{"title":"结合自组织图和聚类分析的客户细分和可视化","authors":"Yohji Kameoka, Keita Yagi, Shohei Munakata, Yoshiro Yamamoto","doi":"10.1109/ICTKE.2015.7368465","DOIUrl":null,"url":null,"abstract":"To perceive the characteristics of customers from market information, it is necessary to aggregate the market information. So, we used a self-organizing map (SOM), arecency, frequency and monetary (RFM) analysis, and other methods to propose the classification of customers. In addition, we propose a visualization of the analysis results. In this study, we consider the combination of cluster analysis and the SOM for further consolidation of small clusters and visualization.","PeriodicalId":128925,"journal":{"name":"2015 13th International Conference on ICT and Knowledge Engineering (ICT & Knowledge Engineering 2015)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Customer segmentation and visualization by combination of self-organizing map and cluster analysis\",\"authors\":\"Yohji Kameoka, Keita Yagi, Shohei Munakata, Yoshiro Yamamoto\",\"doi\":\"10.1109/ICTKE.2015.7368465\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"To perceive the characteristics of customers from market information, it is necessary to aggregate the market information. So, we used a self-organizing map (SOM), arecency, frequency and monetary (RFM) analysis, and other methods to propose the classification of customers. In addition, we propose a visualization of the analysis results. In this study, we consider the combination of cluster analysis and the SOM for further consolidation of small clusters and visualization.\",\"PeriodicalId\":128925,\"journal\":{\"name\":\"2015 13th International Conference on ICT and Knowledge Engineering (ICT & Knowledge Engineering 2015)\",\"volume\":\"53 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 13th International Conference on ICT and Knowledge Engineering (ICT & Knowledge Engineering 2015)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICTKE.2015.7368465\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 13th International Conference on ICT and Knowledge Engineering (ICT & Knowledge Engineering 2015)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICTKE.2015.7368465","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Customer segmentation and visualization by combination of self-organizing map and cluster analysis
To perceive the characteristics of customers from market information, it is necessary to aggregate the market information. So, we used a self-organizing map (SOM), arecency, frequency and monetary (RFM) analysis, and other methods to propose the classification of customers. In addition, we propose a visualization of the analysis results. In this study, we consider the combination of cluster analysis and the SOM for further consolidation of small clusters and visualization.
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