A Change Detection Model for the Sequential Cause-and-Effect Relationship

• Main Authors: Jen-Hung Teng, 鄧任宏
• Other Authors: Wei-Yen Hsu
• Format: Others
• Language: zh-TW
• Published: 2015
• Online Access: http://ndltd.ncl.edu.tw/handle/64140342480086832221

碩士 === 國立中正大學 === 資訊管理學系暨研究所 === 103 === Identifying changes of customer behavior or event is an essential issue that must be faced for existing updating knowledge in a dynamic environment. Especially in nowadays, rapidly growth technology lets information collection becoming more and easier. Business can immediately collect numerous transactional data to discover the knowledge which is behind in their customers. However, there is a problem− the knowledge which business uses data mining to be discovered with the data of customers is still suitable? In this study, we discuss a sequence-based classification pattern, which is used to figure out the sequential relation between cause and effect. The sequenced-based classification pattern may occur a situation that this pattern is suitable in the past time but is useless in nowadays. Without updating this knowledge, the manager will make an inappropriate decision. To settle this problem, this study proposes a novel change mining model, called SeqClassChange, to identify the change of patterns. In the experiments, we use a FoodMart database which is stemming from the Microsoft© SQL Sample database. After the preprocessing procedure, we use our SeqClassChange model to get sequenced-based classification patterns, and then clarify the change of patterns. Experimental result shows how does change mining of pattern works; therefore, we believe that our method can help managers to identify the customer behavioral trends and to make a right decision. Keywords: data mining, change mining, sequenced-based classification