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2002 IEEE International Conference on Data Mining, 2002. Proceedings.最新文献

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A personalized music filtering system based on melody style classification 一种基于旋律风格分类的个性化音乐过滤系统
2002 IEEE International Conference on Data Mining, 2002. Proceedings. Pub Date : 2002-12-09 DOI: 10.1109/ICDM.2002.1184020
Fang-Fei Kuo, M. Shan
{"title":"A personalized music filtering system based on melody style classification","authors":"Fang-Fei Kuo, M. Shan","doi":"10.1109/ICDM.2002.1184020","DOIUrl":"https://doi.org/10.1109/ICDM.2002.1184020","url":null,"abstract":"With the growth of digital music, the personalized music filtering system is helpful for users. Melody style is one of the music features to represent user's music preference. We present a personalized content-based music filtering system to support music recommendation based on user's preference of melody style. We propose the multitype melody style classification approach to recommend the music objects. The system learns the user preference by mining the melody patterns from the music access behavior of the user. A two-way melody preference classifier is therefore constructed for each user. Music recommendation is made through this melody preference classifier. Performance evaluation shows that the filtering effect of the proposed approach meets user's preference.","PeriodicalId":405340,"journal":{"name":"2002 IEEE International Conference on Data Mining, 2002. Proceedings.","volume":"76 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":"121580344","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: 45
Investigative profiling with computer forensic log data and association rules 使用计算机取证日志数据和关联规则进行调查分析
2002 IEEE International Conference on Data Mining, 2002. Proceedings. Pub Date : 2002-12-09 DOI: 10.1109/ICDM.2002.1183880
Tamas Abraham, O. Vel
{"title":"Investigative profiling with computer forensic log data and association rules","authors":"Tamas Abraham, O. Vel","doi":"10.1109/ICDM.2002.1183880","DOIUrl":"https://doi.org/10.1109/ICDM.2002.1183880","url":null,"abstract":"Investigative profiling is an important activity in computer forensics that can narrow the search for one or more computer perpetrators. Data mining is a technique that has produced good results in providing insight into large volumes of data. This paper describes how the association rule data mining technique may be employed to generate profiles from log data and the methodology used for the interpretation of the resulting rule sets. The process relies on background knowledge in the form of concept hierarchies and beliefs, commonly available from, or attainable by, the computer forensic investigative team. Results obtained with the profiling system has identified irregularities in computer logs.","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":"114169089","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: 72
Mining case bases for action recommendation 为行动建议挖掘案例基础
2002 IEEE International Conference on Data Mining, 2002. Proceedings. Pub Date : 2002-12-09 DOI: 10.1109/ICDM.2002.1183997
Qiang Yang, Hong Cheng
{"title":"Mining case bases for action recommendation","authors":"Qiang Yang, Hong Cheng","doi":"10.1109/ICDM.2002.1183997","DOIUrl":"https://doi.org/10.1109/ICDM.2002.1183997","url":null,"abstract":"Corporations and institutions are often interested in deriving marketing strategies from corporate data and providing informed advice for their customers or employees. For example, a financial institution may derive marketing strategies for turning their reluctant customers into active ones and a telecommunications company may plan actions to stop their valuable customers from leaving. In data mining terms, these advice and action plans are aimed at converting individuals from an undesirable class to a desirable one, or to help devising a direct-marketing plan in order to increase the profit for the institution. We present an approach which uses 'role models' for generating such advice and plans. These role models are typical cases that form a case base and can be used for customer advice generation. For each new customer seeking advice, a nearest-neighbor algorithm is used to find a cost-effective and highly probable plan for switching a customer to the most desirable role models. We explore the tradeoff among time, space and quality of computation in this case-based reasoning framework. We demonstrate the effectiveness of the methods through empirical results.","PeriodicalId":405340,"journal":{"name":"2002 IEEE International Conference on Data Mining, 2002. Proceedings.","volume":"105 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":"123801070","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: 33
Mining molecular fragments: finding relevant substructures of molecules 挖掘分子片段:寻找分子的相关亚结构
