Behavior Action Mining
文献类型：期刊论文
| 作者 | Su, Peng1; Zeng, Daniel2 ; Zhao, Huimin3
|
| 刊名 | IEEE ACCESS
 |
| 出版日期 | 2019 |
| 卷号 | 7页码:19954-19964 |
| 关键词 | Business decision support knowledge and data engineering tools and techniques mining methods and algorithms |
| ISSN号 | 2169-3536 |
| DOI | 10.1109/ACCESS.2019.2896141 |
| 通讯作者 | Su, Peng(peng.su.casia@gmail.com) |
| 英文摘要 | The actionable behavioral rules suggest specific actions that may influence certain behavior in the stakeholders' best interest. In mining such rules, it was assumed previously that all attributes are categorical while the numerical attributes have been discretized in advance. However, this assumption significantly reduces the solution space, and thus hinders the potential of mining algorithms, especially when the numerical attributes are prevalent. As the numerical data are ubiquitous in business applications, there is a crucial need for new mining methodologies that can better leverage such data. To meet this need, in this paper, we define a new data mining problem, named behavior action mining, as a problem of continuous variable optimization of expected utility for action. We then develop three approaches to solving this new problem, which uses regression as a technical basis. The experimental results based on a marketing dataset demonstrate the validity and superiority of our proposed approaches. |
| WOS关键词 | ACTION RULES ; SEARCH ; WEB |
| 资助项目 | MOST[2016QY02D0305] ; NNSFC[71462001] ; NNSFC Innovative Team[71621002] ; CAS Grant[ZDRW-XH-2017-3] |
| WOS研究方向 | Computer Science ; Engineering ; Telecommunications |
| 语种 | 英语 |
| WOS记录号 | WOS:000463168700001 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | MOST ; NNSFC ; NNSFC Innovative Team ; CAS Grant |
| 源URL | [http://ir.ia.ac.cn/handle/173211/28076]  |
| 专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_互联网大数据与安全信息学研究中心 |
| 通讯作者 | Su, Peng |
| 作者单位 | 1.Dali Univ, Sch Math & Comp Sci, Dali 671003, Peoples R China 2.Chinese Acad Sci, Inst Automat, State Key Lab Intelligent Control & Management Co, Beijing 100190, Peoples R China 3.Univ Wisconsin, Sheldon B Lubar Sch Business, Milwaukee, WI 53201 USA |
| 推荐引用方式 GB/T 7714 | Su, Peng,Zeng, Daniel,Zhao, Huimin. Behavior Action Mining[J]. IEEE ACCESS,2019,7:19954-19964. |
| APA | Su, Peng,Zeng, Daniel,&Zhao, Huimin.(2019).Behavior Action Mining.IEEE ACCESS,7,19954-19964. |
| MLA | Su, Peng,et al."Behavior Action Mining".IEEE ACCESS 7(2019):19954-19964. |
入库方式： OAI收割
来源：自动化研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。