2008 Eighth IEEE International Conference on Data Mining最新文献_第8页

Inference Analysis in Privacy-Preserving Data Re-publishing 隐私保护数据再发布中的推理分析

2008 Eighth IEEE International Conference on Data Mining Pub Date : 2008-12-15 DOI: 10.1109/ICDM.2008.118

Guan Wang, Zutao Zhu, Wenliang Du, Zhouxuan Teng

引用次数: 30

Efficient Discovery of Statistically Significant Association Rules 统计显著关联规则的有效发现

2008 Eighth IEEE International Conference on Data Mining Pub Date : 2008-12-15 DOI: 10.1109/ICDM.2008.144

W. Hämäläinen, M. Nykänen

引用次数: 71

Graph-Based Iterative Hybrid Feature Selection 基于图的迭代混合特征选择

2008 Eighth IEEE International Conference on Data Mining Pub Date : 2008-12-15 DOI: 10.1109/ICDM.2008.63

Erheng Zhong, Sihong Xie, W. Fan, Jiangtao Ren, Jing Peng, Kun Zhang

{"title":"Graph-Based Iterative Hybrid Feature Selection","authors":"Erheng Zhong, Sihong Xie, W. Fan, Jiangtao Ren, Jing Peng, Kun Zhang","doi":"10.1109/ICDM.2008.63","DOIUrl":"https://doi.org/10.1109/ICDM.2008.63","url":null,"abstract":"When the number of labeled examples is limited, traditional supervised feature selection techniques often fail due to sample selection bias or unrepresentative sample problem. To solve this, semi-supervised feature selection techniques exploit the statistical information of both labeled and unlabeled examples in the same time. However, the results of semi-supervised feature selection can be at times unsatisfactory, and the culprit is on how to effectively use the unlabeled data. Quite different from both supervised and semi-supervised feature selection, we propose a ldquohybridrdquoframework based on graph models. We first apply supervised methods to select a small set of most critical features from the labeled data. Importantly, these initial features might otherwise be missed when selection is performed on the labeled and unlabeled examples simultaneously. Next,this initial feature set is expanded and corrected with the use of unlabeled data. We formally analyze why the expected performance of the hybrid framework is better than both supervised and semi-supervised feature selection. Experimental results demonstrate that the proposed method outperforms both traditional supervised and state-of-the-art semi-supervised feature selection algorithms by at least 10% inaccuracy on a number of text and biomedical problems with thousands of features to choose from. Software and dataset is available from the authors.","PeriodicalId":252958,"journal":{"name":"2008 Eighth IEEE International Conference on Data Mining","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122330828","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

Clustering Geospatial Objects via Hidden Markov Random Fields 基于隐马尔可夫随机场的地理空间对象聚类

2008 Eighth IEEE International Conference on Data Mining Pub Date : 2008-12-15 DOI: 10.1109/ICDM.2008.70

Makoto Sato, S. Imahara

引用次数: 0

Measuring Proximity on Graphs with Side Information 用边信息测量图的接近度

2008 Eighth IEEE International Conference on Data Mining Pub Date : 2008-12-15 DOI: 10.1109/ICDM.2008.42

Hanghang Tong, Huiming Qu, H. Jamjoom

引用次数: 16

Enhancing the Stability of Spectral Ordering with Sparsification and Partial Supervision: Application to Paleontological Data 用稀疏化和部分监督增强光谱排序的稳定性:在古生物数据中的应用