2002 IEEE International Conference on Data Mining, 2002. Proceedings. Pub Date : 2002-12-09 DOI: 10.1109/ICDM.2002.1183885
C. Borgelt, M. Berthold
{"title":"Mining molecular fragments: finding relevant substructures of molecules","authors":"C. Borgelt, M. Berthold","doi":"10.1109/ICDM.2002.1183885","DOIUrl":"https://doi.org/10.1109/ICDM.2002.1183885","url":null,"abstract":"We present an algorithm to find fragments in a set of molecules that help to discriminate between different classes of for instance, activity in a drug discovery context. Instead of carrying out a brute-force search, our method generates fragments by embedding them in all appropriate molecules in parallel and prunes the search tree based on a local order of the atoms and bonds, which results in substantially faster search by eliminating the need for frequent, computationally expensive reembeddings and by suppressing redundant search. We prove the usefulness of our algorithm by demonstrating the discovery of activity-related groups of chemical compounds in the well-known National Cancer Institute's HIV-screening dataset.","PeriodicalId":405340,"journal":{"name":"2002 IEEE International Conference on Data Mining, 2002. Proceedings.","volume":"116 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":"124241742","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: 499
Adaptive parallel sentences mining from web bilingual news collection 基于web双语新闻的自适应平行句挖掘
2002 IEEE International Conference on Data Mining, 2002. Proceedings. Pub Date : 2002-12-09 DOI: 10.1109/ICDM.2002.1184044
B. Zhao, S. Vogel
{"title":"Adaptive parallel sentences mining from web bilingual news collection","authors":"B. Zhao, S. Vogel","doi":"10.1109/ICDM.2002.1184044","DOIUrl":"https://doi.org/10.1109/ICDM.2002.1184044","url":null,"abstract":"In this paper a robust, adaptive approach for mining parallel sentences from a bilingual comparable news collection is described Sentence length models and lexicon-based models are combined under a maximum likelihood criterion. Specific models are proposed to handle insertions and deletions that are frequent in bilingual data collected from the web. The proposed approach is adaptive, updating the translation lexicon iteratively using the mined parallel data to get better vocabulary coverage and translation probability parameter estimation. Experiments are carried out on 10 years of Xinhua bilingual news collection. Using the mined data, we get significant improvement in word-to-word alignment accuracy in machine translation modeling.","PeriodicalId":405340,"journal":{"name":"2002 IEEE International Conference on Data Mining, 2002. Proceedings.","volume":"70 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":"123527745","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: 132
ESRS: a case selection algorithm using extended similarity-based rough sets ESRS:一种使用扩展的基于相似度的粗糙集的案例选择算法
2002 IEEE International Conference on Data Mining, 2002. Proceedings. Pub Date : 2002-12-09 DOI: 10.1109/ICDM.2002.1184010
Liqiang Geng, Howard J. Hamilton
{"title":"ESRS: a case selection algorithm using extended similarity-based rough sets","authors":"Liqiang Geng, Howard J. Hamilton","doi":"10.1109/ICDM.2002.1184010","DOIUrl":"https://doi.org/10.1109/ICDM.2002.1184010","url":null,"abstract":"A case selection algorithm selects representative cases from a large data set for future case-based reasoning tasks. This paper proposes the ESRS algorithm, based on extended similarity-based rough set theory, which selects a reasonable number of the representative cases while maintaining satisfactory classification accuracy. It also can handle noise and inconsistent data. Experimental results on synthetic and real sets of cases showed that its predictive accuracy is similar to that of well-known machine learning systems on standard data sets, while it has the advantage of being applicable to any data set where a similarity function can be defined.","PeriodicalId":405340,"journal":{"name":"2002 IEEE International Conference on Data Mining, 2002. Proceedings.","volume":"41 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":"128574619","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
Discovering frequent geometric subgraphs 发现频繁几何子图
2002 IEEE International Conference on Data Mining, 2002. Proceedings. Pub Date : 2002-12-09 DOI: 10.1109/ICDM.2002.1183911
Michihiro Kuramochi, G. Karypis
