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

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A Data Mining Approach to Classify Credit Cardholders' Behavior 信用卡持卡人行为分类的数据挖掘方法
Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06) Pub Date : 2006-12-18 DOI: 10.1109/ICDMW.2006.4
Ai-hua Li, Yong Shi, Meihong Zhu, J. Dai
{"title":"A Data Mining Approach to Classify Credit Cardholders' Behavior","authors":"Ai-hua Li, Yong Shi, Meihong Zhu, J. Dai","doi":"10.1109/ICDMW.2006.4","DOIUrl":"https://doi.org/10.1109/ICDMW.2006.4","url":null,"abstract":"Credit cardholders' behavior analysis is an important issue to be studied. Multi-criteria linear programming (MCLP) classification method has shown its advantage in fast speed and balanced classification accuracy on this problem. However, dimension reduction is necessary before some classification methods implementing, not only for faster classification speed but also for commercial knowledge discovering. In this paper, a data mining approach based on the combination of MCLP and principal component analysis (PCA) is proposed, and the influence of PCA on MCLP classification method is studied. One dataset, which comes from a bank in US, is used to test the performance of this approach, and the advantage of this classification method is shown by experiments","PeriodicalId":291862,"journal":{"name":"Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06)","volume":"30 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":"125663873","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
Mining the Future: Predicting Itemsets' Support of Association Rules Mining 挖掘未来:预测项目集对关联规则挖掘的支持
Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06) Pub Date : 2006-12-18 DOI: 10.1109/ICDMW.2006.116
Shenoda Guirguis, K. M. Ahmed, Nagwa M. El-Makky, Alaaeldin M. Hafez
{"title":"Mining the Future: Predicting Itemsets' Support of Association Rules Mining","authors":"Shenoda Guirguis, K. M. Ahmed, Nagwa M. El-Makky, Alaaeldin M. Hafez","doi":"10.1109/ICDMW.2006.116","DOIUrl":"https://doi.org/10.1109/ICDMW.2006.116","url":null,"abstract":"This paper proposes a novel research dimension in the field of data mining, which is mining the future data before its arrival, or in other words: predicting association rules ahead before the arrival of the data. To achieve that, we need only predict the itemsets' support, upon which association rules could be easily produced. A time series analysis approach (MFTP) is proposed to perform itemsets' support prediction task. The proposed technique outperforms other prediction techniques for short history. The conducted performance study showed good prediction accuracy and response time. Thus, we provide a new tool to provide more information in the decision support field","PeriodicalId":291862,"journal":{"name":"Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06)","volume":"1 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":"130414225","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
Reducing performance Bias for Unbalanced Text Mining 减少不平衡文本挖掘的性能偏差
Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06) Pub Date : 2006-12-18 DOI: 10.1109/ICDMW.2006.139
L. Zhuang, H. Dai
{"title":"Reducing performance Bias for Unbalanced Text Mining","authors":"L. Zhuang, H. Dai","doi":"10.1109/ICDMW.2006.139","DOIUrl":"https://doi.org/10.1109/ICDMW.2006.139","url":null,"abstract":"In text categorization applications, class imbalance, which refers to an uneven data distribution where one class is represented by far more less instances than the others, is a commonly encountered problem. In such a situation, conventional classifiers tend to have a strong performance bias, which results in high accuracy rate on the majority class but very low rate on the minorities. An extreme strategy for unbalanced, learning is to discard the majority instances and apply one-class classification to the minority class. However, this could easily cause another type of bias, which increases the accuracy rate on minorities by sacrificing the majorities. This paper aims to investigate approaches that reduce these two types of performance bias and improve the reliability of discovered classification rules. Experimental results show that the inexact field learning method and parameter optimized one-class classifiers achieve more balanced performance than the standard approaches","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":"124613819","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: 4
A Mixed Process Neural Network and its Application to Churn Prediction in Mobile Communications 混合过程神经网络及其在移动通信客户流失预测中的应用
Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06) Pub Date : 2006-12-18 DOI: 10.1109/ICDMW.2006.12
Guojie Song, Dongqing Yang, Lingjing Wu, Tengjiao Wang, Shiwei Tang
