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

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NNMF-Based Factorization Techniques for High-Accuracy Privacy Protection on Non-negative-valued Datasets 基于nnmf的非负值数据集高精度隐私保护分解技术
Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06) Pub Date : 2006-12-18 DOI: 10.1109/ICDMW.2006.123
Jie Wang, Weijun Zhong, Jun Zhang
{"title":"NNMF-Based Factorization Techniques for High-Accuracy Privacy Protection on Non-negative-valued Datasets","authors":"Jie Wang, Weijun Zhong, Jun Zhang","doi":"10.1109/ICDMW.2006.123","DOIUrl":"https://doi.org/10.1109/ICDMW.2006.123","url":null,"abstract":"The challenge in preserving data privacy is how to protect attribute values without jeopardizing the similarity between data objects under analysis. In this paper, we further our previous work on applying matrix techniques to protect privacy and present a novel algebraic technique based on iterative methods for non-negative-valued data distortion. As an unsupervised learning method for uncovering latent features in high-dimensional data, a low rank nonnegative matrix factorization (NNMF) is used to preserve natural data non-negativity and avoid subtractive basis vector and encoding interactions present in techniques such as principal component analysis. It is the first in privacy preserving data mining in our paper that combining non-negative matrix decomposition with distortion processing. Two iterative methods to solve bound-constrained optimization problem in NMF are compared by experiments on Wisconsin Breast Cancer Dataset. The overall performance of NMF on distortion level and data utility is compared to our previously-proposed SVD-based distortion strategies and other existing popular data perturbation methods. Data utility is examined by cross validation of a binary classification using the support vector machine. Our experimental results on data mining benchmark datasets indicate that, in comparison with standard data distortion techniques, the proposed NMF-based method are very efficient in balancing data privacy and data utility, and it affords a feasible solution with a good promise on high-accuracy privacy preserving data mining","PeriodicalId":291862,"journal":{"name":"Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06)","volume":"9 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":"117077029","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: 64
A New Algorithm for Mining Fuzzy Association Rules in the Large Databases Based on Ontology 基于本体的大型数据库模糊关联规则挖掘新算法
Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06) Pub Date : 2006-12-18 DOI: 10.1109/ICDMW.2006.16
Zahra Farzanyar, M. Kangavari, S. Hashemi
{"title":"A New Algorithm for Mining Fuzzy Association Rules in the Large Databases Based on Ontology","authors":"Zahra Farzanyar, M. Kangavari, S. Hashemi","doi":"10.1109/ICDMW.2006.16","DOIUrl":"https://doi.org/10.1109/ICDMW.2006.16","url":null,"abstract":"Association rule mining is an active data mining research area. Recent years have witnessed many efforts on discovering fuzzy associations. The key strength of fuzzy association rule mining is its completeness. This strength, however, comes with a major drawback to handle large datasets. It often produces a huge number of candidate itemsets. The huge number of candidate itemsets makes it ineffective for a data mining system to analyze them. To overcome this problem in this study, fuzzy association rule mining system is driven by domain ontology. It describes the use of a concept hierarchy for improving the results of fuzzy association rule mining. Our ontology-based data mining algorithm makes the rules more visual, more interesting and more understandable. At last the paper, the efficiency and advantages of this algorithm has been approved by experimental results","PeriodicalId":291862,"journal":{"name":"Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06)","volume":"77 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":"125746133","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: 17
Incremental Mining of Sequential Patterns over a Stream Sliding Window 流滑动窗口上顺序模式的增量挖掘
Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06) Pub Date : 2006-12-18 DOI: 10.1109/ICDMW.2006.98
Chin-Chuan Ho, Hua-Fu Li, Fang-Fei Kuo, Suh-Yin Lee
{"title":"Incremental Mining of Sequential Patterns over a Stream Sliding Window","authors":"Chin-Chuan Ho, Hua-Fu Li, Fang-Fei Kuo, Suh-Yin Lee","doi":"10.1109/ICDMW.2006.98","DOIUrl":"https://doi.org/10.1109/ICDMW.2006.98","url":null,"abstract":"Incremental mining of sequential patterns from data streams is one of the most challenging problems in mining data streams. However, previous work of mining sequential patterns from data streams is almost focused on mining of patterns from stream of item-sequences, not stream of itemset-sequences. In this paper, we propose an efficient single-pass algorithm, called IncSPAM, to maintain the set of sequential patterns from itemset-sequence streams with a transaction-sensitive sliding window. An effective bit-sequence representation of items is used in the proposed algorithm to reduce the time and memory needed to slide the windows. Experiments show that the proposed IncSPAM algorithm is efficient for mining sequential patterns over data streams","PeriodicalId":291862,"journal":{"name":"Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06)","volume":"42 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":"124848691","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: 56
