2002 IEEE International Conference on Data Mining, 2002. Proceedings.最新文献_第10页

gSpan: graph-based substructure pattern mining gSpan:基于图的子结构模式挖掘

2002 IEEE International Conference on Data Mining, 2002. Proceedings. Pub Date : 2002-12-09 DOI: 10.1109/ICDM.2002.1184038

Xifeng Yan, Jiawei Han

引用次数: 2414

Wavelet based UXO detection 基于小波的未爆弹药检测

2002 IEEE International Conference on Data Mining, 2002. Proceedings. Pub Date : 2002-12-09 DOI: 10.1109/ICDM.2002.1184012

S. Hodgson, N. Dunstan, R. Murison

引用次数: 1

Using functional PCA for cardiac motion exploration 应用功能性PCA进行心脏运动探查

2002 IEEE International Conference on Data Mining, 2002. Proceedings. Pub Date : 2002-12-09 DOI: 10.1109/ICDM.2002.1183890

D. Clot

引用次数: 5

Speed-up iterative frequent itemset mining with constraint changes 加速了约束变化的迭代频繁项集挖掘

2002 IEEE International Conference on Data Mining, 2002. Proceedings. Pub Date : 2002-12-09 DOI: 10.1109/ICDM.2002.1183892

G. Cong, B. Liu

{"title":"Speed-up iterative frequent itemset mining with constraint changes","authors":"G. Cong, B. Liu","doi":"10.1109/ICDM.2002.1183892","DOIUrl":"https://doi.org/10.1109/ICDM.2002.1183892","url":null,"abstract":"Mining of frequent itemsets is a fundamental data mining task. Past research has proposed many efficient algorithms for this purpose. Recent work also highlighted the importance of using constraints to focus the mining process to mine only those relevant itemsets. In practice, data mining is often an interactive and iterative process. The user typically changes constraints and runs the mining algorithm many times before being satisfied with the final results. This interactive process is very time consuming. Existing mining algorithms are unable to take advantage of this iterative process to use previous mining results to speed up the current mining process. This results in an enormous waste of time and computation. In this paper, we propose an efficient technique to utilize previous mining results to improve the efficiency of current mining when constraints are changed. We first introduce the concept of tree boundary to summarize useful information available from previous mining. We then show that the tree boundary provides an effective and efficient framework for the new mining. The proposed technique has been implemented in the context of two existing frequent itemset mining algorithms, FP-tree and tree projection. Experiment results on both synthetic and real-life datasets show that the proposed approach achieves a dramatic saving of computation.","PeriodicalId":405340,"journal":{"name":"2002 IEEE International Conference on Data Mining, 2002. Proceedings.","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129356792","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

FD/spl I.bar/Mine: discovering functional dependencies in a database using equivalences FD/spl .bar/Mine:使用等价发现数据库中的功能依赖

2002 IEEE International Conference on Data Mining, 2002. Proceedings. Pub Date : 2002-12-09 DOI: 10.1109/ICDM.2002.1184040

Hong Yao, Howard J. Hamilton, C. Butz

引用次数: 59

Mining online users' access records for web business intelligence 挖掘在线用户访问记录，实现网络商业智能

2002 IEEE International Conference on Data Mining, 2002. Proceedings. Pub Date : 2002-12-09 DOI: 10.1109/ICDM.2002.1184047

S. Fong, Serena Chan

引用次数: 1

On a capacity control using Boolean kernels for the learning of Boolean functions 用布尔核学习布尔函数的容量控制

2002 IEEE International Conference on Data Mining, 2002. Proceedings. Pub Date : 2002-12-09 DOI: 10.1109/ICDM.2002.1183934

Ken Sadohara

{"title":"On a capacity control using Boolean kernels for the learning of Boolean functions","authors":"Ken Sadohara","doi":"10.1109/ICDM.2002.1183934","DOIUrl":"https://doi.org/10.1109/ICDM.2002.1183934","url":null,"abstract":"This paper concerns the classification task in discrete attribute spaces, but considers the task in a more fundamental framework: the learning of Boolean functions. The purpose of this paper is to present a new learning algorithm for Boolean functions called Boolean kernel classifier (BKC) employing capacity control using Boolean kernels. BKC uses support vector machines (SVMs) as learning engines and Boolean kernels are primarily used for running SVMs in feature spaces spanned by conjunctions of Boolean literals. However, another important role of Boolean kernels is to appropriately control the size of its hypothesis space, to avoid overfitting. After applying a SVM to learn a classifier f in a feature space H induced by a Boolean kernel, BKC uses another Boolean kernel to compute the projections f/sup k/ of f onto a subspace H/sub k/ of H spanned by conjunctions with length at most k. By evaluating the accuracy of f/sup k/ on training data for any k, BKC can determine the smallest k such that f/sup k/ is as accurate as f and learn another f' in H/sub k/ expected to have lower error for unseen data. By an empirical study on learning of randomly generated Boolean functions, it is shown that the capacity control is effective, and BKC outperforms C4.5 and naive Bayes classifiers.","PeriodicalId":405340,"journal":{"name":"2002 IEEE International Conference on Data Mining, 2002. Proceedings.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132577775","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

2002 IEEE International Conference on Data Mining, 2002. Proceedings. Pub Date : 2002-12-09 DOI: 10.1109/ICDM.2002.1183906

S. Hirano, S. Tsumoto

引用次数: 50

Clustering spatial data when facing physical constraints 面对物理约束时的空间数据聚类

2002 IEEE International Conference on Data Mining, 2002. Proceedings. Pub Date : 2002-12-09 DOI: 10.1109/ICDM.2002.1184042

Osmar R Zaiane, Chi-Hoon Lee

{"title":"Clustering spatial data when facing physical constraints","authors":"Osmar R Zaiane, Chi-Hoon Lee","doi":"10.1109/ICDM.2002.1184042","DOIUrl":"https://doi.org/10.1109/ICDM.2002.1184042","url":null,"abstract":"Clustering spatial data is a well-known problem that has been extensively studied to find hidden patterns or meaningful sub-groups and has many applications such as satellite imagery, geographic information systems, medical image analysis, etc. Although many methods have been proposed in the literature, very few have considered constraints such that physical obstacles and bridges linking clusters may have significant consequences on the effectiveness of the clustering. Taking into account these constraints during the clustering process is costly, and the effective modeling of the constraints is of paramount importance for good performance. In this paper we define the clustering problem in the presence of constraints - obstacles and crossings - and investigate its efficiency and effectiveness for large databases. In addition, we introduce a new approach to model these constraints to prune the search space and reduce the number of polygons to test during clustering. The algorithm DBCluC we present detects clusters of arbitrary shape and is insensitive to noise and the input order Its average running complexity is O(NlogN) where N is the number of data objects.","PeriodicalId":405340,"journal":{"name":"2002 IEEE International Conference on Data Mining, 2002. Proceedings.","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132960194","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: 63

Recognition of common areas in a Web page using visual information: a possible application in a page classification 使用视觉信息识别Web页面中的公共区域:在页面分类中可能的应用

2002 IEEE International Conference on Data Mining, 2002. Proceedings. Pub Date : 2002-12-09 DOI: 10.1109/ICDM.2002.1183910

M. Kovačević, Michelangelo Diligenti, M. Gori, V. Milutinovic

引用次数: 132