75
个编辑
更改
跳到导航
跳到搜索
第15行：
第15行： − 知识的发现，是指不断地识别、分析和可视化数据中的内在模式，这是一个持续迭代和交互的过程。网络分析、链路分析和社会网络分析都是知识发现的方法，每一种都是先验方法的一个子集。大多数知识发现方法遵循以下几个步骤(在最高级别) :+ 第49行：
第49行： − 在得到数据后，需进行数据的收集和处理，但此过程存在一些固有的问题，包括信息超载和数据错误等。在数据被收集后，它将转换成一种人和计算机分析程序都能有效使用的格式。之后基于数据，人工或计算机生成的可视化工具可以进行作图，如网络图。目前有几种算法可以帮助进行数据分析-Dijkstra算法，广度优先搜索和深度优先搜索。+ 第77行：
第77行： − + 第139行：
第139行： − 链接分析主要用于三个目的:+ 第153行：
第153行： − 发现与已知模式不符合的异常情况;+ 第169行：
第169行： − Klerks把链接分析工具分为三代。第一代是由哈珀和哈里斯在1975年引入的，阿纳卡帕图。这种方法需要一个领域内的专家来审查数据文件，通过构造一个关联矩阵来识别关联，然后创建一个可视化的链路图，最后通过分析网络图来识别有意义的模式。这种方法需要广泛的领域知识，但因为要人工审查大量数据，所以非常耗时。+ 第177行：
第177行： − 除了关联矩阵外，活动矩阵还可用于生成可操作的信息，对执法具有实用价值和使用价值。活动矩阵，正如这个术语可能暗示的那样，集中于人们相对于地点的行动和活动。而关联矩阵关注的是人、组织和 / 或属性之间的关系。这两类矩阵之间的区别虽然很小，但就已完成或提供的分析的输出而言却很重要。+
无编辑摘要
Knowledge discovery is an iterative and interactive process used to identify, analyze and visualize patterns in data. Network analysis, link analysis and social network analysis are all methods of knowledge discovery, each a corresponding subset of the prior method. Most knowledge discovery methods follow these steps (at the highest level):
Knowledge discovery is an iterative and interactive process used to identify, analyze and visualize patterns in data. Network analysis, link analysis and social network analysis are all methods of knowledge discovery, each a corresponding subset of the prior method. Most knowledge discovery methods follow these steps (at the highest level):
知识的发现，是指不断地识别、分析和可视化数据中的内在模式，这是一个持续迭代和交互的过程。网络分析、链路分析和社会网络分析都是知识发现的方法，但它们都是属于先验方法。大多数知识发现方法遵循以下几个步骤(在最高级别) :
Data gathering and processing requires access to data and has several inherent issues, including information overload and data errors. Once data is collected, it will need to be transformed into a format that can be effectively used by both human and computer analyzers. Manual or computer-generated visualizations tools may be mapped from the data, including network charts. Several algorithms exist to help with analysis of data – Dijkstra’s algorithm, breadth-first search, and depth-first search.
Data gathering and processing requires access to data and has several inherent issues, including information overload and data errors. Once data is collected, it will need to be transformed into a format that can be effectively used by both human and computer analyzers. Manual or computer-generated visualizations tools may be mapped from the data, including network charts. Several algorithms exist to help with analysis of data – Dijkstra’s algorithm, breadth-first search, and depth-first search.
在得到数据后，需进行数据的收集和处理，但此过程存在一些固有的问题，包括信息超载和数据错误等。在数据被收集后，它将转换成一种人和计算机分析程序都能有效使用的格式。之后基于数据，计算机生成的或人工操作的可视化工具进行如网络图这样的作图。目前有几种算法可以帮助人类进行数据分析-Dijkstra算法，广度优先搜索和深度优先搜索。
! Relationship/Network !! Data Sources
! Relationship/Network !! Data Sources
!人际关系 / 网络! ！数据来源
!关系 / 网络! ！数据来源
|-
|-
Link analysis is used for 3 primary purposes:
Link analysis is used for 3 primary purposes:
链接分析主要有3个作用:
Find anomalies where known patterns are violated;
Find anomalies where known patterns are violated;
发现与已知模式不符合的异常数据;
# Discover new patterns of interest (social network analysis, [[data mining]]).
# Discover new patterns of interest (social network analysis, [[data mining]]).
Klerks categorized link analysis tools into 3 generations. The first generation was introduced in 1975 as the Anacpapa Chart of Harper and Harris. This method requires that a domain expert review data files, identify associations by constructing an association matrix, create a link chart for visualization and finally analyze the network chart to identify patterns of interest. This method requires extensive domain knowledge and is extremely time-consuming when reviewing vast amounts of data.
Klerks categorized link analysis tools into 3 generations. The first generation was introduced in 1975 as the Anacpapa Chart of Harper and Harris. This method requires that a domain expert review data files, identify associations by constructing an association matrix, create a link chart for visualization and finally analyze the network chart to identify patterns of interest. This method requires extensive domain knowledge and is extremely time-consuming when reviewing vast amounts of data.
Klerks把链接分析工具分为三代。第一代是由哈珀和哈里斯在1975年引入的，阿纳卡帕图。这种方法需要一个领域内的专家来审查数据文件，通过构造一个关联矩阵来识别关联，然后创建一个可视化的链路图，最后通过分析网络图来识别有意义的模式。这种方法需要广泛的领域知识，且因要人工审查大量数据，所以非常耗时。
In addition to the association matrix, the activities matrix can be used to produce actionable information, which has practical value and use to law-enforcement. The activities matrix, as the term might imply, centers on the actions and activities of people with respect to locations. Whereas the association matrix focuses on the relationships between people, organizations, and/or properties. The distinction between these two types of matrices, while minor, is nonetheless significant in terms of the output of the analysis completed or rendered.
In addition to the association matrix, the activities matrix can be used to produce actionable information, which has practical value and use to law-enforcement. The activities matrix, as the term might imply, centers on the actions and activities of people with respect to locations. Whereas the association matrix focuses on the relationships between people, organizations, and/or properties. The distinction between these two types of matrices, while minor, is nonetheless significant in terms of the output of the analysis completed or rendered.
除了关联矩阵外，活动矩阵也可用于生成对执法具有实用和使用价值的可操作的信息。活动矩阵，正如这个术语可能暗示的那样，关注的是人们基于地点的行动和活动。而关联矩阵关注的是人，组织和/或属性之间的关系。这两类矩阵之间的区别虽然很小，但就已完成或提供的分析的输出而言，区别还是很重要的。