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首页> 外文会议>International Symposium on Knowledge and Systems Sciences(KSS2004); 20041110-12; Ishikawa(JP) >Flow Graphs - a New Paradigm for Data Mining and Knowledge Discovery
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Flow Graphs - a New Paradigm for Data Mining and Knowledge Discovery

机译：流程图-数据挖掘和知识发现的新范例

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摘要

In this paper we propose a new approach to data (mining) and knowledge discovery based on information flow distribution study in a flow graph. Flow graphs introduced in this paper are different from those proposed by Ford and Fulkerson for optimal flow analysis and they model rather, e.g., flow distribution in a network, than the optimal flow. The flow graphs considered in this paper are not meant to physical media (e.g. water) flow analysis, but to information flow examination in decision algorithms. It is revealed that flow in the flow graph is governed by Bayes' rule, but the rule has entirely deterministic interpretation, not referring to its probabilistic roots. Besides, decision algorithm induced by the flow graph and dependency between conditions and decisions of decision rules are defined and studied. This idea is based on statistical concept of dependency but in our setting it has deterministic meaning.
机译：在本文中，我们基于流程图中的信息流分布研究，提出了一种新的数据（挖掘）和知识发现方法。本文介绍的流程图与Ford和Fulkerson提出的用于最佳流量分析的流程图不同，它们是对网络中的流量分配进行建模而不是对最佳流量进行建模。本文中考虑的流程图并不旨在用于物理介质（例如水）流量分析，而是用于决策算法中的信息流检查。揭示了流程图中的流受贝叶斯规则支配，但是该规则具有完全确定性的解释，而不是指其概率根。此外，定义并研究了由流程图引起的决策算法以及决策条件与决策规则之间的依赖关系。这个想法基于依赖性的统计概念，但是在我们的背景下它具有确定性的含义。

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