2009 IEEE International Conference on Data Mining Workshops最新文献_第10页

Induction of Mean Output Prediction Trees from Continuous Temporal Meteorological Data 从连续时态气象数据中归纳平均输出预测树

2009 IEEE International Conference on Data Mining Workshops Pub Date : 2009-12-06 DOI: 10.1109/ICDMW.2009.30

Dima Alberg, Mark Last, Roni Neuman, Avi Sharon

引用次数: 6

Localized Content Based Image Retrieval with Self-Taught Multiple Instance Learning 基于本地化内容的图像检索与自学多实例学习

2009 IEEE International Conference on Data Mining Workshops Pub Date : 2009-12-06 DOI: 10.1109/ICDMW.2009.105

Qifeng Qiao, P. Beling

{"title":"Localized Content Based Image Retrieval with Self-Taught Multiple Instance Learning","authors":"Qifeng Qiao, P. Beling","doi":"10.1109/ICDMW.2009.105","DOIUrl":"https://doi.org/10.1109/ICDMW.2009.105","url":null,"abstract":"There are many scenarios in which multi-instance learning problems may be difficult to solve because of a lack of correctly labeled examples for algorithm training. Labeled examples may be difficult or expensive to obtain because human effort is often needed to produce labels and because there may be limitations on the ability to collect large samples for training from a homogeneous population. In this paper, we present a technique called self-taught multiple-instance learning (STMIL) that deals with learning from a limited number of ambiguously labeled examples. STMIL uses a sparse representation for examples belonging to different classes in terms of a shared dictionary derived from the unlabeled data. This sparse representation can be optimized under the multiple instance setting to both construct high-level features and unite the data distribution. We present an optimization procedure for STMIL along with experiments on localized content-based image retrieval. Our experimental results suggest that, though it learns from a small number of labeled examples, STMIL is superior to standard algorithms in terms of computational efficiency and is at least competitive in terms of accuracy.","PeriodicalId":351078,"journal":{"name":"2009 IEEE International Conference on Data Mining Workshops","volume":"226 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131654079","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: 7

The Flexible Climate Data Analysis Tools (CDAT) for Multi-model Climate Simulation Data 多模式气候模拟数据的灵活气候数据分析工具(CDAT)

2009 IEEE International Conference on Data Mining Workshops Pub Date : 2009-12-06 DOI: 10.1109/ICDMW.2009.64

Dean N. Williams, C. Doutriaux, B. Drach, R. McCoy

{"title":"The Flexible Climate Data Analysis Tools (CDAT) for Multi-model Climate Simulation Data","authors":"Dean N. Williams, C. Doutriaux, B. Drach, R. McCoy","doi":"10.1109/ICDMW.2009.64","DOIUrl":"https://doi.org/10.1109/ICDMW.2009.64","url":null,"abstract":"Being able to incorporate, inspect, and analyze data with newly developed technologies, diagnostics, and visualizations in an easy and flexible way has been a longstanding challenge for scientists interested in understanding the intrinsic and extrinsic empirical assessment of multi-model climate output. To improve research ability and productivity, these technologies and tool must be made easily available to help scientists understand and solve complex scientific climate changes. To increase productivity and ease the challenges of incorporating new tools into the hands of scientists, the Program for Climate Model Diagnosis and Intercomparison (PCMDI) developed the Climate Data Analysis Tools (CDAT). CDAT is an application for developing and bringing together disparate software tools for the discovery, examination, and intercomparison of coupled multi-model climate data. By collaborating with top climate institutions, computational organizations, and other science communities, the CDAT community of developers is leading the way to provide proven data management, analysis, visualization, and diagnostics capabilities to scientists. This communitywide effort has developed CDAT into a powerful and insightful application for knowledge discovery of observed and simulation climate data. As an analysis engine in the Earth System Grid (ESG) data infrastructure, CDAT is making it possible to remotely access and analyze climate data located at multiple sites around the world.","PeriodicalId":351078,"journal":{"name":"2009 IEEE International Conference on Data Mining Workshops","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127811952","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: 8

Fast Visual Trajectory Analysis Using Spatial Bayesian Networks 基于空间贝叶斯网络的快速视觉轨迹分析

2009 IEEE International Conference on Data Mining Workshops Pub Date : 2009-12-06 DOI: 10.1109/ICDMW.2009.44

T. Liebig, Christine Kopp, M. May

{"title":"Fast Visual Trajectory Analysis Using Spatial Bayesian Networks","authors":"T. Liebig, Christine Kopp, M. May","doi":"10.1109/ICDMW.2009.44","DOIUrl":"https://doi.org/10.1109/ICDMW.2009.44","url":null,"abstract":"During the past years the first tools for visual analysis of trajectory data appeared. Considering the growing sizes of trajectory collections, one important task is to ensure user interactivity during data analysis. In this paper we present a fast, model-based visualization approach for the analysis of location dependencies in large trajectory collections. Existing approaches are not suitable for visual dependency analysis as the size and complexity of trajectory data constrain ad hoc and advance computations. Also recent developments in the area of trajectory data warehouses cannot be applied because the spatial correlations are lost during trajectory aggregation. Our approach builds a compact model which represents the dependency structures of the data. The visualisation toolkit then interacts only with the model and is thus independent of the size of the underlying trajectory database. More precisely, we build a Bayesian Network model using the Scalable Sparse Bayesian Network Learning (SSBNL) algorithm, which we improve to represent also negative correlations. We implement our approach into the GIS MapInfo using MapBasic scripts for the user interface and an independent mediator script to retrieve patterns from the model. We demonstrate our approach using mobile phone data of the city of Milan, Italy.","PeriodicalId":351078,"journal":{"name":"2009 IEEE International Conference on Data Mining Workshops","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128158564","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: 17

