well formed fact, the information at right time and at right place deploy a better knowledge.However, the challenge arises when larger volume of inconsistent data is given for decision making and knowledge extraction. To handle such imprecise data certain mathematical tools of greater importance has developed by researches in recent past namely fuzzy set, intuitionistic fuzzy set, rough Set, formal concept analysis and ordering rules. It is also observed that many information system contains numerical attribute values and therefore they are almost similar instead of exact similar. To handle such type of information system, in this paper we use two processes such as pre process and post process. In pre process we use rough set on intuitionistic fuzzy approximation space with ordering rules for finding the knowledge whereas in post process we use formal concept analysis to explore better knowledge and vital factors affecting decisions.
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PDF链接：
https://arxiv.org/pdf/1108.1986