检索结果-内蒙古大学图书馆

Hiding Sensitive XML Association Rules With Supervised Learning Technique

Intelligent Information Management 2011年第6期3卷 219-229页

作者： Khalid Iqbal Dr. Sohail Asghar Dr. Abdulrehman Mirza Department of Computer Science Shaheed Zulfikar Ali Bhutto Institute of Science & Technology Center of Research in Data Engineering Mohammad Ali Jinnah University College of Computer and Information Sciences King Saud University

In the privacy preservation of association rules, sensitivity analysis should be reported after the quantification of items in terms of their occurrence. The traditional methodologies, used for preserving confidentiality of association rules, are based on the assumptions while safeguarding susceptible information rather than recognition of insightful items. Therefore, it is time to go one step ahead in order to remove such assumptions in the protection of responsive information especially in XML association rule mining. Thus, we focus on this central and highly researched area in terms of generating XML association rule mining without arguing on the disclosure risks involvement in such mining process. Hence, we described the identification of susceptible items in order to hide the confidential information through a supervised learning technique. These susceptible items show the high dependency on other items that are measured in terms of statistical significance with Bayesian Network. Thus, we proposed two methodologies based on items probabilistic occurrence and mode of items. Additionally, all this information is modeled and named PPDM (Privacy Preservation in data Mining) model for XARs. Furthermore, the PPDM model is helpful for sharing markets information among competitors with a lower chance of generating monopoly. Finally, PPDM model introduces great accuracy in computing sensitivity of items and opens new dimensions to the academia for the standardization of such NP-hard problems.

关键词： XML Document Association Rules Bayesian Network PPDM Model NP-Hard K2 Algorithm

来源：评论

学校读者我要写书评

暂无评论

Bayesian based subgroup discovery

Bayesian based subgroup discovery

引用

International Conference on Digital Information Management (ICDIM)

作者： Talha Anwar Sohail Asghar Simon Fong Center of Research in Data Engineering (CORDE) Mohammad Ali Jinnah University Islamabad Pakistan Department of Computer Science Mohammad Ali Jinnah University Islamabad Pakistan Department of Computer and Information Science University of Macau Macao China

data Mining is concerned with extraction of interesting patterns or knowledge from huge amounts of data. Generally data mining tasks are either predictive or descriptive. Classification falls under predictive induction while clustering and association rule mining fall under descriptive induction. Subgroup discovery is a task at the intersection of supervised learning and descriptive induction. In subgroup discovery we want to uncover individual patterns in data with a given property of interest. We want to find subgroups that cover a large population and are statistically different. The main application areas of subgroup discovery are exploration and descriptive induction, where the user wants to find the overview of dependencies between a target and many explaining variables. Many techniques have been proposed for discovering subgroups and some of these techniques are based on classification. But none of the techniques uses Bayesian networks for the generation of subgroups. Our contributions include a technique for the discovery of subgroups where the subgroups are generated using Bayesian networks.

关键词： Bayesian methods data mining databases Equations Accuracy Classification algorithms Mathematical model

来源：评论

学校读者我要写书评

暂无评论

Feature reduction using principal component analysis for agricultural data set

Feature reduction using principal component analysis for agr...

引用

International Conference on Electronic computer Technology

作者： Subhadra Mishra Debahuti Mishra Satyabrata Das Amiya Kumar Rath Department of Computer Sc. & Application Center for Post Graduate Studies OUAT Bhubaneswar India Institute of Technical Education & Research Siksha O Anusandhan University Bhubaneswar India Department of Computer Science and Engineering CEB Orissa India

Many applications like video surveillance, telecommunication, weather forecasting and sensor networks uses high volume of data of different types. The effective and efficient analysis of data in such different forms becomes a challenging task. Analysis of such large expression data gives rise to a number of new computational challenges not only due to the increase in number of data objects but also due to the increase in number of attributes. Hence, to improve the efficiency and accuracy of mining task on high dimensional data, the data must be preprocessed by an efficient dimensionality reduction method. In this paper, we have proposed to use the method of k-means clustering and principal component analysis (PCA) approach for attribute reduction, which initially apply PCA to obtain reduced uncorrelated attributes specifying maximal eigenvalues in the dataset with minimum loss of information. Then again we proposed to use k-means on the PCA reduced dataset to discover discriminative features that will be the most adequate ones for classification. This is a combination of clustering approach with feature reduction to obtain a minimal set attributes retaining a suitably high accuracy in representing the original features. We have used the Greengram agricultural data set. Finally, we found that the result of clustering is same after reducing the attributes using PCA.