2009 IEEE International Conference on Data Mining Workshops Pub Date : 2009-12-06 DOI: 10.1109/ICDMW.2009.99

H. Abe, S. Tsumoto

{"title":"Detecting Similarity of Transferring Datasets Based on Features of Classification Rules","authors":"H. Abe, S. Tsumoto","doi":"10.1109/ICDMW.2009.99","DOIUrl":"https://doi.org/10.1109/ICDMW.2009.99","url":null,"abstract":"In order to transfer mined knowledge for various datasets obtained from transferring situations, it is important to detect not only availability of transferring the knowledge but also detecting their limitations of the transfer. Although most of methods to detect the limitations use performance indices of sets of classifiers such as accuracies of classifier sets, those of each classifier are also useful. Data characterizing techniques have been developed to control learning algorithm selection by using statistical measurements of a dataset. Expanding this framework, we consider a method to reuse objective rule evaluation indices of classification rules such as support, precision, and recall, to measure similarity of different datasets. In this paper, we present a method to characterize given datasets based on objective rule evaluation indices and classification learning algorithms. The experimental results show the method can detect similarity of datasets even if the datasets have totally different attribute sets. This indicates that the limitations of transferring both of classifiers and learning algorithms can be detected as the similarity among datasets by using a learning algorithm.","PeriodicalId":351078,"journal":{"name":"2009 IEEE International Conference on Data Mining Workshops","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129120375","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

Semantic Linking between Video Ads and Web Services with Progressive Search 通过渐进式搜索实现视频广告和Web服务之间的语义链接

2009 IEEE International Conference on Data Mining Workshops Pub Date : 2009-12-06 DOI: 10.1109/ICDMW.2009.43

Bo Wang, Jinqiao Wang, Shi Chen, Ling-yu Duan, Hanqing Lu

引用次数: 5

A Differentially Private Graph Estimator 一个差分私有图估计

2009 IEEE International Conference on Data Mining Workshops Pub Date : 2009-12-06 DOI: 10.1109/ICDMW.2009.96

Darakhshan J. Mir, R. Wright

引用次数: 44

Information Services and Middleware for the Coastal Sensor Web 沿海传感器网的信息服务和中间件

2009 IEEE International Conference on Data Mining Workshops Pub Date : 2009-12-06 DOI: 10.1109/ICDMW.2009.108

S. Durbha, R. King, Santhosh K. Amanchi, Shruthi Bheemireddy, N. Younan

{"title":"Information Services and Middleware for the Coastal Sensor Web","authors":"S. Durbha, R. King, Santhosh K. Amanchi, Shruthi Bheemireddy, N. Younan","doi":"10.1109/ICDMW.2009.108","DOIUrl":"https://doi.org/10.1109/ICDMW.2009.108","url":null,"abstract":"It is well recognized that semantic conflicts are responsible for the most serious data heterogeneity problems hindering the efficient interoperability between heterogeneous information sources. In recent years, ontologies are widely used as a means for solving the information heterogeneity problems because of their capability to provide explicit meaning to the information. Several organizations are undertaking the development of domain specific ontlolgies to resolve the semantic ambiguities between various domain specific representations. These ontologies designed for a particular task could be a unique representation of their project needs. Hence, there arises a need to align heterogeneous ontologies to facilitate meaningful knowledge interchange between various sources. Thus, ontology mapping has emerged as an important requirement to enable semantic interoperability between different representations within a domain. In this paper we focus on the semantic heterogeneities present in the coastal information sources whose data are highly heterogeneous in syntax, structure and semantics. Ontological modeling was carried out for the various information sources. A data mining approach was adopted to align the concepts belonging to various land cover ontologies. We present a set of standardized information services and middleware for seamless access to information from various networks.","PeriodicalId":351078,"journal":{"name":"2009 IEEE International Conference on Data Mining Workshops","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2009-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127629815","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

Data Mining Geophysical Content from Satellites and Global Climate Models 来自卫星和全球气候模式的数据挖掘地球物理内容

2009 IEEE International Conference on Data Mining Workshops Pub Date : 2009-12-06 DOI: 10.1109/ICDMW.2009.109

D. Erickson, Jamison Daniel, M. Allen, A. Ganguly, F. Hoffman, S. Pawson, L. Ott, Eric Neilson

引用次数: 1

Feature Selection with High-Dimensional Imbalanced Data 高维不平衡数据的特征选择

2009 IEEE International Conference on Data Mining Workshops Pub Date : 2009-12-06 DOI: 10.1109/ICDMW.2009.35

J. V. Hulse, T. Khoshgoftaar, Amri Napolitano, Randall Wald

引用次数: 151