{"title":"A Mixed Process Neural Network and its Application to Churn Prediction in Mobile Communications","authors":"Guojie Song, Dongqing Yang, Lingjing Wu, Tengjiao Wang, Shiwei Tang","doi":"10.1109/ICDMW.2006.12","DOIUrl":"https://doi.org/10.1109/ICDMW.2006.12","url":null,"abstract":"Churn prediction is an increasingly pressing issue in today's ever-competitive commercial environments, especially in mobile communication arena. In this paper, a mixed process neural network (MPNN) based on Fourier orthogonal base function has been proposed to support churn management, which can deal with both static value and time-varied continuous value simultaneously. To further improve its performance, an optimized network, c-MPNN, has been presented, which adopts Fourier expansion based preprocessing and hidden layer combination techniques to optimize MPNN's structure. Most important of all, our method has been used in real applications in China Mobile. Experiments based on the real datasets also show that our proposed churn prediction method has good maneuverability and performance","PeriodicalId":291862,"journal":{"name":"Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06)","volume":"177 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120991708","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: 29
Dynamic Algorithm for Graph Clustering Using Minimum Cut Tree 基于最小切树的动态图聚类算法
Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06) Pub Date : 2006-12-18 DOI: 10.1109/ICDMW.2006.65
B. Saha, Pabitra Mitra
{"title":"Dynamic Algorithm for Graph Clustering Using Minimum Cut Tree","authors":"B. Saha, Pabitra Mitra","doi":"10.1109/ICDMW.2006.65","DOIUrl":"https://doi.org/10.1109/ICDMW.2006.65","url":null,"abstract":"In this paper we introduce a dynamic algorithm for clustering undirected graphs, whose edge property is continuously changing. The algorithm can maintain high-quality clusters efficiently in presence of insertion and deletion (update) of edges. The algorithm is motivated by the minimum-cut tree based partitioning algorithm presented in G. W. Flake et al. (2004) and G. W. Flake (2000). It takes O(k3) time for each update processing, where k is the maximum size of any cluster. This is the worst case time complexity, and in general time taken is much less. To the best of our knowledge, this is the first clustering algorithm, for evolving graphs, providing strong theoretical quality guarantee on clusters","PeriodicalId":291862,"journal":{"name":"Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06)","volume":"49 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":"129910711","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: 30
A Graph-Theoretic Method for Mining Functional Modules in Large Sparse Protein Interaction Networks 大型稀疏蛋白质相互作用网络中功能模块挖掘的图论方法
Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06) Pub Date : 2006-12-18 DOI: 10.1109/ICDMW.2006.5
Shihua Zhang, Hong-Wei Liu, Xue-Mei Ning, Xiang-Sun Zhang
{"title":"A Graph-Theoretic Method for Mining Functional Modules in Large Sparse Protein Interaction Networks","authors":"Shihua Zhang, Hong-Wei Liu, Xue-Mei Ning, Xiang-Sun Zhang","doi":"10.1109/ICDMW.2006.5","DOIUrl":"https://doi.org/10.1109/ICDMW.2006.5","url":null,"abstract":"With ever increasing amount of available data on protein-protein interaction (PPI) networks, understanding the topology of the networks and then biochemical processes in cells has become a key problem. Modular architecture which encompasses groups of genes/proteins involved in elementary biological functional units is a basic form of the organization of interacting proteins. Here we propose a method that combines the line graph transformation and clique percolation clustering algorithm to detect network modules which may overlap each other in large sparse protein-protein interaction (PPI) networks. The resulting modules by the present method show a high coverage among yeast, fly, and worm PPI networks respectively. Our analysis of the yeast PPI network suggests that most of these modules have well biological significance in context of protein localization, function annotation, and protein complexes","PeriodicalId":291862,"journal":{"name":"Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06)","volume":"27 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":"116432324","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: 10
Understanding theWeb Page Layout 了解网页布局
Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06) Pub Date : 2006-12-18 DOI: 10.1109/ICDMW.2006.163
Minghong Zhou, Rubao Li, Wei Li
{"title":"Understanding theWeb Page Layout","authors":"Minghong Zhou, Rubao Li, Wei Li","doi":"10.1109/ICDMW.2006.163","DOIUrl":"https://doi.org/10.1109/ICDMW.2006.163","url":null,"abstract":"Web pages express their semantics not only by free texts, but also by their layouts. While information is explicitly encoded in free texts, the layout implicitly uncovers the semantical relationships of the free texts. In this paper, we proposed a framework for mining the semantics implied by the layout. The core of our work is a new HTML document model, called nested table model, which synthesize the DOM model and the syntax of HTML language. By the nested table model, we could formally define the relevancy of free texts. And hence, free texts could be grouped by their relevancy. Our experiment results indicate that the relevancy correctly reflects the semantics of Web page layout","PeriodicalId":291862,"journal":{"name":"Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06)","volume":"10 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":"134633727","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