Modeling Dynamic Substate Chains among Massive States for Prediction 海量状态间动态子状态链的建模与预测
Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06) Pub Date : 2006-12-18 DOI: 10.1109/ICDMW.2006.118
V. Nguyen, T. Washio
{"title":"Modeling Dynamic Substate Chains among Massive States for Prediction","authors":"V. Nguyen, T. Washio","doi":"10.1109/ICDMW.2006.118","DOIUrl":"https://doi.org/10.1109/ICDMW.2006.118","url":null,"abstract":"Along the development of ubiquitous sensing technologies, the opportunity to have transaction time series data is increasing. We propose a novel framework named HISC (HIgh-order Substate Chain) modeling to predict the dynamic system behaviors based on the transaction time series which contains explosive states due to the combinatorics of massive sensors and their output values with noise. Its significant performance has been confirmed through the comparisons with high-order Markov chain models and the application to practical data analysis","PeriodicalId":291862,"journal":{"name":"Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06)","volume":"280 6 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":"127505773","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
Designing and Evaluating an Index for Graph Structured Data 图结构化数据索引的设计与评价
Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06) Pub Date : 2006-12-18 DOI: 10.1109/ICDMW.2006.58
S. Barton, P. Zezula
{"title":"Designing and Evaluating an Index for Graph Structured Data","authors":"S. Barton, P. Zezula","doi":"10.1109/ICDMW.2006.58","DOIUrl":"https://doi.org/10.1109/ICDMW.2006.58","url":null,"abstract":"In this paper we present own design of an indexing structure for general graph structured data called p-index that allows an effective processing of special path queries. These special queries represent for example a search for all paths lying between two arbitrary vertices limited to a certain path length. The p-index is a multilevel balanced tree structure where each node is created with a certain graph transformation and described by modified adjacency matrix. Hence, p-index indexes all the paths to a predefined length I inclusive. The search algorithm is then able to find all the paths shorter than or having the length l and some of the paths longer then the predefined l lying between any two vertices in the indexed graph. We also present an experimental evaluation of the process of creating the p-index on graphs with different sizes and also a complexity evaluation of the search algorithm that uses the p-index","PeriodicalId":291862,"journal":{"name":"Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06)","volume":"12 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":"127895340","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 RBF classifier with supervised center selection and weighted norm 具有监督中心选择和加权范数的RBF分类器
Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06) Pub Date : 2006-12-18 DOI: 10.1109/ICDMW.2006.20
Rong Liu, Yong Shi
{"title":"A RBF classifier with supervised center selection and weighted norm","authors":"Rong Liu, Yong Shi","doi":"10.1109/ICDMW.2006.20","DOIUrl":"https://doi.org/10.1109/ICDMW.2006.20","url":null,"abstract":"A variation of RBF (radial basis function) classifier based on supervised (or adaptively) center selection and weighted norm is formulated and then tested in labeling overlapped data sets generated from two binormal distributions in this paper. Besides its simplicity, the experimental results and the comparisons to other classifiers suggest that this method is significant in capability owing to offering reasonable classifications with mild computation costs. Moreover, this variation outperforms the RBF based on exact interpolation not just in accuracy but in efficiency as well","PeriodicalId":291862,"journal":{"name":"Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06)","volume":"12 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":"126838762","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: 3
Enhancing Reliability throughout Knowledge Discovery Process 提高知识发现过程的可靠性
Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06) Pub Date : 2006-12-18 DOI: 10.1109/ICDMW.2006.70
Yi Feng, Zhaohui Wu
