2010 IEEE International Conference on Data Mining最新文献_第8页

Compressed Nonnegative Sparse Coding 压缩非负稀疏编码

2010 IEEE International Conference on Data Mining Pub Date : 2010-12-13 DOI: 10.1109/ICDM.2010.162

Fei Wang, Ping Li

引用次数: 9

A Novel Contrast Co-learning Framework for Generating High Quality Training Data 生成高质量训练数据的新型对比协同学习框架

2010 IEEE International Conference on Data Mining Pub Date : 2010-12-13 DOI: 10.1109/ICDM.2010.23

Zeyu Zheng, Jun Yan, Shuicheng Yan, Ning Liu, Zheng Chen, Ming Zhang

{"title":"A Novel Contrast Co-learning Framework for Generating High Quality Training Data","authors":"Zeyu Zheng, Jun Yan, Shuicheng Yan, Ning Liu, Zheng Chen, Ming Zhang","doi":"10.1109/ICDM.2010.23","DOIUrl":"https://doi.org/10.1109/ICDM.2010.23","url":null,"abstract":"The good performances of most classical learning algorithms are generally founded on high quality training data, which are clean and unbiased. The availability of such data is however becoming much harder than ever in many real world problems due to the difficulties in collecting large scale unbiased data and precisely labeling them for training. In this paper, we propose a general Contrast Co-learning (CCL) framework to refine the biased and noisy training data when an unbiased yet unlabeled data pool is available. CCL starts with multiple sets of probably biased and noisy training data and trains a set of classifiers individually. Then under the assumption that the confidently classified data samples may have higher probabilities to be correctly classified, CCL iteratively and automatically filtering out possible data noises as well as adding those confidently classified samples from the unlabeled data pool to correct the bias. Through this process, we can generate a cleaner and unbiased training dataset with theoretical guarantees. Extensive experiments on two public text datasets clearly show that CCL consistently improves the algorithmic classification performance on biased and noisy training data compared with several state-of-the-art classical algorithms.","PeriodicalId":294061,"journal":{"name":"2010 IEEE International Conference on Data Mining","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121320572","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

One-Class Matrix Completion with Low-Density Factorizations 低密度分解的一类矩阵补全

2010 IEEE International Conference on Data Mining Pub Date : 2010-12-13 DOI: 10.1109/ICDM.2010.164

Vikas Sindhwani, S. Bucak, Jianying Hu, A. Mojsilovic

{"title":"One-Class Matrix Completion with Low-Density Factorizations","authors":"Vikas Sindhwani, S. Bucak, Jianying Hu, A. Mojsilovic","doi":"10.1109/ICDM.2010.164","DOIUrl":"https://doi.org/10.1109/ICDM.2010.164","url":null,"abstract":"Consider a typical recommendation problem. A company has historical records of products sold to a large customer base. These records may be compactly represented as a sparse customer-times-product ``who-bought-what\" binary matrix. Given this matrix, the goal is to build a model that provides recommendations for which products should be sold next to the existing customer base. Such problems may naturally be formulated as collaborative filtering tasks. However, this is a {it one-class} setting, that is, the only known entries in the matrix are one-valued. If a customer has not bought a product yet, it does not imply that the customer has a low propensity to {it potentially} be interested in that product. In the absence of entries explicitly labeled as negative examples, one may resort to considering unobserved customer-product pairs as either missing data or as surrogate negative instances. In this paper, we propose an approach to explicitly deal with this kind of ambiguity by instead treating the unobserved entries as optimization variables. These variables are optimized in conjunction with learning a weighted, low-rank non-negative matrix factorization (NMF) of the customer-product matrix, similar to how Transductive SVMs implement the low-density separation principle for semi-supervised learning. Experimental results show that our approach gives significantly better recommendations in comparison to various competing alternatives on one-class collaborative filtering tasks.","PeriodicalId":294061,"journal":{"name":"2010 IEEE International Conference on Data Mining","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122488644","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: 94

Interval-valued Matrix Factorization with Applications 区间值矩阵分解及其应用

2010 IEEE International Conference on Data Mining Pub Date : 2010-12-13 DOI: 10.1109/ICDM.2010.115

Zhiyong Shen, Liang Du, Xukun Shen, Yi-Dong Shen

引用次数: 4

Discovering Correlated Subspace Clusters in 3D Continuous-Valued Data 三维连续值数据中相关子空间簇的发现

