{"title":"挖掘电子商务数据分析目标客户行为","authors":"Yuantao Jiang, Siqin Yu","doi":"10.1109/WKDD.2008.90","DOIUrl":null,"url":null,"abstract":"In the advent of the information era, e-commerce has developed rapidly and has become significant for every business. With the advanced information technologies, firms are now able to collect and store mountains of data describing their myriad offerings and diverse customer profiles, from which they seek to derive information about their customers' needs and wants. Traditional forecasting methods are no longer suitable for these business situations. This research used the principles of data mining to cluster customer segments by using k-means algorithm and data from Web log of various e-commerce Websites. Consequently, the results showed that there was a clear distinction between the segments in terms of customer behavior.","PeriodicalId":101656,"journal":{"name":"First International Workshop on Knowledge Discovery and Data Mining (WKDD 2008)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"26","resultStr":"{\"title\":\"Mining E-Commerce Data to Analyze the Target Customer Behavior\",\"authors\":\"Yuantao Jiang, Siqin Yu\",\"doi\":\"10.1109/WKDD.2008.90\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In the advent of the information era, e-commerce has developed rapidly and has become significant for every business. With the advanced information technologies, firms are now able to collect and store mountains of data describing their myriad offerings and diverse customer profiles, from which they seek to derive information about their customers' needs and wants. Traditional forecasting methods are no longer suitable for these business situations. This research used the principles of data mining to cluster customer segments by using k-means algorithm and data from Web log of various e-commerce Websites. Consequently, the results showed that there was a clear distinction between the segments in terms of customer behavior.\",\"PeriodicalId\":101656,\"journal\":{\"name\":\"First International Workshop on Knowledge Discovery and Data Mining (WKDD 2008)\",\"volume\":\"41 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-01-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"26\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"First International Workshop on Knowledge Discovery and Data Mining (WKDD 2008)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/WKDD.2008.90\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"First International Workshop on Knowledge Discovery and Data Mining (WKDD 2008)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WKDD.2008.90","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Mining E-Commerce Data to Analyze the Target Customer Behavior
In the advent of the information era, e-commerce has developed rapidly and has become significant for every business. With the advanced information technologies, firms are now able to collect and store mountains of data describing their myriad offerings and diverse customer profiles, from which they seek to derive information about their customers' needs and wants. Traditional forecasting methods are no longer suitable for these business situations. This research used the principles of data mining to cluster customer segments by using k-means algorithm and data from Web log of various e-commerce Websites. Consequently, the results showed that there was a clear distinction between the segments in terms of customer behavior.