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Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06)最新文献

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Kernel-based Algorithms and Visualization for Interval Data Mining 区间数据挖掘的核算法和可视化
Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06) Pub Date : 2006-12-18 DOI: 10.1109/ICDMW.2006.103
Thanh-Nghi Do, F. Poulet
{"title":"Kernel-based Algorithms and Visualization for Interval Data Mining","authors":"Thanh-Nghi Do, F. Poulet","doi":"10.1109/ICDMW.2006.103","DOIUrl":"https://doi.org/10.1109/ICDMW.2006.103","url":null,"abstract":"Our investigation aims at extending kernel methods to interval data mining and using graphical methods to explain the obtained results. Interval data type can be a good way to aggregate large datasets into smaller ones or to represent data with uncertainty. No algorithmic changes are required from the usual case of continuous data other than the modification of the radial basis kernel function evaluation. Thus, kernel-based algorithms can deal easily with interval data. The numerical test results with real and artificial datasets show that the proposed methods have given promising performance. We also use interactive graphical decision tree algorithms and visualization techniques to give an insight into support vector machines results. The user has a better understanding of the models' behaviour","PeriodicalId":291862,"journal":{"name":"Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06)","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127295388","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: 7
Robust Feature Extraction and Reduction of Mass Spectrometry Data for Cancer Classification 用于癌症分类的质谱数据鲁棒特征提取与约简
Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06) Pub Date : 2006-12-18 DOI: 10.1109/ICDMW.2006.143
T. Pham, V. Chandramohan, Xiaobo Zhou, Stephen T. C. Wong
{"title":"Robust Feature Extraction and Reduction of Mass Spectrometry Data for Cancer Classification","authors":"T. Pham, V. Chandramohan, Xiaobo Zhou, Stephen T. C. Wong","doi":"10.1109/ICDMW.2006.143","DOIUrl":"https://doi.org/10.1109/ICDMW.2006.143","url":null,"abstract":"Application of proteomics coupled with pattern classification techniques to discover novel biomarkers that can be used for the predictive diagnoses of several cancer diseases. However, for effective classification, the extraction of good features that can represent the identities of different classes plays the frontal critical factor for any classification problems. In addition, another major problem associated with pattern recognition is how to effectively handle a large number of features. This paper addresses these two frontal issues for mass spectrometry (MS) classification. We apply the theory of linear predictive coding to extract features and vector quantization to reduce the storage of the large feature space of MS data. The proposed methodology was tested using two MS-based cancer datasets and the results are promising","PeriodicalId":291862,"journal":{"name":"Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06)","volume":"176 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114080171","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
Significance Analysis and Improved Discovery of Differentially Co-expressed Gene Sets in Microarray Data 微阵列数据中差异共表达基因集的显著性分析和改进发现
Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06) Pub Date : 2006-12-18 DOI: 10.1109/ICDMW.2006.146
Haixia Li, K. R. K. Murthy
{"title":"Significance Analysis and Improved Discovery of Differentially Co-expressed Gene Sets in Microarray Data","authors":"Haixia Li, K. R. K. Murthy","doi":"10.1109/ICDMW.2006.146","DOIUrl":"https://doi.org/10.1109/ICDMW.2006.146","url":null,"abstract":"Differential coexpression signifies the deregulated pathways as opposed to differential expression that signifies change of gene expression. Kostka and Spang proposed a score and an algorithm to elicit differentially coexpressed gene-sets. We analyze the statistical properties of their score in two different data processing settings and obtain respective null-distributions to provide the statistical significance of a gene-set through the p-value of its score. We propose to use these p-values to automate their algorithm. In addition, we propose a two stage algorithm, based on friendly neighbors (FNs) algorithm, called FNs-KS algorithm for improved discovery of such gene set i.e. improves both sensitivity and specificity of the discovery","PeriodicalId":291862,"journal":{"name":"Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128771792","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: 3
Improving the Results and Performance of Clustering Bit-encoded XML Documents 改进位编码XML文档聚类的结果和性能
Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06) Pub Date : 2006-12-18 DOI: 10.1109/ICDMW.2006.97
M. Kozielski