2010 IEEE International Conference on Data Mining Pub Date : 2010-12-13 DOI: 10.1109/ICDM.2010.19

Kelvin Sim, Z. Aung, V. Gopalkrishnan

{"title":"Discovering Correlated Subspace Clusters in 3D Continuous-Valued Data","authors":"Kelvin Sim, Z. Aung, V. Gopalkrishnan","doi":"10.1109/ICDM.2010.19","DOIUrl":"https://doi.org/10.1109/ICDM.2010.19","url":null,"abstract":"Subspace clusters represent useful information in high-dimensional data. However, mining significant subspace clusters in continuous-valued 3D data such as stock-financial ratio-year data, or gene-sample-time data, is difficult. Firstly, typical metrics either find subspaces with very few objects, or they find too many insignificant subspaces – those which exist by chance. Besides, typical 3D subspace clustering approaches abound with parameters, which are usually set under biased assumptions, making the mining process a ‘guessing game’. We address these concerns by proposing an information theoretic measure, which allows us to identify 3D subspace clusters that stand out from the data. We also develop a highly effective, efficient and parameter-robust algorithm, which is a hybrid of information theoretical and statistical techniques, to mine these clusters. From extensive experimentations, we show that our approach can discover significant 3D subspace clusters embedded in 110 synthetic datasets of varying conditions. We also perform a case study on real-world stock datasets, which shows that our clusters can generate higher profits compared to those mined by other approaches.","PeriodicalId":294061,"journal":{"name":"2010 IEEE International Conference on Data Mining","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125790937","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: 35

Modeling Experts and Novices in Citizen Science Data for Species Distribution Modeling 物种分布建模的公民科学数据建模专家和新手

2010 IEEE International Conference on Data Mining Pub Date : 2010-12-13 DOI: 10.1109/ICDM.2010.103

Jun Yu, Weng-Keen Wong, R. Hutchinson

{"title":"Modeling Experts and Novices in Citizen Science Data for Species Distribution Modeling","authors":"Jun Yu, Weng-Keen Wong, R. Hutchinson","doi":"10.1109/ICDM.2010.103","DOIUrl":"https://doi.org/10.1109/ICDM.2010.103","url":null,"abstract":"Citizen scientists, who are volunteers from the community that participate as field assistants in scientific studies [3], enable research to be performed at much larger spatial and temporal scales than trained scientists can cover. Species distribution modeling [6], which involves understanding species-habitat relationships, is a research area that can benefit greatly from citizen science. The eBird project [16] is one of the largest citizen science programs in existence. By allowing birders to upload observations of bird species to an online database, eBird can provide useful data for species distribution modeling. However, since birders vary in their levels of expertise, the quality of data submitted to eBird is often questioned. In this paper, we develop a probabilistic model called the Occupancy-Detection-Expertise (ODE) model that incorporates the expertise of birders submitting data to eBird. We show that modeling the expertise of birders can improve the accuracy of predicting observations of a bird species at a site. In addition, we can use the ODE model for two other tasks: predicting birder expertise given their history of eBird checklists and identifying bird species that are difficult for novices to detect.","PeriodicalId":294061,"journal":{"name":"2010 IEEE International Conference on Data Mining","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134027538","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: 58

Testing the Significance of Patterns in Data with Cluster Structure 用聚类结构检验数据中模式的显著性

2010 IEEE International Conference on Data Mining Pub Date : 2010-12-13 DOI: 10.1109/ICDM.2010.61

Niko Vuokko, P. Kaski

引用次数: 5

A Generalized Linear Threshold Model for Multiple Cascades 多级联的广义线性阈值模型

2010 IEEE International Conference on Data Mining Pub Date : 2010-12-13 DOI: 10.1109/ICDM.2010.153

Nishith Pathak, A. Banerjee, J. Srivastava

引用次数: 123

A Binary Decision Diagram-Based One-Class Classifier 基于二元决策图的单类分类器

2010 IEEE International Conference on Data Mining Pub Date : 2010-12-13 DOI: 10.1109/ICDM.2010.84

Takuro Kutsuna

引用次数: 4

Understanding of Internal Clustering Validation Measures 内部聚类验证措施的理解

2010 IEEE International Conference on Data Mining Pub Date : 2010-12-13 DOI: 10.1109/ICDM.2010.35

Yanchi Liu, Zhongmou Li, Hui Xiong, Xuedong Gao, Junjie Wu

引用次数: 851