2014 IEEE International Conference on Data Mining Workshop最新文献_第8页

Real-time Dynamic Visualization Techniques for Massive Geospatial Data 海量地理空间数据的实时动态可视化技术

2014 IEEE International Conference on Data Mining Workshop Pub Date : 2014-12-01 DOI: 10.1109/ICDMW.2014.174

Zhou Ya’nan, H. Xiaodong, Li Jun

{"title":"Real-time Dynamic Visualization Techniques for Massive Geospatial Data","authors":"Zhou Ya’nan, H. Xiaodong, Li Jun","doi":"10.1109/ICDMW.2014.174","DOIUrl":"https://doi.org/10.1109/ICDMW.2014.174","url":null,"abstract":"Visualization is a research hotspot in Digital Earth. Available Digital Earth platforms, such as Google Earth and World Wind could only display the prebuilt tiles of geospatial data statically, due to inability to deal with massive geospatial data (especially the remotely sensed imagery) and to adapt for diversified configurations of visualization in real time. In this paper, we propose a complete technical solution for real-time dynamic visualization of massive geospatial data, and mainly focus on the following aspects: a) On the rendering nodes, we build a pyramid model-'Fish File' to store a single remotely sensed imagery, to improve imagery reading efficiency, b) On the visualization server, we adopt a 'distributed storage, centralized management' strategy to organize and manage massive geospatial data, and further introduce a 'data-performance-consistent' schedule scheme to speed the response of servers, c) On the clients, the slicing cache storage and cache update mechanism are proposed for rapid switchover of map layers and geospatial analysis. Following the solution, we constructed a visualization platform. And we compare the performance of our platform with that of the state-of-the-art in rendering of nodes, in response of servers, and in efficiency of platforms, and show screenshots of real-time dynamic visualization.","PeriodicalId":289269,"journal":{"name":"2014 IEEE International Conference on Data Mining Workshop","volume":"7 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120848720","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

Clustering Ensemble and Application in HST Dataset 聚类集成及其在HST数据集中的应用

2014 IEEE International Conference on Data Mining Workshop Pub Date : 2014-12-01 DOI: 10.1109/ICDMW.2014.143

Wenchao Xiao, Yan Yang, Hongjun Wang, Yingge Xu

{"title":"Clustering Ensemble and Application in HST Dataset","authors":"Wenchao Xiao, Yan Yang, Hongjun Wang, Yingge Xu","doi":"10.1109/ICDMW.2014.143","DOIUrl":"https://doi.org/10.1109/ICDMW.2014.143","url":null,"abstract":"Clustering ensemble is an important part of ensemble learning. It aims to study and integrate multiple clustering results from different clustering algorithms or same algorithm with different initial parameters for the same dataset. CHAMELEON is a hierarchical clustering algorithm which can discover natural clusters of different shapes and sizes as the result of its merging decision dynamically adapts to the different clustering model characterized. Inspired by the idea of CHAMELEON, the paper proposes a novel clustering ensemble model including semi-supervised method and discusses its application in fault diagnosis of high speed train (HST) running gear. The model is divided into three phases. Phase 1 is constructing a sparse graph through similarity matrix which aggregates multiple clustering results. Phase 2 is partitioning the sparse graph (vertex = object, edge weight = similarity) into a large number of relatively small sub-clusters. Phase 3 is obtaining the final clustering partition by merging these sub-clusters repeatedly. The experimental results demonstrate that our method out-performs some of state-of-the-art ensemble algorithms regarding the accuracy and stability and recognizes fault patterns of HST running gear effectively.","PeriodicalId":289269,"journal":{"name":"2014 IEEE International Conference on Data Mining Workshop","volume":"9 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":"132403806","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

Identifying Team Style in Soccer Using Formations Learned from Spatiotemporal Tracking Data 利用从时空跟踪数据中学习的阵型来识别足球中的团队风格

2014 IEEE International Conference on Data Mining Workshop Pub Date : 2014-12-01 DOI: 10.1109/ICDMW.2014.167

Alina Bialkowski, P. Lucey, Peter Carr, Yisong Yue, S. Sridharan, I. Matthews

引用次数: 77

Joint Propagation and Refinement for Mining Opinion Words and Targets 意见词与目标挖掘的联合传播与细化

2014 IEEE International Conference on Data Mining Workshop Pub Date : 2014-12-01 DOI: 10.1109/ICDMW.2014.66

Qiyun Zhao, Hao Wang, Pin Lv

{"title":"Joint Propagation and Refinement for Mining Opinion Words and Targets","authors":"Qiyun Zhao, Hao Wang, Pin Lv","doi":"10.1109/ICDMW.2014.66","DOIUrl":"https://doi.org/10.1109/ICDMW.2014.66","url":null,"abstract":"This paper proposes a novel Joint Propagation and Refinement (JPR) method to extract opinion words and targets. We adopt a growing heuristic method to extract new opinion words and targets in two parallel processes: propagation and refinement. In the propagation process, we generate the candidate sets of opinion words and targets and construct Sentiment Graph Model (SGM) to evaluate the relations between opinion words and targets. We employ statistical word co-occurrence and dependency patterns to identify these relations. In addition, we discover new patterns by the newly extracted opinion words and targets, which can capture opinion relations more precisely in the case of informal texts. In the refinement process, we prune false results and update model iteratively. We employ Automatic Rule Refinement (ARR) to refine the rules of extraction, which means to refine the rule to extract false results. By using false results pruning and ARR process, we can efficiently alleviate the error propagation problem in traditional bootstrapping based methods. Experimental results on both English and Chinese datasets demonstrate the effectiveness of our method.","PeriodicalId":289269,"journal":{"name":"2014 IEEE International Conference on Data Mining Workshop","volume":"12 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":"122154012","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: 5

