2010 IEEE International Conference on Data Mining最新文献_第9页

Multi-dimensional Mass Estimation and Mass-based Clustering 多维质量估计和基于质量的聚类

2010 IEEE International Conference on Data Mining Pub Date : 2010-12-13 DOI: 10.1109/ICDM.2010.49

K. Ting, Jonathan R. Wells

引用次数: 25

Accelerating Radius-Margin Parameter Selection for SVMs Using Geometric Bounds 基于几何边界的支持向量机加速半径边界参数选择

2010 IEEE International Conference on Data Mining Pub Date : 2010-12-13 DOI: 10.1109/ICDM.2010.100

Ben Goodrich, D. Albrecht, P. Tischer

引用次数: 0

Content-Based Methods for Predicting Web-Site Demographic Attributes 基于内容的网站人口统计属性预测方法

2010 IEEE International Conference on Data Mining Pub Date : 2010-12-13 DOI: 10.1109/ICDM.2010.97

Santosh Kabbur, Eui-Hong Han, G. Karypis

{"title":"Content-Based Methods for Predicting Web-Site Demographic Attributes","authors":"Santosh Kabbur, Eui-Hong Han, G. Karypis","doi":"10.1109/ICDM.2010.97","DOIUrl":"https://doi.org/10.1109/ICDM.2010.97","url":null,"abstract":"Demographic information plays an important role in gaining valuable insights about a web-site's user-base and is used extensively to target online advertisements and promotions. This paper investigates machine-learning approaches for predicting the demographic attributes of web-sites using information derived from their content and their hyper linked structure and not relying on any information directly or indirectly obtained from the web-site's users. Such methods are important because users are becoming increasingly more concerned about sharing their personal and behavioral information on the Internet. Regression-based approaches are developed and studied for predicting demographic attributes that utilize different content-derived features, different ways of building the prediction models, and different ways of aggregating web-page level predictions that take into account the web's hyper linked structure. In addition, a matrix-approximation based approach is developed for coupling the predictions of individual regression models into a model designed to predict the probability mass function of the attribute. Extensive experiments show that these methods are able to achieve an RMSE of 8-10% and provide insights on how to best train and apply such models.","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":"126374467","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: 28

Multi-label Feature Selection for Graph Classification 图分类的多标签特征选择

2010 IEEE International Conference on Data Mining Pub Date : 2010-12-13 DOI: 10.1109/ICDM.2010.58

Xiangnan Kong, Philip S. Yu

{"title":"Multi-label Feature Selection for Graph Classification","authors":"Xiangnan Kong, Philip S. Yu","doi":"10.1109/ICDM.2010.58","DOIUrl":"https://doi.org/10.1109/ICDM.2010.58","url":null,"abstract":"Nowadays, the classification of graph data has become an important and active research topic in the last decade, which has a wide variety of real world applications, e.g. drug activity predictions and kinase inhibitor discovery. Current research on graph classification focuses on single-label settings. However, in many applications, each graph data can be assigned with a set of multiple labels simultaneously. Extracting good features using multiple labels of the graphs becomes an important step before graph classification. In this paper, we study the problem of multi-label feature selection for graph classification and propose a novel solution, called gMLC, to efficiently search for optimal sub graph features for graph objects with multiple labels. Different from existing feature selection methods in vector spaces which assume the feature set is given, we perform multi-label feature selection for graph data in a progressive way together with the sub graph feature mining process. We derive an evaluation criterion, named gHSIC, to estimate the dependence between sub graph features and multiple labels of graphs. Then a branch-and-bound algorithm is proposed to efficiently search for optimal sub graph features by judiciously pruning the sub graph search space using multiple labels. Empirical studies on real-world tasks demonstrate that our feature selection approach can effectively boost multi-label graph classification performances and is more efficient by pruning the sub graph search space using multiple labels.","PeriodicalId":294061,"journal":{"name":"2010 IEEE International Conference on Data Mining","volume":"2012 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":"127376914","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: 45

Max-Clique: A Top-Down Graph-Based Approach to Frequent Pattern Mining Max-Clique:一种自顶向下基于图的频繁模式挖掘方法

