2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW)最新文献_第10页

A Novel Method for Assessing Event Impacts on Event-Driven Time Series 一种新的事件驱动时间序列事件影响评估方法

2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW) Pub Date : 2016-12-01 DOI: 10.1109/ICDMW.2016.0080

Lianhua Chi, Saket K. Sathe, Bong-Koo Han, Yun Wang

引用次数: 0

Graph Mining for Complex Data Analytics 复杂数据分析的图挖掘

2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW) Pub Date : 2016-12-01 DOI: 10.1109/ICDMW.2016.0193

André Petermann, Martin Junghanns, Stephan Kemper, Kevin Gómez, Niklas Teichmann, E. Rahm

引用次数: 8

Structural Patterns in the Rise of Germany’s New Right on Facebook Facebook上德国新右翼崛起的结构模式

2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW) Pub Date : 2016-12-01 DOI: 10.1109/ICDMW.2016.0069

Sebastian Schelter, F. Biessmann, Malisa Zobel, Nedelina Teneva

{"title":"Structural Patterns in the Rise of Germany’s New Right on Facebook","authors":"Sebastian Schelter, F. Biessmann, Malisa Zobel, Nedelina Teneva","doi":"10.1109/ICDMW.2016.0069","DOIUrl":"https://doi.org/10.1109/ICDMW.2016.0069","url":null,"abstract":"In the last years a new right-wing, populist and eurosceptic party emerged in Germany, the 'Alternative für Deutschland'. Topics that were used by the party to draw attention to their program included the Euro-crisis and the so-called 'refugee crisis'. We investigate some aspects of social media use of the AfD. Our goal is to relate the rise of this party to some quantitative measures of their social media usage. A particular focus will be placed on users that interact with AfD content as well as with content of other parties. Our analysis is based on more than 11 million interactions of more than one million users with the public Facebook pages of the major German political parties during the years 2014 and 2015. Investigating the time courses of social media activity and user interaction, we find that the rise of the AfD can be associated with an amount of social media coverage and user engagement that is unprecedented in the German political landscape. One main effect of this campaign is a substantial increase of user interactions with other parties from the right spectrum, suggesting a migration of voters from established right wing parties. In order to better interpret the dynamics of social media activity, we relate the analysed metrics to the textual content of the posts and classical survey data. These results suggest that the intense use of social media platforms poses a major success factor of this newly emerging right-wing party.","PeriodicalId":373866,"journal":{"name":"2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124556409","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: 10

Towards Data-Driven Football Player Assessment 迈向数据驱动的足球运动员评估

2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW) Pub Date : 2016-12-01 DOI: 10.1109/ICDMW.2016.0031

R. Stanojevic, L. Gyarmati

引用次数: 15

Data Exchange Platform to Fight Insurance Fraud on Blockchain 打击区块链保险欺诈的数据交换平台

2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW) Pub Date : 2016-12-01 DOI: 10.1109/ICDMW.2016.0121

Indrani Nath

引用次数: 46

Regression Techniques for Modelling Conception in Seasonally Calving Dairy Cows 季节性产犊奶牛概念建模的回归技术

2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW) Pub Date : 2016-12-01 DOI: 10.1109/ICDMW.2016.0172

Caroline Fenlon, L. O’Grady, M. Doherty, S. Butler, L. Shalloo, J. Dunnion

{"title":"Regression Techniques for Modelling Conception in Seasonally Calving Dairy Cows","authors":"Caroline Fenlon, L. O’Grady, M. Doherty, S. Butler, L. Shalloo, J. Dunnion","doi":"10.1109/ICDMW.2016.0172","DOIUrl":"https://doi.org/10.1109/ICDMW.2016.0172","url":null,"abstract":"Reproductive performance is important for the economic efficiency of pasture-based dairy farms. In these seasonal calving systems, a concise period of breeding is essential to ensure the alignment of peak grass availability with peak lactating cow energy demands. Trials and statistical analysis have identified the factors affecting overall reproductive performance, but few studies have analysed performance at the individual service level. In this paper, four binary models of service outcome are described, incorporating age, stage of lactation, calving events, and measures of energy balance and milk production. Random effects at the cow, sire and herd level were included. Logistic regression and generalised additive models were created, both as stand-alone predictors and using ensemble learning in the form of bagging. The four models were evaluated in terms of calibration and discrimination using an external dataset of nine dairy herds representing the typical Irish pasture-based system. Logistic regression (with and without bagging) and generalised additive modelling with bagging all performed satisfactorily and would be useful as stand-alone models or in whole-farm simulation. Logistic regression is suggested as the most useful model for farmers and their advisers due to ease of interpretation. This model will be used as part of a PhD project to create simulation software for seasonally calving dairy animals.","PeriodicalId":373866,"journal":{"name":"2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121246241","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: 1

SemStim: Exploiting Knowledge Graphs for Cross-Domain Recommendation SemStim:利用知识图谱进行跨领域推荐

2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW) Pub Date : 2016-12-01 DOI: 10.1109/ICDMW.2016.0145

B. Heitmann, Conor Hayes

引用次数: 9

Identifying Warning Behaviors of Violent Lone Offenders in Written Communication 识别暴力孤独罪犯在书面交流中的警告行为

2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW) Pub Date : 2016-12-01 DOI: 10.1109/ICDMW.2016.0152

Lisa Kaati, A. Shrestha, Tony Sardella

引用次数: 23

Classification Rule Mining Supported by Ontology for Discrimination Discovery 本体支持的分类规则挖掘用于鉴别发现

2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW) Pub Date : 2016-12-01 DOI: 10.1109/ICDMW.2016.0128

B. Luong, S. Ruggieri, F. Turini

引用次数: 3

Selecting the Top-k Discriminative Features Using Principal Component Analysis 利用主成分分析选择Top-k判别特征

2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW) Pub Date : 2016-12-01 DOI: 10.1109/ICDMW.2016.0096

Aminata Kane, Nematollaah Shiri

引用次数: 3