2010 IEEE International Conference on Data Mining最新文献_第5页

Permutations as Angular Data: Efficient Inference in Factorial Spaces 作为角数据的排列:阶乘空间中的有效推理

2010 IEEE International Conference on Data Mining Pub Date : 2010-12-13 DOI: 10.1109/ICDM.2010.122

S. Plis, T. Lane, V. Calhoun

{"title":"Permutations as Angular Data: Efficient Inference in Factorial Spaces","authors":"S. Plis, T. Lane, V. Calhoun","doi":"10.1109/ICDM.2010.122","DOIUrl":"https://doi.org/10.1109/ICDM.2010.122","url":null,"abstract":"Distributions over permutations arise in applications ranging from multi-object tracking to ranking of instances. The difficulty of dealing with these distributions is caused by the size of their domain, which is factorial in the number of considered entities ($n!$). It makes the direct definition of a multinomial distribution over permutation space impractical for all but a very small $n$. In this work we propose an embedding of all $n!$ permutations for a given $n$ in a surface of a hyper sphere defined in $mathbbm{R}^{(n-1)}$. As a result of the embedding, we acquire ability to define continuous distributions over a hyper sphere with all the benefits of directional statistics. We provide polynomial time projections between the continuous hyper sphere representation and the $n!$-element permutation space. The framework provides a way to use continuous directional probability densities and the methods developed thereof for establishing densities over permutations. As a demonstration of the benefits of the framework we derive an inference procedure for a state-space model over permutations. We demonstrate the approach with simulations on a large number of objects hardly manageable by the state of the art inference methods, and an application to a real flight traffic control dataset.","PeriodicalId":294061,"journal":{"name":"2010 IEEE International Conference on Data Mining","volume":"8 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":"134370003","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

Generalized Probabilistic Matrix Factorizations for Collaborative Filtering 协同过滤的广义概率矩阵分解

2010 IEEE International Conference on Data Mining Pub Date : 2010-12-13 DOI: 10.1109/ICDM.2010.116

Hanhuai Shan, A. Banerjee

引用次数: 162

SONNET: Efficient Approximate Nearest Neighbor Using Multi-core 十四行诗:高效近似近邻使用多核

2010 IEEE International Conference on Data Mining Pub Date : 2010-12-13 DOI: 10.1109/ICDM.2010.157

M. Hasan, Hilmi Yildirim, Abhirup Chakraborty

引用次数: 6

Subgroup Discovery Meets Bayesian Networks -- An Exceptional Model Mining Approach 子群发现满足贝叶斯网络——一种特殊的模型挖掘方法

2010 IEEE International Conference on Data Mining Pub Date : 2010-12-13 DOI: 10.1109/ICDM.2010.53

W. Duivesteijn, A. Knobbe, Ad Feelders, Matthijs van Leeuwen

引用次数: 67

Finding Local Anomalies in Very High Dimensional Space 在非常高维空间中寻找局部异常

2010 IEEE International Conference on Data Mining Pub Date : 2010-12-13 DOI: 10.1109/ICDM.2010.151

T. D. Vries, S. Chawla, M. Houle

引用次数: 86

Active Spectral Clustering 主动光谱聚类

2010 IEEE International Conference on Data Mining Pub Date : 2010-12-13 DOI: 10.1109/ICDM.2010.119

Xiang Wang, I. Davidson

引用次数: 89

Exponential Family Tensor Factorization for Missing-Values Prediction and Anomaly Detection 缺失值预测与异常检测的指数族张量分解

2010 IEEE International Conference on Data Mining Pub Date : 2010-12-13 DOI: 10.1109/ICDM.2010.39

K. Hayashi, Takashi Takenouchi, T. Shibata, Yuki Kamiya, Daishi Kato, Kazuo Kunieda, Keiji Yamada, K. Ikeda

引用次数: 21

Supervised Link Prediction Using Multiple Sources 多源监督链路预测

2010 IEEE International Conference on Data Mining Pub Date : 2010-12-13 DOI: 10.1109/ICDM.2010.112

Zhengdong Lu, Berkant Savas, Wei Tang, I. Dhillon

引用次数: 116

An Approach Based on Tree Kernels for Opinion Mining of Online Product Reviews 基于树核的在线产品评论意见挖掘方法

2010 IEEE International Conference on Data Mining Pub Date : 2010-12-13 DOI: 10.1109/ICDM.2010.104

Peng Jiang, Chunxia Zhang, Hongping Fu, Zhendong Niu, Qing Yang

{"title":"An Approach Based on Tree Kernels for Opinion Mining of Online Product Reviews","authors":"Peng Jiang, Chunxia Zhang, Hongping Fu, Zhendong Niu, Qing Yang","doi":"10.1109/ICDM.2010.104","DOIUrl":"https://doi.org/10.1109/ICDM.2010.104","url":null,"abstract":"Opinion mining is a challenging task to identify the opinions or sentiments underlying user generated contents, such as online product reviews, blogs, discussion forums, etc. Previous studies that adopt machine learning algorithms mainly focus on designing effective features for this complex task. This paper presents our approach based on tree kernels for opinion mining of online product reviews. Tree kernels alleviate the complexity of feature selection and generate effective features to satisfy the special requirements in opinion mining. In this paper, we define several tree kernels for sentiment expression extraction and sentiment classification, which are subtasks of opinion mining. Our proposed tree kernels encode not only syntactic structure information, but also sentiment related information, such as sentiment boundary and sentiment polarity, which are important features to opinion mining. Experimental results on a benchmark data set indicate that tree kernels can significantly improve the performance of both sentiment expression extraction and sentiment classification. Besides, a linear combination of our proposed tree kernels and traditional feature vector kernel achieves the best performances using the benchmark data set.","PeriodicalId":294061,"journal":{"name":"2010 IEEE International Conference on Data Mining","volume":"32 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":"125046867","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: 39

Exploiting Local Data Uncertainty to Boost Global Outlier Detection 利用局部数据不确定性提高全局异常值检测

2010 IEEE International Conference on Data Mining Pub Date : 2010-12-13 DOI: 10.1109/ICDM.2010.10

Bo Liu, Jie Yin, Yanshan Xiao, Longbing Cao, Philip S. Yu

{"title":"Exploiting Local Data Uncertainty to Boost Global Outlier Detection","authors":"Bo Liu, Jie Yin, Yanshan Xiao, Longbing Cao, Philip S. Yu","doi":"10.1109/ICDM.2010.10","DOIUrl":"https://doi.org/10.1109/ICDM.2010.10","url":null,"abstract":"This paper presents a novel hybrid approach to outlier detection by incorporating local data uncertainty into the construction of a global classifier. To deal with local data uncertainty, we introduce a confidence value to each data example in the training data, which measures the strength of the corresponding class label. Our proposed method works in two steps. Firstly, we generate a pseudo training dataset by computing a confidence value of each input example on its class label. We present two different mechanisms: kernel k-means clustering algorithm and kernel LOF-based algorithm, to compute the confidence values based on the local data behavior. Secondly, we construct a global classifier for outlier detection by generalizing the SVDD-based learning framework to incorporate both positive and negative examples as well as their associated confidence values. By integrating local and global outlier detection, our proposed method explicitly handles the uncertainty of the input data and enhances the ability of SVDD in reducing the sensitivity to noise. Extensive experiments on real life datasets demonstrate that our proposed method can achieve a better tradeoff between detection rate and false alarm rate as compared to four state-of-the-art outlier detection algorithms.","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":"123480680","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: 16