2002 IEEE International Conference on Data Mining, 2002. Proceedings.最新文献_第4页

On the mining of substitution rules for statistically dependent items 统计相关项的替换规则挖掘

2002 IEEE International Conference on Data Mining, 2002. Proceedings. Pub Date : 2002-12-09 DOI: 10.1109/ICDM.2002.1183986

Wei-Guang Teng, M. Hsieh, Ming-Syan Chen

{"title":"On the mining of substitution rules for statistically dependent items","authors":"Wei-Guang Teng, M. Hsieh, Ming-Syan Chen","doi":"10.1109/ICDM.2002.1183986","DOIUrl":"https://doi.org/10.1109/ICDM.2002.1183986","url":null,"abstract":"In this paper a new mining capability, called mining of substitution rules, is explored. A substitution refers to the choice made by a customer to replace the purchase of items with that of others. The process of mining substitution rules can be decomposed into two procedures. The first identifies concrete itemsets among a large number of frequent itemsets, where a concrete itemset is a frequent itemset whose items are statistically dependent. The second is substitution rule generation. Two concrete itemsets X and Y form a substitution rule, denoted by X /spl utri/ Y to mean that X is a substitute for Y if and only if X and Y are negatively correlated and the negative association rule X /spl rarr/ Y~ exists. We derive theoretical properties for the model of substitution rule mining. Then, in light of these properties, the SRM algorithm (substitution rule mining) is designed and implemented to discover substitution rules efficiently while attaining good statistical significance. Empirical studies are performed to evaluate the performance of the SRM algorithm. It is shown that SRM produces substitution rules of very high quality.","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":"130156696","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: 67

Automatic web page classification in a dynamic and hierarchical way 自动网页分类在一个动态和分层的方式

2002 IEEE International Conference on Data Mining, 2002. Proceedings. Pub Date : 2002-12-09 DOI: 10.1109/ICDM.2002.1183930

Xiaogang Peng, Ben Choi

引用次数: 47

Mining genes in DNA using GeneScout 使用GeneScout挖掘DNA中的基因

2002 IEEE International Conference on Data Mining, 2002. Proceedings. Pub Date : 2002-12-09 DOI: 10.1109/ICDM.2002.1184041

M. M. Yin, J. Wang

引用次数: 1

Mining general temporal association rules for items with different exhibition periods 挖掘具有不同展期的物品的通用时间关联规则

2002 IEEE International Conference on Data Mining, 2002. Proceedings. Pub Date : 2002-12-09 DOI: 10.1109/ICDM.2002.1183886

Cheng-Yue Chang, Ming-Syan Chen, Chang-Hung Lee

{"title":"Mining general temporal association rules for items with different exhibition periods","authors":"Cheng-Yue Chang, Ming-Syan Chen, Chang-Hung Lee","doi":"10.1109/ICDM.2002.1183886","DOIUrl":"https://doi.org/10.1109/ICDM.2002.1183886","url":null,"abstract":"In this paper we explore a new model of mining general temporal association rules from large databases where the exhibition periods of the items are allowed to be different from one to another. Note that in this new model, the downward closure property which all prior Apriori-based algorithms relied upon to attain good efficiency is no longer valid. As a result, how to efficiently generate candidate itemsets form large databases has become the major challenge. To address this issue, we develop an efficient algorithm, referred to as algorithm SPF (standing for Segmented Progressive Filter) in this paper The basic idea behind SPF is to first segment the database into sub-databases in such a way that items in each sub-database will have either the common starting time or the common ending time. Then, for each sub-database, SPF progressively filters candidate 2-itemsets with cumulative filtering thresholds either forward or backward in time. This feature allows SPF of adopting the scan reduction technique by generating all candidate k-itemsets (k>2) from candidate 2-itemsets directly. The experimental results show that algorithm SPF significantly outperforms other schemes which are extended from prior methods in terms of the execution time and scalability.","PeriodicalId":405340,"journal":{"name":"2002 IEEE International Conference on Data Mining, 2002. Proceedings.","volume":"40 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":"124371809","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: 103

/spl Delta/B/sup +/ tree: indexing 3D point sets for pattern discovery /spl Delta/B/sup +/ tree:为模式发现索引3D点集

2002 IEEE International Conference on Data Mining, 2002. Proceedings. Pub Date : 2002-12-09 DOI: 10.1109/ICDM.2002.1184033

Xiong Wang

引用次数: 2

Discovery of interesting association rules from Livelink web log data 从Livelink web日志数据中发现有趣的关联规则