关键词： Principal component analysis Feature extraction Synthetic aperture sonar Clustering algorithms data mining Algorithm design and analysis Agriculture

来源：评论

学校读者我要写书评

暂无评论

Special issue on the best papers of the Conference on Intelligent data Understanding (CIDU 2010)

引用

Statistical Analysis and data Mining 2011年第4期4卷 355-357页

作者： Srivastava, Ashok N. Chawla, Nitesh V. Intelligent Systems Division Intelligent Data Understanding Group NASA Ames Research Center Moffett Field CA United States Department of Computer Science and Engineering Interdisciplinary Center for Network Science and Applications University of Notre Dame South Bend IN United States

来源：评论

学校读者我要写书评

暂无评论

Passenger Search by Spatial Index for Ridesharing

Passenger Search by Spatial Index for Ridesharing

引用

International Conference on Technologies and applications of Artificial Intelligence (TAai)

作者： Chung-Wen Cho Yi-Hung Wu Chieh Yen Chun-Yen Chang Cloud Computing Center of Mobile Application Industrial Technology Research Institute Taiwan Department of Information & Computer Engineering Chung Yuan Christian University Taiwan Science Education Center National Taiwan Normal University Taiwan

Ridesharing has the great opportunity to reduce the consumption of energy and the emission of harmful gases, and to let people share the traffic costs with others. Most of the current ridesharing systems simply provide a number of candidates for users to choose. Time-consuming negotiation often discourages people from ridesharing. We propose a novel approach that assigns users to form ridesharing groups according to their routes and payments. Given a driver, our goal is to find a group of passengers who will pay the driver the most. Under the payment scheme, the passengers who share rides on the same route will equally share the expense with the driver. For the prompt response to an online system, our approach aims for the near-optimal group, where the available seats on the driver route are occupied by passengers as many as possible. Compared with the previous methods, the experiment results show that our approach incurs a little overhead but obtains answers of good quality, measured by the driver's saving, under various parameter settings.

关键词： Vehicles Cities and towns Estimation Google Educational institutions Indexes

来源：评论

学校读者我要写书评

暂无评论

Context-specific miRNA regulation network predicts cancer prognosis

Context-specific miRNA regulation network predicts cancer pr...

引用

IEEE International Conference on Systems Biology

作者： Xionghui Zhou Juan Liu Changning Liu Simon Rayner Fengji Liang Jingfang Ju Yinghui Li Shanguang Chen Jianghui Xiong School of Computer Science Wuhan University Wuhan China Bioinformatics Research Group Key Laboratory of Intelligent Information Processing Advanced Computing Research Laboratory Institute of Computing Technology Chinese Academy of Sciences (CAS) Beijing China Bioinformatics Group State Key Laboratory of Virology Wuhan Institute of Virology Chinese Academy of Sciences (CAS) Wuhan China Bioinformatics Group and Data Coordination Center State Key Laboratory of Space Medicine Fundamentals and Application China Astronaut Research and Training Center Beijing China Department of Pathology Stony Brook University Medical Center New York USA

MicroRNAs can regulate hundreds of target genes and play a pivotal role in a broad range of biological process. However, relatively little is known about how these highly connected miRNAs-target networks are remodelled in the context of various diseases. Here we examine the dynamic alteration of context-specific miRNA regulation to determine whether modified microRNAs regulation on specific biological processes is a useful information source for predicting cancer prognosis. A new concept, Context-specific miRNA activity (CoMi activity) is introduced to describe the statistical difference between the expression level of a miRNA's target genes and non-targets genes within a given gene set (context).

关键词： Breast cancer Context Correlation Biological processes Metastasis Tumors

来源：评论

学校读者我要写书评

暂无评论

Web usage mining: A survey on preprocessing of web log file

Web usage mining: A survey on preprocessing of web log file

引用

International Conference on Information and Emerging Technologies, ICIET

作者： Tasawar Hussain Sohail Asghar Nayyer Masood Center of Research in Data Engineering (CORDE Department of Computer Science Mohammad Ali Jinnah University Islamabad Pakistan

Web applications are increasing at an enormous speed and its users are increasing at exponential speed. The evolutionary changes in technology have made it possible to capture the users' essence and interactions with web applications through web server log file. Web log file is saved as text (.txt) file. Due to large amount of “irrelevant information” in the web log, the original log file can not be directly used in the web usage mining (WUM) procedure. Therefore the preprocessing of web log file becomes imperative. The proper analysis of web log file is beneficial to manage the web sites effectively for administrative and users' prospective. Web log preprocessing is initial necessary step to improve the quality and efficiency of the later steps of WUM. There are number of techniques available at preprocessing level of WUM. Different techniques are applied at preprocessing level such as data cleaning, data filtering, and data integration. In this paper, we will survey the preprocessing techniques to identify the issues and how WUM preprocessing can be improved for pattern mining and analysis.

