Proceedings. IEEE International Conference on Data Mining最新文献_第3页

Feature Selection in the Tensor Product Feature Space. 张量积特征空间中的特征选择。

Proceedings. IEEE International Conference on Data Mining Pub Date : 2009-01-01 DOI: 10.1109/ICDM.2009.101

Aaron Smalter, Jun Huan, Gerald Lushington

引用次数: 16

Information Extraction for Clinical Data Mining: A Mammography Case Study. 临床数据挖掘的信息提取：乳腺放射摄影案例研究

Proceedings. IEEE International Conference on Data Mining Pub Date : 2009-01-01 DOI: 10.1109/icdmw.2009.63

Houssam Nassif, Ryan Woods, Elizabeth Burnside, Mehmet Ayvaci, Jude Shavlik, David Page

{"title":"Information Extraction for Clinical Data Mining: A Mammography Case Study.","authors":"Houssam Nassif, Ryan Woods, Elizabeth Burnside, Mehmet Ayvaci, Jude Shavlik, David Page","doi":"10.1109/icdmw.2009.63","DOIUrl":"10.1109/icdmw.2009.63","url":null,"abstract":"Breast cancer is the leading cause of cancer mortality in women between the ages of 15 and 54. During mammography screening, radiologists use a strict lexicon (BI-RADS) to describe and report their findings. Mammography records are then stored in a well-defined database format (NMD). Lately, researchers have applied data mining and machine learning techniques to these databases. They successfully built breast cancer classifiers that can help in early detection of malignancy. However, the validity of these models depends on the quality of the underlying databases. Unfortunately, most databases suffer from inconsistencies, missing data, inter-observer variability and inappropriate term usage. In addition, many databases are not compliant with the NMD format and/or solely consist of text reports. BI-RADS feature extraction from free text and consistency checks between recorded predictive variables and text reports are crucial to addressing this problem. We describe a general scheme for concept information retrieval from free text given a lexicon, and present a BI-RADS features extraction algorithm for clinical data mining. It consists of a syntax analyzer, a concept finder and a negation detector. The syntax analyzer preprocesses the input into individual sentences. The concept finder uses a semantic grammar based on the BI-RADS lexicon and the experts' input. It parses sentences detecting BI-RADS concepts. Once a concept is located, a lexical scanner checks for negation. Our method can handle multiple latent concepts within the text, filtering out ultrasound concepts. On our dataset, our algorithm achieves 97.7% precision, 95.5% recall and an F1-score of 0.97. It outperforms manual feature extraction at the 5% statistical significance level.","PeriodicalId":74565,"journal":{"name":"Proceedings. IEEE International Conference on Data Mining","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3676897/pdf/nihms464344.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"31505491","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Fully Automated Method for Discovering Community Structures in High Dimensional Data. 一种发现高维数据中社区结构的全自动方法。

Proceedings. IEEE International Conference on Data Mining Pub Date : 2009-01-01 DOI: 10.1109/ICDM.2009.141

Jianhua Ruan

{"title":"A Fully Automated Method for Discovering Community Structures in High Dimensional Data.","authors":"Jianhua Ruan","doi":"10.1109/ICDM.2009.141","DOIUrl":"https://doi.org/10.1109/ICDM.2009.141","url":null,"abstract":"Identifying modules, or natural communities, in large complex networks is fundamental in many fields, including social sciences, biological sciences and engineering. Recently several methods have been developed to automatically identify communities from complex networks by optimizing the modularity function. The advantage of this type of approaches is that the algorithm does not require any parameter to be tuned. However, the modularity-based methods for community discovery assume that the network structure is given explicitly and is correct. In addition, these methods work best if the network is unweighted and/or sparse. In reality, networks are often not directly defined, or may be given as an affinity matrix. In the first case, each node of the network is defined as a point in a high dimensional space and different networks can be obtained with different network construction methods, resulting in different community structures. In the second case, an affinity matrix may define a dense weighted graph, for which modularity-based methods do not perform well. In this work, we propose a very simple algorithm to automatically identify community structures from these two types of data. Our approach utilizes a k-nearest-neighbor network construction method to capture the topology embedded in high dimensional data, and applies a modularity-based algorithm to identify the optimal community structure. A key to our approach is that the network construction is incorporated with the community identification process and is totally parameter-free. Furthermore, our method can suggest appropriate preprocessing/normalization of the data to improve the results of community identification. We tested our methods on several synthetic and real data sets, and evaluated its performance by internal or external accuracy indices. Compared with several existing approaches, our method is not only fully automatic, but also has the best accuracy overall.","PeriodicalId":74565,"journal":{"name":"Proceedings. IEEE International Conference on Data Mining","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/ICDM.2009.141","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"32732007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 14

Using Betweenness Centrality to Identify Manifold Shortcuts. 利用中间中心性识别流形捷径。

Proceedings. IEEE International Conference on Data Mining Pub Date : 2008-12-01 DOI: 10.1109/ICDMW.2008.39

William J Cukierski, David J Foran

引用次数: 33

Predictive Integration of Gene Ontology-Driven Similarity and Functional Interactions. 基因本体驱动的相似性和功能相互作用的预测集成。

Proceedings. IEEE International Conference on Data Mining Pub Date : 2006-12-01 DOI: 10.1109/ICDMW.2006.130

Francisco Azuaje, Haiying Wang, Huiru Zheng, Olivier Bodenreider, Alban Chesneau

引用次数: 27