{"title":"Enhancing Reliability throughout Knowledge Discovery Process","authors":"Yi Feng, Zhaohui Wu","doi":"10.1109/ICDMW.2006.70","DOIUrl":"https://doi.org/10.1109/ICDMW.2006.70","url":null,"abstract":"Reliability is a key issue in knowledge discovery. However, this topic is not fully explored in data mining community. This paper takes a process perspective towards the reliability of knowledge discovery, and the reliability of extracted knowledge is evaluated by the reliability of whole knowledge discovery process. To describe the relationship between the final reliability and the reliability in each stage of process, a reliability model for generic knowledge process is proposed, and is further extended to the context of cross-industry standard process for data mining (CRISP-DM). Moreover, eight factors contributing to knowledge discovery reliability are presented in the order of six phases in CRISP-DM. Based on these factors, ten suggestions on how to enhance reliability throughout knowledge discovery process are provided","PeriodicalId":291862,"journal":{"name":"Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06)","volume":"23 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":"125221434","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: 21
Association Bundle - A New Pattern for Association Analysis 关联包——一种新的关联分析模式
Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06) Pub Date : 2006-12-18 DOI: 10.1109/ICDMW.2006.33
Wenxue Huang, M. Krneta, Limin Lin, Jianhong Wu
{"title":"Association Bundle - A New Pattern for Association Analysis","authors":"Wenxue Huang, M. Krneta, Limin Lin, Jianhong Wu","doi":"10.1109/ICDMW.2006.33","DOIUrl":"https://doi.org/10.1109/ICDMW.2006.33","url":null,"abstract":"In this paper we introduce the notion of association bundle as a new pattern for association analysis. Different from association rule which has the one-way association from rule antecedent to rule consequent, an association bundle has the two-way association between each pair of bundle elements. Association bundles present a more compact association pattern than association rules, and have meaningful interpretations in marketing applications including shelf arrangement and cross-selling by discounting. The identification of association bundles can be transformed into the problem of maximal clique enumeration, and very efficient algorithms can be applied. Our experimental results show that the time cost is less than two minutes on three real world data sets","PeriodicalId":291862,"journal":{"name":"Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06)","volume":"32 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":"122956292","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: 6
Chance Discovery in Credit Risk Management 信用风险管理中的机会发现
Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06) Pub Date : 2006-12-18 DOI: 10.1109/ICDMW.2006.40
Shinichi Goda, Y. Ohsawa
{"title":"Chance Discovery in Credit Risk Management","authors":"Shinichi Goda, Y. Ohsawa","doi":"10.1109/ICDMW.2006.40","DOIUrl":"https://doi.org/10.1109/ICDMW.2006.40","url":null,"abstract":"Credit risk management based on portfolio theory becomes popular in recent Japanese financial industry. But consideration and modeling of chain reaction bankruptcy effect in credit portfolio analysis leave much room for improvement even though the importance of the effect is recognized among credit analysis experts. That is partly because method for grasping relations among companies with limited data is underdeveloped. In this article, chance discovery method is applied to estimate structure of industrial relations that are to include companies' relations that transmit chain reaction of bankruptcy. The steps for the data analysis is introduced and result of example analysis with Japanese default events data of 2005 is presented","PeriodicalId":291862,"journal":{"name":"Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06)","volume":"34 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":"132847901","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
Modeling Multiple Time Units Delayed Gene Regulatory Network Using Dynamic Bayesian Network 基于动态贝叶斯网络的多时间单位延迟基因调控网络建模
Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06) Pub Date : 2006-12-18 DOI: 10.1109/ICDMW.2006.120
Zhengzheng Xing, Dan Wu
{"title":"Modeling Multiple Time Units Delayed Gene Regulatory Network Using Dynamic Bayesian Network","authors":"Zhengzheng Xing, Dan Wu","doi":"10.1109/ICDMW.2006.120","DOIUrl":"https://doi.org/10.1109/ICDMW.2006.120","url":null,"abstract":"Most of the current applications which use dynamic Bayesian network to model gene regulatory network assume that the time delay between regulators and their targets is one time unit in a time series gene expression dataset. In fact, multiple time units delay is indicated to exist in a gene regulation process. In this paper, we propose using higher-order Markov dynamic Bayesian network (DBN) to model multiple time units delayed gene regulatory network. A two steps heuristic learning framework is designed to learn higher-order Markov DBN from time series gene expression data. We apply the learning framework to a yeast cell cycle gene expression dataset. The predicted gene regulatory network is strongly supported by biological evidence and consistent with the yeast cell cycle phase information","PeriodicalId":291862,"journal":{"name":"Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06)","volume":"14 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":"122325221","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: 37
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