Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining最新文献_第4页

A scalable two-stage approach for a class of dimensionality reduction techniques 一类降维技术的可扩展两阶段方法

Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining Pub Date : 2010-07-25 DOI: 10.1145/1835804.1835846

Liang Sun, Betul Ceran, Jieping Ye

{"title":"A scalable two-stage approach for a class of dimensionality reduction techniques","authors":"Liang Sun, Betul Ceran, Jieping Ye","doi":"10.1145/1835804.1835846","DOIUrl":"https://doi.org/10.1145/1835804.1835846","url":null,"abstract":"Dimensionality reduction plays an important role in many data mining applications involving high-dimensional data. Many existing dimensionality reduction techniques can be formulated as a generalized eigenvalue problem, which does not scale to large-size problems. Prior work transforms the generalized eigenvalue problem into an equivalent least squares formulation, which can then be solved efficiently. However, the equivalence relationship only holds under certain assumptions without regularization, which severely limits their applicability in practice. In this paper, an efficient two-stage approach is proposed to solve a class of dimensionality reduction techniques, including Canonical Correlation Analysis, Orthonormal Partial Least Squares, linear Discriminant Analysis, and Hypergraph Spectral Learning. The proposed two-stage approach scales linearly in terms of both the sample size and data dimensionality. The main contributions of this paper include (1) we rigorously establish the equivalence relationship between the proposed two-stage approach and the original formulation without any assumption; and (2) we show that the equivalence relationship still holds in the regularization setting. We have conducted extensive experiments using both synthetic and real-world data sets. Our experimental results confirm the equivalence relationship established in this paper. Results also demonstrate the scalability of the proposed two-stage approach.","PeriodicalId":20529,"journal":{"name":"Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2010-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82384231","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: 42

Document clustering via dirichlet process mixture model with feature selection 基于特征选择的dirichlet过程混合模型的文档聚类

Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining Pub Date : 2010-07-25 DOI: 10.1145/1835804.1835901

Guan Yu, Rui-zhang Huang, Zhaojun Wang

引用次数: 61

GLS-SOD: a generalized local statistical approach for spatial outlier detection GLS-SOD:一种用于空间离群点检测的广义局部统计方法

Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining Pub Date : 2010-07-25 DOI: 10.1145/1835804.1835939

F. Chen, Chang-Tien Lu, Arnold P. Boedihardjo

引用次数: 50

Data winnowing 数据筛选

Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining Pub Date : 2010-07-25 DOI: 10.1145/1835804.1835806

Y. Freund

引用次数: 0

Cold start link prediction 冷启动链路预测

Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining Pub Date : 2010-07-25 DOI: 10.1145/1835804.1835855

V. Leroy, B. B. Cambazoglu, F. Bonchi

引用次数: 171

Medical coding classification by leveraging inter-code relationships 利用代码间关系进行医学编码分类

Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining Pub Date : 2010-07-25 DOI: 10.1145/1835804.1835831

Yan Yan, Glenn Fung, Jennifer G. Dy, Rómer Rosales

{"title":"Medical coding classification by leveraging inter-code relationships","authors":"Yan Yan, Glenn Fung, Jennifer G. Dy, Rómer Rosales","doi":"10.1145/1835804.1835831","DOIUrl":"https://doi.org/10.1145/1835804.1835831","url":null,"abstract":"Medical coding or classification is the process of transforming information contained in patient medical records into standard predefined medical codes. There are several worldwide accepted medical coding conventions associated with diagnoses and medical procedures; however, in the United States the Ninth Revision of ICD(ICD-9) provides the standard for coding clinical records. Accurate medical coding is important since it is used by hospitals for insurance billing purposes. Since after discharge a patient can be assigned or classified to several ICD-9 codes, the coding problem can be seen as a multi-label classification problem. In this paper, we introduce a multi-label large-margin classifier that automatically learns the underlying inter-code structure and allows the controlled incorporation of prior knowledge about medical code relationships. In addition to refining and learning the code relationships, our classifier can also utilize this shared information to improve its performance. Experiments on a publicly available dataset containing clinical free text and their associated medical codes showed that our proposed multi-label classifier outperforms related multi-label models in this problem.","PeriodicalId":20529,"journal":{"name":"Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining","volume":"331 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2010-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77141746","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: 53

Indexing and mining time sequences 索引和挖掘时间序列

Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining Pub Date : 2010-07-25 DOI: 10.1145/1835804.1866295

Lei Li, C. Faloutsos

引用次数: 0

Data mining with differential privacy 差分隐私的数据挖掘

Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining Pub Date : 2010-07-25 DOI: 10.1145/1835804.1835868

Arik Friedman, A. Schuster

{"title":"Data mining with differential privacy","authors":"Arik Friedman, A. Schuster","doi":"10.1145/1835804.1835868","DOIUrl":"https://doi.org/10.1145/1835804.1835868","url":null,"abstract":"We consider the problem of data mining with formal privacy guarantees, given a data access interface based on the differential privacy framework. Differential privacy requires that computations be insensitive to changes in any particular individual's record, thereby restricting data leaks through the results. The privacy preserving interface ensures unconditionally safe access to the data and does not require from the data miner any expertise in privacy. However, as we show in the paper, a naive utilization of the interface to construct privacy preserving data mining algorithms could lead to inferior data mining results. We address this problem by considering the privacy and the algorithmic requirements simultaneously, focusing on decision tree induction as a sample application. The privacy mechanism has a profound effect on the performance of the methods chosen by the data miner. We demonstrate that this choice could make the difference between an accurate classifier and a completely useless one. Moreover, an improved algorithm can achieve the same level of accuracy and privacy as the naive implementation but with an order of magnitude fewer learning samples.","PeriodicalId":20529,"journal":{"name":"Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2010-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74292963","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: 473

Online discovery and maintenance of time series motifs 在线发现和维护时间序列图案

Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining Pub Date : 2010-07-25 DOI: 10.1145/1835804.1835941

A. Mueen, Eamonn J. Keogh

{"title":"Online discovery and maintenance of time series motifs","authors":"A. Mueen, Eamonn J. Keogh","doi":"10.1145/1835804.1835941","DOIUrl":"https://doi.org/10.1145/1835804.1835941","url":null,"abstract":"The detection of repeated subsequences, time series motifs, is a problem which has been shown to have great utility for several higher-level data mining algorithms, including classification, clustering, segmentation, forecasting, and rule discovery. In recent years there has been significant research effort spent on efficiently discovering these motifs in static offline databases. However, for many domains, the inherent streaming nature of time series demands online discovery and maintenance of time series motifs. In this paper, we develop the first online motif discovery algorithm which monitors and maintains motifs exactly in real time over the most recent history of a stream. Our algorithm has a worst-case update time which is linear to the window size and is extendible to maintain more complex pattern structures. In contrast, the current offline algorithms either need significant update time or require very costly pre-processing steps which online algorithms simply cannot afford. Our core ideas allow useful extensions of our algorithm to deal with arbitrary data rates and discovering multidimensional motifs. We demonstrate the utility of our algorithms with a variety of case studies in the domains of robotics, acoustic monitoring and online compression.","PeriodicalId":20529,"journal":{"name":"Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining","volume":"32 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2010-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73669293","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: 135

Session details: Research track 20: evolving and spatial data 研究专场20:演变和空间数据

Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining Pub Date : 2010-07-25 DOI: 10.1145/3248800

Hui Xiong

引用次数: 0