2002 IEEE International Conference on Data Mining, 2002. Proceedings.最新文献_第2页

Optimal projections of high dimensional data 高维数据的最佳投影

2002 IEEE International Conference on Data Mining, 2002. Proceedings. Pub Date : 2002-12-09 DOI: 10.1109/ICDM.2002.1184006

E. Corchado, C. Fyfe

引用次数: 9

Mining significant associations in large scale text corpora 挖掘大规模文本语料库中的重要关联

2002 IEEE International Conference on Data Mining, 2002. Proceedings. Pub Date : 2002-12-09 DOI: 10.1109/ICDM.2002.1183933

P. Raghavan, Panayiotis Tsaparas

引用次数: 6

Evolutionary time series segmentation for stock data mining 股票数据挖掘的演化时间序列分割

2002 IEEE International Conference on Data Mining, 2002. Proceedings. Pub Date : 2002-12-09 DOI: 10.1109/ICDM.2002.1183889

K. F. Chung, Tak-Chung Fu, R. Luk, Vincent Ng

{"title":"Evolutionary time series segmentation for stock data mining","authors":"K. F. Chung, Tak-Chung Fu, R. Luk, Vincent Ng","doi":"10.1109/ICDM.2002.1183889","DOIUrl":"https://doi.org/10.1109/ICDM.2002.1183889","url":null,"abstract":"Stock data in the form of multiple time series are difficult to process, analyze and mine. However, when they can be transformed into meaningful symbols like technical patterns, it becomes easier. Most recent work on time series queries concentrates only on how to identify a given pattern from a time series. Researchers do not consider the problem of identifying a suitable set of time points for segmenting the time series in accordance with a given set of pattern templates (e.g., a set of technical patterns for stock analysis). On the other hand, using fixed length segmentation is a primitive approach to this problem; hence, a dynamic approach (with high controllability) is preferred so that the time series can be segmented flexibly and effectively according to the needs of users and applications. In view of the fact that such a segmentation problem is an optimization problem and evolutionary computation is an appropriate tool to solve it, we propose an evolutionary time series segmentation algorithm. This approach allows a sizeable set of stock patterns to be generated for mining or query. In addition, defining the similarity between time series (or time series segments) is of fundamental importance in fitness computation. By identifying perceptually important points directly from the time domain, time series segments and templates of different lengths can be compared and intuitive pattern matching can be carried out in an effective and efficient manner. Encouraging experimental results are reported from tests that segment the time series of selected Hong Kong stocks.","PeriodicalId":405340,"journal":{"name":"2002 IEEE International Conference on Data Mining, 2002. Proceedings.","volume":"103 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":"130765051","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: 57

Progressive modeling 先进的建模

2002 IEEE International Conference on Data Mining, 2002. Proceedings. Pub Date : 2002-12-09 DOI: 10.1109/ICDM.2002.1183899

W. Fan, Haixun Wang, Philip S. Yu, S. Lo, S. Stolfo

引用次数: 4

Text document categorization by term association 按术语关联对文本文档进行分类

2002 IEEE International Conference on Data Mining, 2002. Proceedings. Pub Date : 2002-12-09 DOI: 10.1109/ICDM.2002.1183881

M. Antonie, Osmar R Zaiane

{"title":"Text document categorization by term association","authors":"M. Antonie, Osmar R Zaiane","doi":"10.1109/ICDM.2002.1183881","DOIUrl":"https://doi.org/10.1109/ICDM.2002.1183881","url":null,"abstract":"A good text classifier is a classifier that efficiently categorizes large sets of text documents in a reasonable time frame and with an acceptable accuracy, and that provides classification rules that are human readable for possible fine-tuning. If the training of the classifier is also quick, this could become in some application domains a good asset for the classifier. Many techniques and algorithms for automatic text categorization have been devised. According to published literature, some are more accurate than others, and some provide more interpretable classification models than others. However, none can combine all the beneficial properties enumerated above. In this paper we present a novel approach for automatic text categorization that borrows from market basket analysis techniques using association rule mining in the data-mining field. We focus on two major problems: (1) finding the best term association rules in a textual database by generating and pruning; and (2) using the rules to build a text classifier. Our text categorization method proves to be efficient and effective, and experiments on well-known collections show that the classifier performs well. In addition, training as well as classification are both fast and the generated rules are human readable.","PeriodicalId":405340,"journal":{"name":"2002 IEEE International Conference on Data Mining, 2002. Proceedings.","volume":"34 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":"128829258","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: 264

Comparison of lazy Bayesian rule, and tree-augmented Bayesian learning 懒惰贝叶斯规则和树增强贝叶斯学习的比较

2002 IEEE International Conference on Data Mining, 2002. Proceedings. Pub Date : 2002-12-09 DOI: 10.1109/ICDM.2002.1183993

Zhihai Wang, Geoffrey I. Webb

引用次数: 26

Attribute (feature) completion - the theory of attributes from data mining prospect 属性(特征)补全——来自数据挖掘前景的属性理论

2002 IEEE International Conference on Data Mining, 2002. Proceedings. Pub Date : 2002-12-09 DOI: 10.1109/ICDM.2002.1183914

T. Lin

引用次数: 33

Maintenance of sequential patterns for record modification using pre-large sequences 维护使用预大序列进行记录修改的顺序模式

2002 IEEE International Conference on Data Mining, 2002. Proceedings. Pub Date : 2002-12-09 DOI: 10.1109/ICDM.2002.1184031

Ching-Yao Wang, T. Hong, S. Tseng

引用次数: 4

Association analysis with one scan of databases 通过一次数据库扫描进行关联分析

2002 IEEE International Conference on Data Mining, 2002. Proceedings. Pub Date : 2002-12-09 DOI: 10.1109/ICDM.2002.1184015

Hao Huang, Xindong Wu, R. Relue

引用次数: 76

Mining a set of coregulated RNA sequences 挖掘一组协同调节的RNA序列

2002 IEEE International Conference on Data Mining, 2002. Proceedings. Pub Date : 2002-12-09 DOI: 10.1109/ICDM.2002.1184014

Yuh-Jyh Hu

引用次数: 2