2008 Eighth IEEE International Conference on Data Mining Pub Date : 2008-12-15 DOI: 10.1109/ICDM.2008.120

Dimitrios Mavroeidis, Ella Bingham

{"title":"Enhancing the Stability of Spectral Ordering with Sparsification and Partial Supervision: Application to Paleontological Data","authors":"Dimitrios Mavroeidis, Ella Bingham","doi":"10.1109/ICDM.2008.120","DOIUrl":"https://doi.org/10.1109/ICDM.2008.120","url":null,"abstract":"Recent studies have demonstrated the prospects of data mining algorithms for addressing the task of seriation in paleontological data (i.e. the age-based ordering of the sites of excavation). A prominent approach is spectral ordering that computes a similarity measure between the sites and orders them such that similar sites become adjacent and dissimilar sites are placed far apart. In the paleontological domain, the similarity measure is based on the mammal genera whose remains are retrieved at each site of excavation. Although spectral ordering achieves good performance in the seriation task, it ignores the background knowledge that is naturally present in the domain, as paleontologists can derive the ages of the sites of excavation within some accuracy. On the other hand, the age information is uncertain, so the best approach would be to combine the background knowledge with the information on mammal co-occurrences. Motivated by this kind of partial supervision we propose a novel semi-supervised spectral ordering algorithm. Our algorithm modifies the Laplacian matrix used in spectral ordering, such that domain knowledge of the ordering is taken into account. Also, it performs feature selection (sparsification) by discarding features that contribute most to the unwanted variability of the data in bootstrap sampling. The theoretical properties of the proposed algorithm are thoroughly analyzed and it is demonstrated that the proposed framework enhances the stability of the spectral ordering output and induces computational gains.","PeriodicalId":252958,"journal":{"name":"2008 Eighth IEEE International Conference on Data Mining","volume":"304 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132916505","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

Discovering Significant Patterns in Multi-stream Sequences 发现多流序列中的重要模式

2008 Eighth IEEE International Conference on Data Mining Pub Date : 2008-12-15 DOI: 10.1109/ICDM.2008.146

Robert Gwadera, F. Crestani

引用次数: 18

DisCo: Distributed Co-clustering with Map-Reduce: A Case Study towards Petabyte-Scale End-to-End Mining 基于Map-Reduce的分布式协同集群:pb级端到端挖掘的案例研究

2008 Eighth IEEE International Conference on Data Mining Pub Date : 2008-12-15 DOI: 10.1109/ICDM.2008.142

S. Papadimitriou, Jimeng Sun

{"title":"DisCo: Distributed Co-clustering with Map-Reduce: A Case Study towards Petabyte-Scale End-to-End Mining","authors":"S. Papadimitriou, Jimeng Sun","doi":"10.1109/ICDM.2008.142","DOIUrl":"https://doi.org/10.1109/ICDM.2008.142","url":null,"abstract":"Huge datasets are becoming prevalent; even as researchers, we now routinely have to work with datasets that are up to a few terabytes in size. Interesting real-world applications produce huge volumes of messy data. The mining process involves several steps, starting from pre-processing the raw data to estimating the final models. As data become more abundant, scalable and easy-to-use tools for distributed processing are also emerging. Among those, Map-Reduce has been widely embraced by both academia and industry. In database terms, Map-Reduce is a simple yet powerful execution engine, which can be complemented with other data storage and management components, as necessary. In this paper we describe our experiences and findings in applying Map-Reduce, from raw data to final models, on an important mining task. In particular, we focus on co-clustering, which has been studied in many applications such as text mining, collaborative filtering, bio-informatics, graph mining. We propose the distributed co-clustering (DisCo) framework, which introduces practical approaches for distributed data pre-processing, and co-clustering. We develop DisCo using Hadoop, an open source Map-Reduce implementation. We show that DisCo can scale well and efficiently process and analyze extremely large datasets (up to several hundreds of gigabytes) on commodity hardware.","PeriodicalId":252958,"journal":{"name":"2008 Eighth IEEE International Conference on Data Mining","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122340564","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: 246

SPARCL: Efficient and Effective Shape-Based Clustering SPARCL:高效的基于形状的聚类

2008 Eighth IEEE International Conference on Data Mining Pub Date : 2008-12-15 DOI: 10.1109/ICDM.2008.73

V. Chaoji, M. Hasan, Saeed Salem, Mohammed J. Zaki

引用次数: 33

HIREL: An Incremental Clustering Algorithm for Relational Datasets 关系型数据集的增量聚类算法

2008 Eighth IEEE International Conference on Data Mining Pub Date : 2008-12-15 DOI: 10.1109/ICDM.2008.116

Tao Li, S. Anand

引用次数: 18