HClustream: A Novel Approach for Clustering Evolving Heterogeneous Data Stream HClustream:异构数据流聚类演化的新方法
Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06) Pub Date : 2006-12-18 DOI: 10.1109/ICDMW.2006.89
Chunyu Yang, Jie Zhou
{"title":"HClustream: A Novel Approach for Clustering Evolving Heterogeneous Data Stream","authors":"Chunyu Yang, Jie Zhou","doi":"10.1109/ICDMW.2006.89","DOIUrl":"https://doi.org/10.1109/ICDMW.2006.89","url":null,"abstract":"Recently, the continuously arriving and evolving data stream has become a common phenomenon in many fields, such as sensor networks, Web click stream and Internet traffic flow. One of the most important mining tasks is clustering. Clustering has attracted extensive research by both the community of machine learning and data mining. Many stream clustering methods have been proposed. These methods have proven to be efficient on specific problems. However, most of these methods are on continuous clustering and few of them are about to solve the heterogeneous clustering problems. In this paper, we propose a novel approach based on the CluStream framework for clustering data stream with heterogeneous features. The centroid of continuous attributes and the histogram of the discrete attributes are used to represent the micro clusters, and k-prototype clustering algorithm is used to create the micro clusters and macro clusters. Experimental results on both synthetic and real data sets show its efficiency","PeriodicalId":291862,"journal":{"name":"Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06)","volume":"58 18","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133323222","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: 36
An Improved Feature Representation Method for Maximum Entropy Model 一种改进的最大熵模型特征表示方法
Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06) Pub Date : 2006-12-18 DOI: 10.1109/ICDMW.2006.29
Yi Guan, Jian Zhao
{"title":"An Improved Feature Representation Method for Maximum Entropy Model","authors":"Yi Guan, Jian Zhao","doi":"10.1109/ICDMW.2006.29","DOIUrl":"https://doi.org/10.1109/ICDMW.2006.29","url":null,"abstract":"In maximum entropy model (MEM), features are typically represented by either 0-1 binary-valued function or real-valued function. However, both representations only examine the impact of specific value of some attributes but not their types. Such negligence not only causes the decreasing of classification precision, but also slows the convergence speed of the generalized iterative scaling (GIS) algorithm, as more apparent to incomplete data. In this paper, an improved feature representation method is presented. The feature is composed of two parts: the first one is for specific value of an attribute; the second one is for the type of corresponding attribute. The experimental results on Mushroom dataset of UCI data repository showed that the average classifying precisions on incomplete dataset and complete dataset were improved by 1.5% and 3.0% respectively, and the average convergence speed was improved by 42.9% and 90.7% respectively","PeriodicalId":291862,"journal":{"name":"Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06)","volume":"56 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114060284","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: 1
Knowledge Discovery across Documents through Concept Chain Queries 通过概念链查询跨文档的知识发现
Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06) Pub Date : 2006-12-18 DOI: 10.1109/ICDMW.2006.105
Wei Jin, R. Srihari
{"title":"Knowledge Discovery across Documents through Concept Chain Queries","authors":"Wei Jin, R. Srihari","doi":"10.1109/ICDMW.2006.105","DOIUrl":"https://doi.org/10.1109/ICDMW.2006.105","url":null,"abstract":"This paper focuses on detecting links between two concepts across text documents (e.g. two persons). We interpret such a query as finding the most meaningful evidence trail across documents that connect these two concepts. Here we propose a fast and efficient algorithm to perform this task. It is based on the idea of hypothesis generation originated by Swanson called \"complementary structures in disjoint literatures\" (CSD). We adapted the technique by (i) developing an alternate method of generating semantic profiles and (ii) extending the technique to generate concept chains. Counterterrorism corpus is used to evaluate the performance of this approach and demonstrates the effectiveness of our algorithm","PeriodicalId":291862,"journal":{"name":"Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06)","volume":"221 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":"114744719","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: 18
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