{"title":"Improving the Results and Performance of Clustering Bit-encoded XML Documents","authors":"M. Kozielski","doi":"10.1109/ICDMW.2006.97","DOIUrl":"https://doi.org/10.1109/ICDMW.2006.97","url":null,"abstract":"Clustering XML documents according to their structure is one of the techniques that may improve the effectiveness of XML documents storage and retrieval. One of existing approaches to this problem is to encode XML document structure as a string of bits and cluster such feature vectors. High dimensionality and sparseness of the feature vectors are the weaknesses of this method. The paper presents four methods reducing the dimensionality of the bit feature vectors. Two of these methods are novel. They are dedicated to XML documents and should be applied during the encoding process. The results showed good efficiency of these inner-encoding methods and their ability of improving clustering results in some cases. The methods presented in the paper are tested on two datasets of XML documents having different characteristics","PeriodicalId":291862,"journal":{"name":"Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06)","volume":"288 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115213913","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: 8
Hierarchical Density Shaving: A clustering and visualization framework for large biological datasets 分层密度剃须:大型生物数据集的聚类和可视化框架
Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06) Pub Date : 2006-12-18 DOI: 10.1109/ICDMW.2006.92
Gunjan Gupta, Alexander Y. Liu, Joydeep Ghosh
{"title":"Hierarchical Density Shaving: A clustering and visualization framework for large biological datasets","authors":"Gunjan Gupta, Alexander Y. Liu, Joydeep Ghosh","doi":"10.1109/ICDMW.2006.92","DOIUrl":"https://doi.org/10.1109/ICDMW.2006.92","url":null,"abstract":"In many clustering applications for bioinformatics, only part of the data clusters into one or more groups while the rest needs to be pruned. For such situations, we present hierarchical density shaving (HDS), a framework that consists of a fast, hierarchical, density-based clustering algorithm. Our framework also provides a simple yet powerful 2D visualization of the hierarchy of clusters that can be very useful for further exploration. We present results to show the effectiveness of our methods","PeriodicalId":291862,"journal":{"name":"Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06)","volume":"172 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116056335","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: 11
Bounded Support Vector Machines, Semidefinite 有界支持向量机，半定
Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06) Pub Date : 2006-12-18 DOI: 10.1109/ICDMW.2006.37
Jianping Li, Zhenyu Chen, Weixuan Xu
{"title":"Bounded Support Vector Machines, Semidefinite","authors":"Jianping Li, Zhenyu Chen, Weixuan Xu","doi":"10.1109/ICDMW.2006.37","DOIUrl":"https://doi.org/10.1109/ICDMW.2006.37","url":null,"abstract":"Credit risk assessment is a basic and critical factor in credit risk management. In addition to conventional statistical method, neural network, decision tree and Support Vector Machine are the popular methods in this field in recent years. However, they all have weakness in two aspects: poor classification accuracy for unbalanced data and poor interpretability in real applications. A novel method, called Least Squares Support Feature Machine (LS-SFM), is proposed to reduce the misclassification cost and achieve an interpretable model by introducing the single feature kernel and sampling method. One important character of LS-SFM is that it can deliver the significance of each feature to users Our experiment on a real credit card dataset shows good performance. LS-SFM outperforms some well-known methods in several aspects.","PeriodicalId":291862,"journal":{"name":"Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06)","volume":"77 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114908749","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: 0
Segmentation of Evolving Complex Data and Generation of Models 演化复杂数据的分割与模型生成
Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06) Pub Date : 2006-12-18 DOI: 10.1109/ICDMW.2006.144
Corrado Loglisci, Margherita Berardi
{"title":"Segmentation of Evolving Complex Data and Generation of Models","authors":"Corrado Loglisci, Margherita Berardi","doi":"10.1109/ICDMW.2006.144","DOIUrl":"https://doi.org/10.1109/ICDMW.2006.144","url":null,"abstract":"The problem of time-series segmentation has been widely discussed and it has been successfully applied in a variety of areas including computational genomics, telecommunications and process monitoring. Nevertheless not many techniques have been devised to deal with multidimensional evolving data describing complex objects. Moreover, in many applications the resulting segments have not a description understandable to the user, and this is exacerbated in the applications with complex data. Our contribute aims to propose an algorithmic framework to segment multidimensional evolving data or multidimensional time-series and to resort to an ILP system to generate characterizations of segments close to the user. The application and the results to the real-world data are reported","PeriodicalId":291862,"journal":{"name":"Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06)","volume":"88 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125456738","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: 8
