SPICE: A New Framework for Data Mining based on Probability Logic and Formal Concept Analysis
SPICE: A New Framework for Data Mining based on Probability Logic and Formal Concept Analysis
复制标题
DOI：
--
复制
发表时间：
2007-12
期刊：
影响因子：
--
通讯作者：
Liying Jiang;J. Deogun
中科院分区：
文献类型：
--
作者：
Liying Jiang;J. Deogun
形式概念分析和概率逻辑是数据分析的两个有用工具。数据通常表示为对象和要素的二维上下文。FCA根据对象和特征之间的关系发现数据中的依赖关系。另一方面，概率逻辑表示和推理数据中的统计概率和命题概率。我们提出了概率推理和概念提取的SPICE-符号集成，为数据挖掘任务提供了一个更灵活、更健壮的框架。在SPICE中，我们形式化数据挖掘的重要概念，如概念和模式，并开发新的概念，如最大潜在有用模式。本文对SPICE中的关联规则挖掘进行了形式化描述，提出了一种改进的关联规则挖掘方法--SPICE关联规则挖掘，以解决一般关联规则挖掘中存在的时间效率低和规则冗余的问题。我们展示了SPICE方法在地理空间决策支持系统中的应用。实验结果表明，SPICE算法能够高效、有效地发现一组简洁的关联规则。
Formal concept analysis and probability logic are two useful tools for data analysis. Data is usually represented as a two-dimensional context of objects and features. FCA discovers dependencies within the data based on the relation among objects and features. On the other hand, the probability logic represents and reasons with both statistical and propositional probability among data. We propose SPICE - Symbolic integration of Probability Inference and Concept Extraction, which provides a more flexible and robust framework for data mining tasks. Within SPICE, we formalize the important notions of data mining, such as concepts and patterns, and develop new notions such as maximal potentially useful patterns. In this paper, we formalize the association rule mining in SPICE and propose an enhanced rule mining approach, called SPICE association rule mining, to solve the problem of time inefficiency and rule redundancy in general association rule mining. We show an application of the SPICE approach in the Geo-spatial Decision Support System (GDSS). The experimental results show that SPICE can efficiently and effectively discover a succinct set of interesting association rules.