Improving aspect extraction by augmenting a frequency-based method with web-based similarity measures

Online review mining has been used to help manufacturers and service providers improve their products and services, and to provide valuable support for consumer decision making. Product aspect extraction is fundamental to online review mining. This research is aimed to improve the performance of aspect extraction from online consumer reviews. To this end, we augment a frequency-based extraction method with PMI-IR, which utilizes web search in measuring the semantic similarity between aspect candidates and target entities. In addition, we extend RCut, an algorithm originally developed for text classification, to learn the threshold for selecting candidate aspects. Experiment results with Chinese online reviews show that our proposed method not only outperforms the state of the art frequency-based method for aspect extraction but also generalizes across different product domains and various data sizes.     

从诗文酬唱看苏轼与黄庭坚的非凡友谊

苏轼是我国北宋中期文坛上的领袖,黄庭坚为苏门学士并开一代诗派。师徒之间诗文酬唱频繁,友谊非凡。从他们大量的酬唱诗文来看,二人之间的友谊体现在他们生活志趣以及人格理想上的相知相通,诗歌艺术上的交流切磋、争奇斗巧和宦海沉浮,人生遭遇中的勖勉与慰藉等方面。     

An efficient algorithm for mining closed itemsets

INTRODUCTIONMiningfrequentitemsetsisafundamentalandessentialprobleminmanydataminingapplica tionsincludingthediscoveryofassociationrules,strongrules,correlations,sequentialrules,epi sodes,multi dimensionalpatterns,andmanyoth erimportantdiscoverytasks (AgarwalandSri kant,1994;Wangetal.,2 0 0 2 ) .Mostalgo rithmsproposedsofarworkwellondatasetswherethesizesofitemsetsarerelativelysmall.Howev er,theyusuallycrashwithdensedatasetswheretheitemsetsizesarelarge.Suchdatasetsincludethosecomposedofque…     

Students’ perception of frequent assessments and its relation to motivation and grades in a statistics course: a pilot study

This pilot study measures university students’ perceptions of graded frequent assessments in an obligatory statistics course using a novel questionnaire. Relations between perceptions of frequent assessments, intrinsic motivation and grades were also investigated. A factor analysis of the questionnaire revealed four factors, which were labelled value, formative function, positive effects and negative effects. The results showed that most students valued graded frequent assessments as a study motivator. A modest number of students experienced positive or negative effects from assessments and grades received. Less than half of the students used the results of frequent assessments in their learning process. The perception of negative effects (lower self-confidence and more stress) negatively mediated the relation between grades and intrinsic motivation. It is argued that communication with students regarding the purpose and benefits of frequent assessments could mitigate these negative effects.     

关联规则的增量更新算法研究

关联规则是数据挖掘领域的一个重要分支。随着数据库的不断变化,关联规则的增量更新变得尤为重要。为了更好地对关联规则进行有效的更新,对已经提出的经典的关联规则更新算法FUP和IUA算法进行分析,指出其优缺点;提出了一个改进的关联规则算法PFUP。该算法减少了候选项集数目,从而减少扫描数据库D的次数,提高了效率。     

数据挖掘在数据分析中的应用

本文针对运用数据挖掘方法构建规则库的过程进行阐述,对选取的算法进行介绍及确定其中的可变量,并着重解决方法运用过程中面临的若干问题,其中包括审计数据的收集、格式转换、规则集合并、规则匹配等。通过对挖掘结果的分析可以看出,将数据挖掘技术应用到数据分析中,大大提高了入侵检测系统的可靠性和智能化程度。     

数据挖掘中的关联规则方法

介绍了关联规则的基本概念,总结了关联规则的分类及各种挖掘算法,并对一些典型算法进行了介绍,最后展望了关联规则挖掘的下一步研究方向。     

A novel algorithm for frequent itemset mining in data warehouses

INTRODUCTION A data warehouse (Inmon, 1996) is an integrated and time-varying database primarily used for the support of decision-making, and integrates volumi- nous data from multiple and independent data sources consisting of operational databases in a common repository for querying and analysis. In terms of data modeling, a data warehouse consists of one or several dimensional models that are composed of a central fact table and a set of surrounding dimension tables each corresponding t…     

基于现有数据挖掘结果的关联规则更新算法

Apriori算法是整个关联规则算法的基础.它必须对数据库进行重复性扫描.效率低,耗时巨大.针对关联规则的维护问题,提出利用已生成关联规则的挖掘结果进行更新的算法,仅需对数据库进行两次扫描,提高了关联规则的更新效率.     

基于事务压缩的Apriori算法的改进

在数据挖掘算法中，Apriori算法是关联规则的经典算法。文章在分析经典Apriori算法的基础上，提出了改进型的Apriori算法。改进后的算法对事务数据库进行两次压缩，减少事务数据库的平均长度，提高了数据挖掘的效率，具有一定的实用性。