interestingness

英

美

网络  趣味性; 有趣的; 兴趣度; 有趣性; 兴趣性

英英释义

noun

• the power of attracting or holding one's attention (because it is unusual or exciting etc.)
  1. they said nothing of great interest
  2. primary colors can add interest to a room
  Synonym:interest

双语例句

• At the same time, the users 'interested changes are tracked dynamically according to the reading actions, and the interesting ontological profile is submitted, then the measure of interestingness is analyzed and calculated.
  再借助精细语义逐句解读其内容，提取用户所关注的信息.根据用户的阅读行为动态了解用户的兴趣变化，建立用户兴趣的本体模型，并分析和定义了用户兴趣度的度量。
• Interestingness Research of Association Rules in Incremental Mining Data
  兴趣度在增量的关联规则挖掘中的研究
• After introduction of some typical Web log preprocessing techniques, it is pointed out that the frame pages in a Web site can reduce the interestingness of the result page groups. Then, a frame-filtering algorithm is proposed to solve this problem.
  在介绍了典型的数据预处理技术的基础上，指出Frame页面降低了挖掘结果的兴趣性，并提出相应的解决方法&Frame页面过滤算法消除其影响。
• A m_d distance measure for evaluating the subject interestingness of data warehouse
  评估数据仓库主题兴趣度的Md距离测度方法
• After adding the subjective factors, it further improves the interestingness of the rules and reduces lots of unwanted or rubbish rules.
  增加主观性因素后能进一步提高规则的有趣性，减少一些无用，垃圾规则。
• The Comparative Study on Interestingness Measures for Mining Association Rules
  关联规则兴趣度度量方法的比较研究
• It uses interestingness threshold to filter rules in order to reduce the number of association rules, which are useless, inappropriate.
  兴趣度模型采用兴趣度参数对规则进行筛选，减少关联规则的个数，去掉一些无用、不合适的规则。
• The problem of discovering association rules consists of four elements: data set, the form of the rule, search algorithm, interestingness measure.
  关联规则发现问题可以归纳为四个要素：数据集、规则形式、搜索方法、兴趣度量。它们分别对应机器学习问题中的数据空间、假设空间、算法、评价标准。
• After analyzing the quantitative association rules and interestingness of association rules which are encountered often in distributed association rule mining, the dissertation proposes the methods of changing the quantitative attributions into bool attributions using FCM and Gene algorithm.
  并且，在分析和研究了分布式关联规则挖掘中常见的数量型关联规则、关联规则的兴趣度问题的基础上提出了数量关联规则的聚类划分方法以及兴趣度过滤方法。
• In order to find the useful association rules, the interestingness of mining association rules is discussed.
  关联规则挖掘中的有趣性问题可从客观和主观两个方面对关联规则的兴趣度进行评测。