2014 IEEE International Conference on Data Mining Workshop最新文献_第10页

Towards Achieving Diagnostic Consensus in Medical Image Interpretation 在医学图像解读中实现诊断共识

2014 IEEE International Conference on Data Mining Workshop Pub Date : 2014-12-01 DOI: 10.1109/ICDMW.2014.134

Mike Seidel, A. Rasin, J. Furst, D. Raicu

引用次数: 3

Comparing Classifiers in Historical Census Linkage 历史普查联动中的分类器比较

2014 IEEE International Conference on Data Mining Workshop Pub Date : 2014-12-01 DOI: 10.1109/ICDMW.2014.160

Laura Richards, L. Antonie, S. Areibi, G. Grewal, K. Inwood, J. A. Ross

引用次数: 6

Social Preference Ontologies for Enriching User and Item Data in Recommendation Systems 在推荐系统中丰富用户和项目数据的社会偏好本体

2014 IEEE International Conference on Data Mining Workshop Pub Date : 2014-12-01 DOI: 10.1109/ICDMW.2014.76

Christopher Krauss, S. Arbanowski

{"title":"Social Preference Ontologies for Enriching User and Item Data in Recommendation Systems","authors":"Christopher Krauss, S. Arbanowski","doi":"10.1109/ICDMW.2014.76","DOIUrl":"https://doi.org/10.1109/ICDMW.2014.76","url":null,"abstract":"Some of the known issues of recommendation algorithms are a result of the so called \"Cold Start Problem\" that is caused by a lack of sufficient data of users, items or the content, which are essential for the calculation of context-sensitive predictions. Along with this comes the \"Sparsity Problem\" which also exposes the problem of recommendation systems which are being provided with too little information of user feedback such as likes and views. As a consequent collaborative and knowledge-based filtering algorithms are unable of precise prediction which is causing a decline of the customer satisfaction. If beyond that there also is a lack of metadata, the calculation of similarities through content-based filtering algorithms is likely to fail as well. This paper introduces preference ontologies and how they help to reduce these issues by analyzing external data, in terms of texts from social networks and other web sources. Thereby we introduce a self-designed semantic engine, performing sentiment analysis and semantic keyword extraction. These novel ontologies represent the mined information and thus, describe the users interest in automatic analyzed topics and map them to the meta data of items in recommendation engines.","PeriodicalId":289269,"journal":{"name":"2014 IEEE International Conference on Data Mining Workshop","volume":"142 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":"123434986","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: 4

NLMF: NonLinear Matrix Factorization Methods for Top-N Recommender Systems Top-N推荐系统的非线性矩阵分解方法

2014 IEEE International Conference on Data Mining Workshop Pub Date : 2014-12-01 DOI: 10.1109/ICDMW.2014.108

Santosh Kabbur, G. Karypis

{"title":"NLMF: NonLinear Matrix Factorization Methods for Top-N Recommender Systems","authors":"Santosh Kabbur, G. Karypis","doi":"10.1109/ICDMW.2014.108","DOIUrl":"https://doi.org/10.1109/ICDMW.2014.108","url":null,"abstract":"Many existing state-of-the-art top-N recommendation methods model users and items in the same latent space and the recommendation scores are computed via the dot product between those vectors. These methods assume that the user preference is consistent across all the items that he/she has rated. This assumption is not necessarily true, since many users can have multiple personas/interests and their preferences can vary with each such interest. To address this, a recently proposed method modeled the users with multiple interests. In this paper, we build on this approach and model users using a much richer representation. We propose a method which models the user preference as a combination of having global preference and interest-specific preference. The proposed method uses a nonlinear model for predicting the recommendation score, which is used to perform top-N recommendation task. The recommendation score is computed as a sum of the scores from the components representing global preference and interest-specific preference. A comprehensive set of experiments on multiple datasets show that the proposed model outperforms other state-of-the-art methods for top-N recommendation task.","PeriodicalId":289269,"journal":{"name":"2014 IEEE International Conference on Data Mining Workshop","volume":"47 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":"123693814","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: 22

Medical Error Prevention Based on Path Integration System Approach 基于路径集成系统方法的医疗差错预防

2014 IEEE International Conference on Data Mining Workshop Pub Date : 2014-12-01 DOI: 10.1109/ICDMW.2014.119

S. W. Chan, C. Leung, V. Cheng, Jiming Liu

{"title":"Medical Error Prevention Based on Path Integration System Approach","authors":"S. W. Chan, C. Leung, V. Cheng, Jiming Liu","doi":"10.1109/ICDMW.2014.119","DOIUrl":"https://doi.org/10.1109/ICDMW.2014.119","url":null,"abstract":"Recent findings show that medical errors are prevalent and lead to many unnecessary iatrogenic deaths and injuries. Medical error studies using different approaches such as person approach or system approach enable clinicians to have a better understanding and valuable insight into error prevention through employing guidelines, standardized procedures, and devices, etc. A novel approach is proposed, known as the Path Integration System Approach (PISA), based on information technology (IT), in the design of health systems processes to reduce adverse events. Unlike the person approach or the system approach, which basically addresses error-prone procedures and situations by building more error barriers or changing human behaviour, PISA is concerned with re-constructing the medical procedure paths and system operations to lower the association between clinical staff and medical errors, through the judicious deployment of Information and Communication Technology. It is shown that PISA has the potential to achieve medical error reduction in excess of 70%. Examples and guidelines are given to illustrate the application of the approach. The paper proposes an integrated approach which transforms the effective deployment of IT systems to focus on integration and communication between subsystems. Through the adoption of PISA, such integration maybe achieved to the benefit of different groups of stakeholders in medical error prevention.","PeriodicalId":289269,"journal":{"name":"2014 IEEE International Conference on Data Mining Workshop","volume":"2 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":"125063452","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: 0

