Pub Date : 2010-08-07DOI: 10.3233/978-1-60750-633-1-66
T. Bruckhaus, William E. Guthrie
A central need in the emerging business of model-based prediction is to enable customers to validate the accuracy of a predictive product. This paper discusses how analysts can evaluate data mining models and their inferences from the customer viewpoint, where the customer is not particularly knowledgeable in data mining. To date, academia has focused primarily on the validation of algorithms through mathematical metrics and benchmarking studies. This type of validation is not sufficient in the business context, where organizations must validate specific models in terms that customers can understand quickly and effortlessly. We describe our predictive business and our customer validation needs. To that end, we discuss examples of customer needs, review issues associated with model validation, and point out how academic research may help to address these business needs.
{"title":"Customer Validation of Commercial Predictive Models","authors":"T. Bruckhaus, William E. Guthrie","doi":"10.3233/978-1-60750-633-1-66","DOIUrl":"https://doi.org/10.3233/978-1-60750-633-1-66","url":null,"abstract":"A central need in the emerging business of model-based prediction is to enable customers to validate the accuracy of a predictive product. This paper discusses how analysts can evaluate data mining models and their inferences from the customer viewpoint, where the customer is not particularly knowledgeable in data mining. To date, academia has focused primarily on the validation of algorithms through mathematical metrics and benchmarking studies. This type of validation is not sufficient in the business context, where organizations must validate specific models in terms that customers can understand quickly and effortlessly. We describe our predictive business and our customer validation needs. To that end, we discuss examples of customer needs, review issues associated with model validation, and point out how academic research may help to address these business needs.","PeriodicalId":438467,"journal":{"name":"Data Mining for Business Applications","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131891095","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2010-08-07DOI: 10.3233/978-1-60750-633-1-164
Christine Kopp, D. Hecker, Maike Krause-Traudes, M. May, S. Scheider, Daniel Schulz, Hendrik Stange, S. Wrobel
Almost any data can be referenced in geographic space. Such data permit advanced analyses that utilize the position and relationships of objects in space as well as geographic background information. Even though spatial data mining is still a young research discipline, in the past years research advances have shown that the particular challenges of spatial data can be mastered and that the technology is ready for practical application when spatial aspects are treated as an integrated part of data mining and model building. In this chapter in particular, we give a detailed description of several customer projects that we have carried out and which all involve customized data mining solutions for business relevant tasks. The applications range from customer segmentation to the prediction of traffic frequencies and the analysis of GPS trajectories. They have been selected to demonstrate key challenges, to provide advanced solutions and to arouse further research questions.
{"title":"Spatial Data Mining in Practice: Principles and Case Studies","authors":"Christine Kopp, D. Hecker, Maike Krause-Traudes, M. May, S. Scheider, Daniel Schulz, Hendrik Stange, S. Wrobel","doi":"10.3233/978-1-60750-633-1-164","DOIUrl":"https://doi.org/10.3233/978-1-60750-633-1-164","url":null,"abstract":"Almost any data can be referenced in geographic space. Such data permit advanced analyses that utilize the position and relationships of objects in space as well as geographic background information. Even though spatial data mining is still a young research discipline, in the past years research advances have shown that the particular challenges of spatial data can be mastered and that the technology is ready for practical application when spatial aspects are treated as an integrated part of data mining and model building. In this chapter in particular, we give a detailed description of several customer projects that we have carried out and which all involve customized data mining solutions for business relevant tasks. The applications range from customer segmentation to the prediction of traffic frequencies and the analysis of GPS trajectories. They have been selected to demonstrate key challenges, to provide advanced solutions and to arouse further research questions.","PeriodicalId":438467,"journal":{"name":"Data Mining for Business Applications","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134339364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2010-08-07DOI: 10.3233/978-1-60750-633-1-17
A. Blumenstock, Markus Mueller, Carsten Lanquillon, S. Kempe, Jochen Hipp, R. Wirth
After nearly two decades of data mining research there are many commercial mining tools available, and a wide range of algorithms can be found in literature. One might think there is a solution to most of the problems practitioners face. In our application of descriptive induction on warranty data, however, we found a considerable gap between many standard solutions and our practical needs. Confronted with challenging data and requirements such as understandability and support of existing work flows, we tried many things that did not work, ending up in simple solutions that do. We feel that the problems we faced are not so uncommon, and would like to advocate that it is better to focus on simplicity---allowing domain experts to bring in their knowledge---rather than on complex algorithms. Interactivity and simplicity turn out to be key features to success.
{"title":"Interactivity Closes the Gap - Lessons Learned in an Automotive Industry Application","authors":"A. Blumenstock, Markus Mueller, Carsten Lanquillon, S. Kempe, Jochen Hipp, R. Wirth","doi":"10.3233/978-1-60750-633-1-17","DOIUrl":"https://doi.org/10.3233/978-1-60750-633-1-17","url":null,"abstract":"After nearly two decades of data mining research there are many commercial mining tools available, and a wide range of algorithms can be found in literature. One might think there is a solution to most of the problems practitioners face. In our application of descriptive induction on warranty data, however, we found a considerable gap between many standard solutions and our practical needs. Confronted with challenging data and requirements such as understandability and support of existing work flows, we tried many things that did not work, ending up in simple solutions that do. We feel that the problems we faced are not so uncommon, and would like to advocate that it is better to focus on simplicity---allowing domain experts to bring in their knowledge---rather than on complex algorithms. Interactivity and simplicity turn out to be key features to success.","PeriodicalId":438467,"journal":{"name":"Data Mining for Business Applications","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121585627","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：