2002 IEEE International Conference on Data Mining, 2002. Proceedings. Pub Date : 2002-12-09 DOI: 10.1109/ICDM.2002.1184048

Xiangji Huang, Aijun An, N. Cercone, Gary Promhouse

引用次数: 31

A parameterless method for efficiently discovering clusters of arbitrary shape in large datasets 一种有效发现大数据集任意形状聚类的无参数方法

2002 IEEE International Conference on Data Mining, 2002. Proceedings. Pub Date : 2002-12-09 DOI: 10.1109/ICDM.2002.1183901

Andrew Foss, Osmar R Zaiane

引用次数: 53

Mining top-k frequent closed patterns without minimum support 在没有最小支持的情况下挖掘top-k频繁封闭模式

2002 IEEE International Conference on Data Mining, 2002. Proceedings. Pub Date : 2002-12-09 DOI: 10.1109/ICDM.2002.1183905

Jiawei Han, Jianyong Wang, Ying Lu, P. Tzvetkov

{"title":"Mining top-k frequent closed patterns without minimum support","authors":"Jiawei Han, Jianyong Wang, Ying Lu, P. Tzvetkov","doi":"10.1109/ICDM.2002.1183905","DOIUrl":"https://doi.org/10.1109/ICDM.2002.1183905","url":null,"abstract":"In this paper, we propose a new mining task: mining top-k frequent closed patterns of length no less than min_/spl lscr/, where k is the desired number of frequent closed patterns to be mined, and min_/spl lscr/ is the minimal length of each pattern. An efficient algorithm, called TFP, is developed for mining such patterns without minimum support. Two methods, closed-node-count and descendant-sum are proposed to effectively raise support threshold and prune FP-tree both during and after the construction of FP-tree. During the mining process, a novel top-down and bottom-up combined FP-tree mining strategy is developed to speed-up support-raising and closed frequent pattern discovering. In addition, a fast hash-based closed pattern verification scheme has been employed to check efficiently if a potential closed pattern is really closed. Our performance study shows that in most cases, TFP outperforms CLOSET and CHARM, two efficient frequent closed pattern mining algorithms, even when both are running with the best tuned min-support. Furthermore, the method can be extended to generate association rules and to incorporate user-specified constraints.","PeriodicalId":405340,"journal":{"name":"2002 IEEE International Conference on Data Mining, 2002. Proceedings.","volume":"30 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":"133028197","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: 311

Feature selection for clustering - a filter solution 聚类的特征选择——一个过滤器解决方案

2002 IEEE International Conference on Data Mining, 2002. Proceedings. Pub Date : 2002-12-09 DOI: 10.1109/ICDM.2002.1183893

M. Dash, Ki-Hoon Choi, P. Scheuermann, Huan Liu

{"title":"Feature selection for clustering - a filter solution","authors":"M. Dash, Ki-Hoon Choi, P. Scheuermann, Huan Liu","doi":"10.1109/ICDM.2002.1183893","DOIUrl":"https://doi.org/10.1109/ICDM.2002.1183893","url":null,"abstract":"Processing applications with a large number of dimensions has been a challenge for the KDD community. Feature selection, an effective dimensionality reduction technique, is an essential pre-processing method to remove noisy features. In the literature only a few methods have been proposed for feature selection for clustering, and almost all these methods are 'wrapper' techniques that require a clustering algorithm to evaluate candidate feature subsets. The wrapper approach is largely unsuitable in real-world applications due to its heavy reliance on clustering algorithms that require parameters such as the number of clusters, and the lack of suitable clustering criteria to evaluate clustering in different subspaces. In this paper we propose a 'filter' method that is independent of any clustering algorithm. The proposed method is based on the observation that data with clusters has a very different point-to-point distance histogram to that of data without clusters. By exploiting this we propose an entropy measure that is low if data has distinct clusters and high if it does not. The entropy measure is suitable for selecting the most important subset of features because it is invariant with the number of dimensions, and is affected only by the quality of clustering. Extensive performance evaluation over synthetic, benchmark, and real datasets shows its effectiveness.","PeriodicalId":405340,"journal":{"name":"2002 IEEE International Conference on Data Mining, 2002. Proceedings.","volume":"10 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":"133866318","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: 437

A theory of inductive query answering 归纳式问答理论

2002 IEEE International Conference on Data Mining, 2002. Proceedings. Pub Date : 2002-12-09 DOI: 10.1109/ICDM.2002.1183894

L. D. Raedt, M. Jaeger, Sau-dan. Lee, H. Mannila

引用次数: 107