2014 IEEE International Conference on Data Mining Workshop最新文献

A Data Mining Framework to Model Consumer Indebtedness with Psychological Factors 基于心理因素的消费者负债数据挖掘框架

2014 IEEE International Conference on Data Mining Workshop Pub Date : 2014-12-18 DOI: 10.2139/ssrn.2828030

J. Garibaldi, E. Ferguson, U. Aickelin

引用次数: 12

Refining Adverse Drug Reactions Using Association Rule Mining for Electronic Healthcare Data 使用关联规则挖掘电子医疗数据来精炼药物不良反应

2014 IEEE International Conference on Data Mining Workshop Pub Date : 2014-12-14 DOI: 10.1109/ICDMW.2014.53

J. Reps, U. Aickelin, Jiangang Ma, Yanchun Zhang

{"title":"Refining Adverse Drug Reactions Using Association Rule Mining for Electronic Healthcare Data","authors":"J. Reps, U. Aickelin, Jiangang Ma, Yanchun Zhang","doi":"10.1109/ICDMW.2014.53","DOIUrl":"https://doi.org/10.1109/ICDMW.2014.53","url":null,"abstract":"Side effects of prescribed medications are a common occurrence. Electronic healthcare databases present the opportunity to identify new side effects efficiently but currently the methods are limited due to confounding (i.e. When an association between two variables is identified due to them both being associated to a third variable). In this paper we propose a proof of concept method that learns common associations and uses this knowledge to automatically refine side effect signals (i.e. Exposure-outcome associations) by removing instances of the exposure-outcome associations that are caused by confounding. This leaves the signal instances that are most likely to correspond to true side effect occurrences. We then calculate a novel measure termed the confounding-adjusted risk value, a more accurate absolute risk value of a patient experiencing the outcome within 60 days of the exposure. Tentative results suggest that the method works. For the four signals (i.e. Exposure-outcome associations) investigated we are able to correctly filter the majority of exposure-outcome instances that were unlikely to correspond to true side effects. The method is likely to improve when tuning the association rule mining parameters for specific health outcomes. This paper shows that it may be possible to filter signals at a patient level based on association rules learned from considering patients' medical histories. However, additional work is required to develop a way to automate the tuning of the method's parameters.","PeriodicalId":289269,"journal":{"name":"2014 IEEE International Conference on Data Mining Workshop","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125940604","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}

引用次数: 15

Personalising Mobile Advertising Based on Users' Installed Apps 基于用户安装应用的个性化移动广告

2014 IEEE International Conference on Data Mining Workshop Pub Date : 2014-12-14 DOI: 10.1109/ICDMW.2014.90

J. Reps, U. Aickelin, J. Garibaldi, Christopher H. Damski

{"title":"Personalising Mobile Advertising Based on Users' Installed Apps","authors":"J. Reps, U. Aickelin, J. Garibaldi, Christopher H. Damski","doi":"10.1109/ICDMW.2014.90","DOIUrl":"https://doi.org/10.1109/ICDMW.2014.90","url":null,"abstract":"Mobile advertising is a billion pound industry that is rapidly expanding. The success of an advert is measured based on how users interact with it. In this paper we investigate whether the application of unsupervised learning and association rule mining could be used to enable personalised targeting of mobile adverts with the aim of increasing the interaction rate. Over May and June 2014 we recorded advert interactions such as tapping the advert or watching the whole advert video along with the set of apps a user has installed at the time of the interaction. Based on the apps that the users have installed we applied k-means clustering to profile the users into one of ten classes. Due to the large number of apps considered we implemented dimension reduction to reduced the app feature space by mapping the apps to their iTunes category and clustered users based on the percentage of their apps that correspond to each iTunes app category. The clustering was externally validated by investigating differences between the way the ten profiles interact with the various adverts genres (lifestyle, finance and entertainment adverts). In addition association rule mining was performed to find whether the time of the day that the advert is served and the number of apps a user has installed makes certain profiles more likely to interact with the advert genres. The results showed there were clear differences in the way the profiles interact with the different advert genres and the results of this paper suggest that mobile advert targeting would improve the frequency that users interact with an advert.","PeriodicalId":289269,"journal":{"name":"2014 IEEE International Conference on Data Mining Workshop","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130241987","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}

引用次数: 3

Incorporating Spontaneous Reporting System Data to Aid Causal Inference in Longitudinal Healthcare Data 结合自发报告系统数据，以协助纵向医疗保健数据的因果推理