Fuzzy Related Classification Approach Based on Semantic Measurement for Web Document 基于语义度量的Web文档模糊相关分类方法
Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06) Pub Date : 2006-12-18 DOI: 10.1109/ICDMW.2006.83
Hui Zhang, Hantao Song
{"title":"Fuzzy Related Classification Approach Based on Semantic Measurement for Web Document","authors":"Hui Zhang, Hantao Song","doi":"10.1109/ICDMW.2006.83","DOIUrl":"https://doi.org/10.1109/ICDMW.2006.83","url":null,"abstract":"A fuzzy related classification approach for Web document based on ontology concept semantic measurement is presented in this paper. The features of ontology in the semantic Web are explored, and a method to compute the semantic similarity between concepts is put forward. Motivated by the semantic similarity and the fuzzy characteristics of the Web document, the fuzzy related technology combined with VSM in classifying Web documents into a predefined set of ontology categories is adopted. Finally some examples are presented to show that the results are satisfying","PeriodicalId":291862,"journal":{"name":"Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125221290","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: 7
Mining Information Extraction Models for HmtDB annotation 面向HmtDB注释的信息提取模型挖掘
Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06) Pub Date : 2006-12-18 DOI: 10.1109/ICDMW.2006.113
Margherita Berardi, D. Malerba, M. Attimonelli
{"title":"Mining Information Extraction Models for HmtDB annotation","authors":"Margherita Berardi, D. Malerba, M. Attimonelli","doi":"10.1109/ICDMW.2006.113","DOIUrl":"https://doi.org/10.1109/ICDMW.2006.113","url":null,"abstract":"Advances of genome sequencing techniques have risen an overwhelming increase in the literature on discovered genes, proteins and their role in biological processes. However, the biomedical literature remains a greatly unexploited source of biological information. Information extraction (IE) techniques are necessary to map this information into structured representations that allow facts relating domain-relevant entities to be automatically recognized. In this paper, we present a framework that supports biologists in the task of automatic extraction of information from texts. The framework integrates a data mining module that discovers extraction rules from a set of manually labelled texts. Extraction models are subsequently applied in an automatic mode on unseen texts. We report an application to a real-world dataset composed by publications selected to support biologists in the annotation of the HmtDB database","PeriodicalId":291862,"journal":{"name":"Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123554393","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: 2
A simple genetic algorithm for tracing the deformed midline on a single slice of brain CT using quadratic Bezier curves 一个简单的遗传算法，用于跟踪变形的中线在脑CT单片上使用二次贝塞尔曲线
Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06) Pub Date : 2006-12-18 DOI: 10.1109/ICDMW.2006.22
Chun-Chih Liao, Furen Xiao, J. Wong, I. Chiang
{"title":"A simple genetic algorithm for tracing the deformed midline on a single slice of brain CT using quadratic Bezier curves","authors":"Chun-Chih Liao, Furen Xiao, J. Wong, I. Chiang","doi":"10.1109/ICDMW.2006.22","DOIUrl":"https://doi.org/10.1109/ICDMW.2006.22","url":null,"abstract":"Midline shift (MLS) is one of the most important quantitative features clinicians use to evaluate the severity of brain compression. It can be recognized by modeling brain deformation according to the estimated biomechanical properties of the brain structures. This paper proposes a novel method to identify the deformed midline by decomposing it into three segments: the upper and the lower straight segments representing parts of the tough meninges separating two brain hemispheres, and the central curved segment formed by a quadratic Bezier curve, representing the intervening soft brain tissue. The deformed midline is obtained by minimizing the summed square of the differences across all midline points, applying a genetic algorithm. Our algorithm was evaluated on images containing various pathologies from 81 consecutive patients treated in a single institute over one-year period. The deformed midlines were evaluated by human experts, and the values of midline shift were accurate in 95%","PeriodicalId":291862,"journal":{"name":"Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126443579","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: 19
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