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Decision Support System to Determine Promotional Methods and Targets with K-Means Clustering

机译:K-Means聚类确定促销方法和目标的决策支持系统

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Promotion becomes one of the important aspects of institutions of college. The number of competitors demanding the marketing must be fast and accurate in formulating strategies and decision making. Data warehouse and data mining become one of the means to build a decision support system that can provide knowledge and wisdom quickly to be taken into consideration in promotion strategy planning. Development of this system then does the process of testing with the number of data 6171 rows of student enrollment taken directly from a transactional database. The data is done ETL process and clustering with the k-means clustering algorithm, then the data in each cluster is done grouping and summarization to get weighting. After that just done ranking to produce wisdom, one of them determine the list of schools that will be the target roadshow. The analysis also produces several patterns of student enrollment, namely the registrant pattern from the wave of registration and favorite or non-favorite school categories. In addition, the results of system design in this study can be developed easily if requires added external data. Such as data of SMK/SMK school graduates in the area or data of students enrolling in other universities. This is one of the superiority of semantic-based data warehouses.
机译:晋升已成为大学制度的重要方面之一。在制定战略和决策时,要求营销的竞争者数量必须快速准确。数据仓库和数据挖掘成为构建决策支持系统的手段之一,该决策支持系统可以快速提供知识和智慧,这些知识和智慧将在促销策略计划中加以考虑。然后,该系统的开发将使用直接从交易数据库获取的6171行学生入学数据的数量来进行测试过程。对数据进行ETL处理并使用k-means聚类算法进行聚类,然后对每个聚类中的数据进行分组和汇总以获得权重。在完成排名以产生智慧之后,其中之一确定了将成为目标路演的学校的列表。该分析还产生了几种学生入学模式,即注册浪潮中的注册人模式以及喜爱或不喜欢的学校类别。此外,如果需要添加外部数据,可以轻松开发此研究中的系统设计结果。例如该地区SMK / SMK学校毕业生的数据或在其他大学就读的学生的数据。这是基于语义的数据仓库的优势之一。

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