Creating Essential Hypothesis and Rules in Product Planning -- Introducing Abductive Inference Model 在产品规划中创造基本假设和规则——引入溯因推理模型

2014 IEEE International Conference on Data Mining Workshop Pub Date : 2014-12-01 DOI: 10.1109/ICDMW.2014.63

Jun Nakamura

引用次数: 1

2014 IEEE International Conference on Data Mining Workshop Pub Date : 2014-12-01 DOI: 10.1109/ICDMW.2014.157

Yaqiong Wang, Junjie Wu, Zhiang Wu, Hua Yuan, Xu Zhang

{"title":"Popular Items or Niche Items: Flexible Recommendation Using Cosine Patterns","authors":"Yaqiong Wang, Junjie Wu, Zhiang Wu, Hua Yuan, Xu Zhang","doi":"10.1109/ICDMW.2014.157","DOIUrl":"https://doi.org/10.1109/ICDMW.2014.157","url":null,"abstract":"Recent years have witnessed the explosive growth of recommender systems in various exciting application domains such as electronic commerce, social networking, and location-based services. A great many algorithms have been proposed to improve the accuracy of recommendation, but until recently the long tail problem rising from inadequate recommendation of niche items is recognized as a real challenge to a recommender. This is particularly true for ultra-massive online retailers who usually have tremendous niche goods for sale. In light of this, in this paper, we propose a pattern-based method called CORE for flexible recommendation of both popular and niche items. CORE has two notable features compared with various existing recommenders. First, it is superior to previous pattern-based methods by adopting cosine rather than frequent patterns for recommendation. This helps filter out spurious cross-support patterns harmful to recommendation. Second, compared with some benchmark methods such as SVD and LDA, CORE does well in niche item recommendation given particularly heavy tailed data sets. Indeed, the coupled configuration of the support and cosine measures enables CORE to switch freely between recommending popular and niche items. Experimental results on two benchmark data sets demonstrate the effectiveness of CORE especially in long tail recommendation. To our best knowledge, CORE is among the earliest recommenders designed purposefully for flexible recommendation of both head and tail items.","PeriodicalId":289269,"journal":{"name":"2014 IEEE International Conference on Data Mining Workshop","volume":"16 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":"128337130","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: 6

A Novel Time-Frequency Analysis Approach for Nonstationary Time Series Using Multiresolution Wavelet 基于多分辨率小波的非平稳时间序列时频分析方法

2014 IEEE International Conference on Data Mining Workshop Pub Date : 2014-12-01 DOI: 10.1109/ICDMW.2014.89

Si-Rui Tan, Yang Li, Ke Li

引用次数: 0

Classification of 3D Surface Data Using the Concept of Vertex Unique Labelled Subgraphs 基于顶点唯一标记子图概念的三维曲面数据分类

2014 IEEE International Conference on Data Mining Workshop Pub Date : 2014-12-01 DOI: 10.1109/ICDMW.2014.125

Wen Yu, Frans Coenen, M. Zito, Kwankamon Dittakan

{"title":"Classification of 3D Surface Data Using the Concept of Vertex Unique Labelled Subgraphs","authors":"Wen Yu, Frans Coenen, M. Zito, Kwankamon Dittakan","doi":"10.1109/ICDMW.2014.125","DOIUrl":"https://doi.org/10.1109/ICDMW.2014.125","url":null,"abstract":"An overview is presented on the use of the concept of Vertex Unique Labelled Sub graph (VULS) mining for the use of localised classification of regions in 3D surfaces represented in terms of grid graphs. A VULS is a sub graph within some larger graph G that has a unique (\"one-of\") vertex labelling associated with it. Given a 3D surface represented as a grid graph, we can identify a number of different forms of VULS that may be discovered: (i) all, (ii) minimal, (iii) frequent and (iv) frequent minimal. Algorithms for discovering (mining) these are presented in the paper. The paper also presents the Backward Match Voting (BMV) algorithm for predicting (classifying) vertex labels associated with an \"unseen' graph using a given collection of VULS. The operation of the VULS mining algorithms, and the BMV algorithm, is fully described and evaluated. The evaluation is conducted using satellite image data where the ground surface is represented as a 3D surface with the z dimension describing grey scale value. The idea is to predict vertex labels describing ground type. A statistical analysis of the results, using the Friedman test, is also presented so as to demonstrate the statistical significance of the VULS based 3D surface regional classification idea. The results indicate that the VULS concept is well suited to the task of 3D surface regional classification.","PeriodicalId":289269,"journal":{"name":"2014 IEEE International Conference on Data Mining Workshop","volume":"207 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":"122430592","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

A Hierarchy Method Based on LDA and SVM for News Classification 基于LDA和SVM的新闻分类层次方法

2014 IEEE International Conference on Data Mining Workshop Pub Date : 2014-12-01 DOI: 10.1109/ICDMW.2014.8

Limeng Cui, Fan Meng, Yong Shi, Minqiang Li, An Liu

引用次数: 20