2014 IEEE International Conference on Data Mining Workshop Pub Date : 2014-12-14 DOI: 10.1109/ICDMW.2014.54

J. Reps, U. Aickelin

{"title":"Incorporating Spontaneous Reporting System Data to Aid Causal Inference in Longitudinal Healthcare Data","authors":"J. Reps, U. Aickelin","doi":"10.1109/ICDMW.2014.54","DOIUrl":"https://doi.org/10.1109/ICDMW.2014.54","url":null,"abstract":"Inferring causality using longitudinal observational databases is challenging due to the passive way the data are collected. The majority of associations found within longitudinal observational data are often non-causal and occur due to confounding. The focus of this paper is to investigate incorporating information from additional databases to complement the longitudinal observational database analysis. We investigate the detection of prescription drug side effects as this is an example of a causal relationship. In previous work a framework was proposed for detecting side effects only using longitudinal data. In this paper we combine a measure of association derived from mining a spontaneous reporting system database to previously proposed analysis that extracts domain expertise features for causal analysis of a UK general practice longitudinal database. The results show that there is a significant improvement to the performance of detecting prescription drug side effects when the longitudinal observation data analysis is complemented by incorporating additional drug safety sources into the framework. The area under the receiver operating characteristic curve (AUC) for correctly classifying a side effect when other data were considered was 0.967, whereas without it the AUC was 0.923 However, the results of this paper may be biased by the evaluation and future work should overcome this by developing an unbiased reference set.","PeriodicalId":289269,"journal":{"name":"2014 IEEE International Conference on Data Mining Workshop","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114534628","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}

引用次数: 2

e2-Diagnoser: A System for Monitoring, Forecasting and Diagnosing Energy Usage e2-Diagnoser:一个监测、预测和诊断能源使用的系统

2014 IEEE International Conference on Data Mining Workshop Pub Date : 2014-12-01 DOI: 10.1109/ICDMW.2014.56

J. Ploennigs, Bei Chen, Paulito Palmes, Raymond Lloyd

引用次数: 13

Email Analytics for Support Center Performance Analysis 电子邮件分析支持中心的性能分析

2014 IEEE International Conference on Data Mining Workshop Pub Date : 2014-12-01 DOI: 10.1109/ICDMW.2014.74

Kunal Ranjan, Lipika Dey

引用次数: 5

A Narrative Analysis by Text Mining Technique Using Key Graph: Similarity and Difference of a View of Oral Health and Oral Risk Cognition between Japanese Living People and Dentists 基于关键图的文本挖掘叙事分析:日本在世人群与牙医口腔健康观和口腔风险认知的异同

2014 IEEE International Conference on Data Mining Workshop Pub Date : 2014-12-01 DOI: 10.1109/ICDMW.2014.86

Fukiko Kobayashi, Y. Nara

{"title":"A Narrative Analysis by Text Mining Technique Using Key Graph: Similarity and Difference of a View of Oral Health and Oral Risk Cognition between Japanese Living People and Dentists","authors":"Fukiko Kobayashi, Y. Nara","doi":"10.1109/ICDMW.2014.86","DOIUrl":"https://doi.org/10.1109/ICDMW.2014.86","url":null,"abstract":"The main stakeholders in oral health management process are living people (seikatsu-sya) and dentists, the mutual awareness and understanding of them are supposed to be the indispensable basements to improve the quality of oral health treatments for both of living people and dentists. This study aims to examine the similarity and difference of a view of oral health and oral risk cognition between the main stakeholders with interview data in order to obtain a clue for generating their mutual awareness and understanding. Focusing on the method of narrative analysis, the authors discuss on the specifically process, the result, and the significance in this study. It was conducted interviews with five living people and five dentists analyzed their narratives by Key Graph which is a text mining tool. And then we arranged key concepts by KJ method, or an Affinity Diagram, which is a brainstorming tool which organizes a large number of ideas into their natural relationships. Through this analysis, the authors arranged the similarity and difference between Japanese living people's view of oral health and oral risk cognition, and those of dentists in a table. It is observed from the table that oral risk cognitions between Japanese living people and dentists have some difference, on the other side Japanese living people's super ordinate view of oral health and that of dentists are almost the same. This study revealed that it is desirable to apply the collaboration of Key Graph and KJ Method, such as the mechanical processing by the text-mining and the brainstorming.","PeriodicalId":289269,"journal":{"name":"2014 IEEE International Conference on Data Mining Workshop","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125894274","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}

引用次数: 2

Parallel Frequent Pattern Mining without Candidate Generation on GPUs gpu上无候选生成的并行频繁模式挖掘

2014 IEEE International Conference on Data Mining Workshop Pub Date : 2014-12-01 DOI: 10.1109/ICDMW.2014.71

Fei Wang, Bo Yuan

引用次数: 8

A Standard Bibliography Recommended Method Based on Topic Model and Fusion of Multi-feature 基于主题模型和多特征融合的标准书目推荐方法

2014 IEEE International Conference on Data Mining Workshop Pub Date : 2014-12-01 DOI: 10.1109/ICDMW.2014.133

F. Shao, Yantuan Xian, Jianyi Guo, Zhengtao Yu, Cunli Mao

引用次数: 1

Eventera: Real-Time Event Recommendation System from Massive Heterogeneous Online Media Eventera:海量异构在线媒体实时事件推荐系统

2014 IEEE International Conference on Data Mining Workshop Pub Date : 2014-12-01 DOI: 10.1109/ICDMW.2014.32

Dongyeop Kang, Donggyun Han, Nahea Park, Sangtae Kim, U. Kang, Soobin Lee

引用次数: 9