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Interpreting the web-mining results by cognitive map and association rule approach

机译:用认知图和关联规则法解释网络挖掘结果

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A variety of the web-mining techniques are now being extensively utilized to extract useful knowledge about customer behaviors on the Internet. However, the naive interpretation of the web-mining results would lead to poor decision on customer behaviors, which is likely to cause waste of time and efforts on managing electronic commerce strategy. To overcome this pitfall, this study proposes using the cognitive map-based interpretation of the web-mining results. Conventional approach to obtaining the web-mining results is based on the association rule approach (ARA), while the cognitive map approach (CMA) is believed to provide more robust support in interpreting the web-mining results. Therefore, to compare the interpretation capability of the two approaches, the four constructs such as perceived usefulness, causality, information richness, users' attitude and intention to use the approaches are adopted in the research model and tested against the questionnaire data. The test results obtained through applying the structural equation models reveal that CMA is comparable to ARA and the cognitive map has a great potential in helping enrich the interpretation of the web mining results and build more effective Internet business strategy.
机译:现在,广泛使用各种Web挖掘技术来提取有关Internet上客户行为的有用知识。但是,对网络挖掘结果的天真解释会导致对客户行为的错误决策,这可能会浪费时间和精力来管理电子商务战略。为了克服这个陷阱,本研究提出使用基于认知图的网络挖掘结果解释。获取网络挖掘结果的常规方法是基于关联规则方法(ARA),而认知图方法(CMA)被认为在解释网络挖掘结果方面提供了更强大的支持。因此,为了比较这两种方法的解释能力,在研究模型中采用了感知的有用性,因果关系,信息丰富性,用户的态度和使用这些方法的意图这四种结构,并针对问卷数据进行了测试。通过应用结构方程模型获得的测试结果表明,CMA可与ARA媲美,并且认知图谱在帮助丰富对Web挖掘结果的解释和建立更有效的Internet业务策略方面具有巨大的潜力。

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