2010 IEEE International Conference on Data Mining Pub Date : 2010-12-13 DOI: 10.1109/ICDM.2010.73

Yan Xie, Philip S. Yu

引用次数: 20

Network Simplification with Minimal Loss of Connectivity 网络简化与最小的连通性损失

2010 IEEE International Conference on Data Mining Pub Date : 2010-12-13 DOI: 10.1109/ICDM.2010.133

Fang Zhou, S. Mahler, Hannu (TT) Toivonen

{"title":"Network Simplification with Minimal Loss of Connectivity","authors":"Fang Zhou, S. Mahler, Hannu (TT) Toivonen","doi":"10.1109/ICDM.2010.133","DOIUrl":"https://doi.org/10.1109/ICDM.2010.133","url":null,"abstract":"We propose a novel problem to simplify weighted graphs by pruning least important edges from them. Simplified graphs can be used to improve visualization of a network, to extract its main structure, or as a pre-processing step for other data mining algorithms. We define a graph connectivity function based on the best paths between all pairs of nodes. Given the number of edges to be pruned, the problem is then to select a subset of edges that best maintains the overall graph connectivity. Our model is applicable to a wide range of settings, including probabilistic graphs, flow graphs and distance graphs, since the path quality function that is used to find best paths can be defined by the user. We analyze the problem, and give lower bounds for the effect of individual edge removal in the case where the path quality function has a natural recursive property. We then propose a range of algorithms and report on experimental results on real networks derived from public biological databases. The results show that a large fraction of edges can be removed quite fast and with minimal effect on the overall graph connectivity. A rough semantic analysis of the removed edges indicates that few important edges were removed, and that the proposed approach could be a valuable tool in aiding users to view or explore weighted graphs.","PeriodicalId":294061,"journal":{"name":"2010 IEEE International Conference on Data Mining","volume":"26 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":"122041022","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: 52

Efficient Probabilistic Latent Semantic Analysis with Sparsity Control 基于稀疏度控制的高效概率潜在语义分析

2010 IEEE International Conference on Data Mining Pub Date : 2010-12-13 DOI: 10.1109/ICDM.2010.136

Sen Liu, Chaolun Xia, Xiaohong Jiang

引用次数: 9

Feature Selection for Unsupervised Learning Using Random Cluster Ensembles 基于随机聚类集成的无监督学习特征选择

2010 IEEE International Conference on Data Mining Pub Date : 2010-12-13 DOI: 10.1109/ICDM.2010.137

H. Elghazel, A. Aussem

引用次数: 27

Rare Category Characterization 稀有品类特征

2010 IEEE International Conference on Data Mining Pub Date : 2010-12-13 DOI: 10.1109/ICDM.2010.154

Jingrui He, Hanghang Tong, J. Carbonell

引用次数: 37

Viral Marketing for Multiple Products 多种产品的病毒式营销

2010 IEEE International Conference on Data Mining Pub Date : 2010-12-13 DOI: 10.1109/ICDM.2010.52

S. Datta, Anirban Majumder, Nisheeth Shrivastava

{"title":"Viral Marketing for Multiple Products","authors":"S. Datta, Anirban Majumder, Nisheeth Shrivastava","doi":"10.1109/ICDM.2010.52","DOIUrl":"https://doi.org/10.1109/ICDM.2010.52","url":null,"abstract":"Viral Marketing, the idea of exploiting social interactions of users to propagate awareness for products, has gained considerable focus in recent years. One of the key issues in this area is to select the best seeds that maximize the influence propagated in the social network. In this paper, we define the seed selection problem (called t-Influence Maximization, or t-IM) for multiple products. Specifically, given the social network and t products along with their seed requirements, we want to select seeds for each product that maximize the overall influence. As the seeds are typically sent promotional messages, to avoid spamming users, we put a hard constraint on the number of products for which any single user can be selected as a seed. In this paper, we design two efficient techniques for the t-IM problem, called Greedy and FairGreedy. The Greedy algorithm uses simple greedy hill climbing, but still results in a 1/3-approximation to the optimum. Our second technique, FairGreedy, allocates seeds with not only high overall influence (close to Greedy in practice), but also ensures fairness across the influence of different products. We also design efficient heuristics for estimating the influence of the selected seeds, that are crucial for running the seed selection on large social network graphs. Finally, using extensive simulations on real-life social graphs, we show the effectiveness and scalability of our techniques compared to existing and naive strategies.","PeriodicalId":294061,"journal":{"name":"2010 IEEE International Conference on Data Mining","volume":"1989 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":"125496392","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: 56