{"title":"Discovering frequent geometric subgraphs","authors":"Michihiro Kuramochi, G. Karypis","doi":"10.1109/ICDM.2002.1183911","DOIUrl":"https://doi.org/10.1109/ICDM.2002.1183911","url":null,"abstract":"As data mining techniques are being increasingly applied to non-traditional domains, existing approaches for finding frequent itemsets cannot be used as they cannot model the requirement of these domains. An alternate way of modeling the objects in these data sets, is to use a graph to model the database objects. Within that model, the problem of finding frequent patterns becomes that of discovering subgraphs that occur frequently over the entire set of graphs. We present a computationally efficient algorithm for finding frequent geometric subgraphs in a large collection of geometric graphs. Our algorithm is able to discover geometric subgraphs that can be rotation, scaling and translation invariant, and it can accommodate inherent errors on the coordinates of the vertices. Our experimental results show that our algorithms require relatively little time, can accommodate low support values, and scale linearly on the number of transactions.","PeriodicalId":405340,"journal":{"name":"2002 IEEE International Conference on Data Mining, 2002. Proceedings.","volume":"39 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":"121684468","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: 96
Convex Hull Ensemble Machine 凸壳集成机
2002 IEEE International Conference on Data Mining, 2002. Proceedings. Pub Date : 2002-12-09 DOI: 10.1109/ICDM.2002.1183909
Yongdai Kim
{"title":"Convex Hull Ensemble Machine","authors":"Yongdai Kim","doi":"10.1109/ICDM.2002.1183909","DOIUrl":"https://doi.org/10.1109/ICDM.2002.1183909","url":null,"abstract":"We propose a new ensemble algorithm called Convex Hull Ensemble Machine (CHEM). CHEM in Hilbert space is developed first and it is modified to regression and classification problems. Empirical studies show that in classification problems CHEM has similar prediction accuracy as AdaBoost, but CHEM is much more robust to output noise. In regression problems, CHEM works competitively with other ensemble methods such as Gradient Boost and Bagging.","PeriodicalId":405340,"journal":{"name":"2002 IEEE International Conference on Data Mining, 2002. Proceedings.","volume":"19 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":"126518954","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
Extraction techniques for mining services from Web sources 用于从Web源挖掘服务的提取技术
2002 IEEE International Conference on Data Mining, 2002. Proceedings. Pub Date : 2002-12-09 DOI: 10.1109/ICDM.2002.1184008
H. Davulcu, Saikat Mukherjee, I. Ramakrishnan
{"title":"Extraction techniques for mining services from Web sources","authors":"H. Davulcu, Saikat Mukherjee, I. Ramakrishnan","doi":"10.1109/ICDM.2002.1184008","DOIUrl":"https://doi.org/10.1109/ICDM.2002.1184008","url":null,"abstract":"The Web has established itself as the dominant medium for doing electronic commerce. Consequently the number of service providers, both large and small, advertising their services on the web continues to proliferate. In this paper we describe new extraction algorithms for mining service directories from web pages. We develop a novel propagation technique for identifying and accumulating all of the attributes related to a service entity in a web page. We provide experimental results of the effectiveness of our extraction techniques by mining a database of veterinarian service providers from web sources.","PeriodicalId":405340,"journal":{"name":"2002 IEEE International Conference on Data Mining, 2002. Proceedings.","volume":"29 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":"130655933","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: 9
Learning with progressive transductive Support Vector Machine 渐进式转换支持向量机学习
2002 IEEE International Conference on Data Mining, 2002. Proceedings. Pub Date : 2002-12-09 DOI: 10.1109/ICDM.2002.1183887
Yisong Chen, Guoping Wang, Shihai Dong
{"title":"Learning with progressive transductive Support Vector Machine","authors":"Yisong Chen, Guoping Wang, Shihai Dong","doi":"10.1109/ICDM.2002.1183887","DOIUrl":"https://doi.org/10.1109/ICDM.2002.1183887","url":null,"abstract":"Support Vector Machine (SVM) is a new learning method developed in recent years based on the foundations of statistical learning theory. By taking a transductive approach instead of an inductive one in support vector classifiers, the test set can be used as an additional source of information about margins. Intuitively, we would expect transductive learning to yield improvements when the training sets are small or when there is a significant deviation between the training and working set subsamples of the total population. In this paper, a progressive transductive support vector machine is addressed to extend Joachims' Transductive SVM to handle different class distributions. It solves the problem of having to estimate the ratio of positive/negative examples from the working set. The experimental results show that the algorithm is very promising.","PeriodicalId":405340,"journal":{"name":"2002 IEEE International Conference on Data Mining, 2002. Proceedings.","volume":"20 8 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":"130884734","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: 168
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