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

• An Overview of Measures of Interestingness
  感兴趣度的研究综述
• 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。
• 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。
• 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。
• For objective measure the interestingness of association rules is first defined on the basis of the support and confidence of association rules and the interestingness of association rules is then eval-uated accordingly.
  在关联规则的置信度和支持度基础上对关联规则的有趣性的客观评测增加了约束。
• At the same time, a new objective interestingness function, which can measure the novelty and simplicity of linguistic value association rules, is given to reduce workload of selecting the interesting linguistic value association rules.
  同时，给出一个能够度量语言值关联规则简洁性和新奇性的客观关注程度（兴趣度）的计算函数，用于减少选取关注语言值关联规则的工作量。
• 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
• WWW Personalized Information Retrieval Based on Measures of Interestingness
  基于感兴趣度的WWW个性化信息发现
• 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。
• Mining Optimized Support and Interestingness Quantitative Association Rules
  挖掘支持度和兴趣度最优的数量关联规则