Discretizing Numerical Attributes in Decision Tree for Big Data Analysis 面向大数据分析的决策树数值属性离散化

2014 IEEE International Conference on Data Mining Workshop Pub Date : 2014-12-01 DOI: 10.1109/ICDMW.2014.103

Yiqun Zhang, Yiu-ming Cheung

引用次数: 14

GlucoGuide: An Intelligent Type-2 Diabetes Solution Using Data Mining and Mobile Computing GlucoGuide:使用数据挖掘和移动计算的智能2型糖尿病解决方案

2014 IEEE International Conference on Data Mining Workshop Pub Date : 2014-12-01 DOI: 10.1109/ICDMW.2014.177

Yan Luo, C. Ling, Jody Schuurman, R. Petrella

{"title":"GlucoGuide: An Intelligent Type-2 Diabetes Solution Using Data Mining and Mobile Computing","authors":"Yan Luo, C. Ling, Jody Schuurman, R. Petrella","doi":"10.1109/ICDMW.2014.177","DOIUrl":"https://doi.org/10.1109/ICDMW.2014.177","url":null,"abstract":"Type-2 Diabetes (T2D) is a dreadful disease affecting hundreds of millions of people worldwide, and is linked and worsen by unhealthy lifestyles. However, managing T2D effectively with lifestyle change remains highly challenging for both T2D patients and doctors. In this paper, we proposed, built, and evaluated a personalized diabetes recommendation system, called Gluco Guide for T2D patients. Gluco Guide conveniently aggregates a variety of lifestyle data via medical sensors and mobile devices, mines the data with a novel data-mining framework, and outputs personalized and timely recommendations to patients aimed to control their blood glucose level. To evaluate its clinical efficiency, we conducted a three-month clinical trial on human subjects. Due to the high cost and complexity of trials on human, a small but representative subject group was involved. Two standard laboratory blood tests for diabetes were used before and after the trial. The results are quite remarkable. Generally speaking, Gluco Guide amounted to turning an early diabetic patient to be pre-diabetic, and pre-diabetic to non-diabetic, in only 3-months.","PeriodicalId":289269,"journal":{"name":"2014 IEEE International Conference on Data Mining Workshop","volume":"9 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":"122355207","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: 11

Mining of Training Samples for Multiple Learning Machines in Computer-Aided Detection of Lesions in CT Images 基于多学习机的CT图像病灶计算机辅助检测训练样本挖掘

2014 IEEE International Conference on Data Mining Workshop Pub Date : 2014-12-01 DOI: 10.1109/ICDMW.2014.111

Kenji Suzuki

引用次数: 1

Clinical Decision Making: A Framework for Predicting Rx Response 临床决策:预测Rx反应的框架

2014 IEEE International Conference on Data Mining Workshop Pub Date : 2014-12-01 DOI: 10.1109/ICDMW.2014.154

Aarti Sathyanarayana, Jyotishman Pathak, R. McCoy, S. Romero-Brufau, Maryam Panaziahar, J. Srivastava

引用次数: 5

Web Service QoS Prediction Approach in Mobile Internet Environments 移动互联网环境下的Web服务QoS预测方法

2014 IEEE International Conference on Data Mining Workshop Pub Date : 2014-12-01 DOI: 10.1109/ICDMW.2014.27

Lubao Wang, Qibo Sun, Shangguang Wang, You Ma, Jinliang Xu, Jinglin Li

引用次数: 7

Cost-Effective User Monitoring for Popularity Prediction of Online User-Generated Content 基于用户生成内容流行度预测的高性价比用户监测

2014 IEEE International Conference on Data Mining Workshop Pub Date : 2014-12-01 DOI: 10.1109/ICDMW.2014.72

Mengmeng Yang, Kai Chen, Zhongchen Miao, Xiaokang Yang

{"title":"Cost-Effective User Monitoring for Popularity Prediction of Online User-Generated Content","authors":"Mengmeng Yang, Kai Chen, Zhongchen Miao, Xiaokang Yang","doi":"10.1109/ICDMW.2014.72","DOIUrl":"https://doi.org/10.1109/ICDMW.2014.72","url":null,"abstract":"In this paper, we study on the popularity prediction of online user-generated contents, where high quality predictions give us much more flexibility and preparing time in deploying limited resources (such as advertising budget, monitoring capacity) into more popular contents. However the high retrieval cost of data used in prediction is a big challenge due to the large amount of users and contents involved. We propose a notion that higher popularity user-generated contents can be predicted by concentrating on fewer but informative users, as we notice the fact that contents generated by those users tend to become popular while that which are generated by the rest users do not. We develop a cost-effective popularity prediction framework to fulfil online prediction. It contains 3 modules: (a) online data retrieving, (b) informative users selection and (c) popularity prediction. A hybrid user selection algorithm and several popularity prediction algorithms/improvements are presented, and their performance are evaluated and compared using (a) the selected users' generated data and (b) all users' generated data, retrieved from Sina Weibo Micro blogger. The best prediction algorithm reaches a 78% accuracy at the time of 24 hours after publishing time when level width Nl equals 500. And the best combination of prediction and selection algorithms performs only about 7% worse on dataset of 2000 users than on dataset of all users (about 4.46 million).","PeriodicalId":289269,"journal":{"name":"2014 IEEE International Conference on Data Mining Workshop","volume":"36 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":"124410639","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