关键词： Web sites data mining Cleaning IP networks Servers Filtering Browsers

来源：评论

学校读者我要写书评

暂无评论

On the security of a certificateless signature scheme

On the security of a certificateless signature scheme

引用

2010 2nd International Conference on Signal Processing Systems, ICSPS 2010

作者： Miao, Songqin Zhang, Futai Zhang, Lei School of Computer Science and Technology Nanjing Normal University Nanjing China Jiangsu Engineering Research Center on Information Security and Privacy Protection Technology Nanjing China UNESCO in Data Privacy Department of Computer Engineering and Mathematics Universitat Rovira i Virgili Av. Països Catalans 26 E-43007 Tarragona Catalonia Spain

ISBN: (纸本)9781424468911

Certificateless public key cryptography (CLPKC) eliminates certificate management in traditional public key infrastructure and solves the problem of the key escrow in identity-based cryptography. Certificateless signature is one of the most important primitives in CLPKC. Many certificateless signature (CLS) schemes have been proposed these years. For a CLS scheme to be secure, it should be resistant to the attacks of both Type I Adversary and Type II Adversary. In this paper, we give cryptanalysis to a recently proposed certificateless signature scheme. We show it is insecure against a Type II adversary who models a malicious-but-passive key generation center (KGC). An attack is described which reveals that a Type II adversary can successfully forge a certificateless signature of any signer upon obtaining two valid signatures of that signer. © 2010 IEEE.

关键词： Authentication

来源：评论

学校读者我要写书评

暂无评论

Hiding sensitive association rules using central tendency

Hiding sensitive association rules using central tendency

引用

International Conference on Advanced Information Management and Service

作者： Muhammad Naeem Sohail Asghar Simon Fong Center of Research in Data Engineering (CORDE) Mohammad Ali Jinnah University Islamabad Pakistan Department of Computer and Info. Science Faculty of Science and Technology University of Macau Macao China

Privacy Preserving in data Mining (PPDM) is a process by which certain sensitive information is hidden during data mining without precise access to original dataset. Majority of the techniques proposed in the literature for hiding sensitive information are based on using Support and Confidence measures in the association rules, which suffer from limitations. In this paper we propose a novel architecture which acquired other standard statistical measures instead of conventional framework of Support and Confidence to generate association rules. Specifically a weighing mechanism based on central tendency is introduced. The proposed architecture is tested with UCI datasets to hide the sensitive association rules as experimental evaluation. A performance comparison is made between the new technique and the existing one. The new architecture generates no ghost rules with complete avoidance of failure in hiding sensitive association rules. We demonstrate that Support and Confidence are not the only measures in hiding sensitive association rules. This research is aimed to contribute to data mining areas where privacy preservation is a concern.

关键词： Association rules Equations Mathematical model Algorithm design and analysis Itemsets

来源：评论

学校读者我要写书评

暂无评论

A hierarchical cluster based preprocessing methodology for Web Usage Mining

A hierarchical cluster based preprocessing methodology for W...

引用

International Conference on Advanced Information Management and Service

作者： Tasawar Hussain Sohail Asghar Simon Fong Center of Research in Data Engineering (CORDE) Faculty of Engineering and Applied Sciences Mohammad Ali Jinnah University Islamabad Pakistan Department of Computer and Information Science Faculty of Science and Technology University of Macau Macao China

In Web Usage Mining (WUM), web session clustering plays a key role to classify web visitors on the basis of user click history and similarity measure. Swarm based web session clustering helps in many ways to manage the web resources effectively such as web personalization, schema modification, website modification and web server performance. In this paper, we propose a framework for web session clustering at preprocessing level of web usage mining. The framework will cover the data preprocessing steps to prepare the web log data and convert the categorical web log data into numerical data. A session vector is obtained, so that appropriate similarity and swarm optimization could be applied to cluster the web log data. The hierarchical cluster based approach will enhance the existing web session techniques for more structured information about the user sessions.

关键词： Clustering algorithms data mining Cleaning Euclidean distance IP networks Particle swarm optimization Filtering algorithms

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：