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Intelligent Information and Database Systems, 2009. ACIIDS 2009. First Asian Conference on

Date 1-3 April 2009

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Displaying Results 1 - 25 of 97
  • [Front cover]

    Page(s): C1
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    Freely Available from IEEE
  • [Title page i]

    Page(s): i
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    Freely Available from IEEE
  • [Title page iii]

    Page(s): iii
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    Freely Available from IEEE
  • [Copyright notice]

    Page(s): iv
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    Freely Available from IEEE
  • Table of contents

    Page(s): v - xi
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    Freely Available from IEEE
  • Preface

    Page(s): xii - xiii
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    Freely Available from IEEE
  • Keynote speakers

    Page(s): xiv
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (68 KB)  

    Provides an abstract for each of the keynote presentations and a brief professional biography of each presenter. The complete presentations were not made available for publication as part of the conference proceedings. View full abstract»

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  • Conference organization

    Page(s): xv
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    Freely Available from IEEE
  • International Program Committee

    Page(s): xvi - xvii
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    Freely Available from IEEE
  • list-reviewer

    Page(s): xviii
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    Freely Available from IEEE
  • Intelligent Distributed Intrusion Detection Systems of Computer Communication Systems

    Page(s): 1 - 6
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (337 KB) |  | HTML iconHTML  

    The paper is devoted to present various intelligent, distributed network-based intrusion detection systems architectures and quality measures. Moreover an impact of network and their intrusion detection system architectures parameters on the intrusion detection systems quality is discussed. View full abstract»

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  • On Scheduling Problems with an Intelligent Use of the Learning Effect

    Page(s): 7 - 12
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (166 KB) |  | HTML iconHTML  

    This paper is devoted to scheduling problems with the learning effect, which is understood as a process of acquiring experience that increases the efficiency of a processor. To bring closer the considered phenomenon, a short survey on results concerning scheduling problems with the learning effect is provided. In particular, the existing models of the experience are presented along with a discussion on different shapes of the learning curve. Some complexity results of scheduling problems with the learning effect are also presented. We also show that scheduling problems with the learning effect model such problems as a minimization of a total transmission cost of packets in a computer network that uses a reinforcement learning routing algorithm. We also derive properties that allow us to construct scheduling algorithms, which can be applied in the computer network to increase its effectiveness by the utilization of its learning ability. View full abstract»

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  • A Novel Algorithm for Mining High Utility Itemsets

    Page(s): 13 - 17
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (217 KB) |  | HTML iconHTML  

    The utility based itemset mining approach has been discussed widely in recent years. There are many algorithms mining high utility itemsets by pruning candidates based on estimated utility values, and based on transaction-weighted utilization values. These algorithms aim to reduce search space. Besides, candidate pruning based on transaction-weighted utilization value is better than other strategies. In this paper, we propose TWU-Mining, a novel algorithm based-on WIT-tree for improving the cost of time and search space. Experiments show that the proposed algorithm is more effective on the testing databases. View full abstract»

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  • Argument Based Machine Learning from Examples and Text

    Page(s): 18 - 23
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (271 KB) |  | HTML iconHTML  

    We introduce a novel approach to cross-media learning based on argument based machine learning (ABML). ABML is a recent method that combines argumentation and machine learning from examples, and its main idea is to use arguments for some of the learning examples. Arguments are usually provided by a domain expert. In this paper, we present an alternative approach, where arguments used in ABML are automatically extracted from text with a technique for relation extraction. We demonstrate and evaluate the approach through a case study of learning to classify animals by using arguments automatically extracted from Wikipedia. View full abstract»

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  • A Structural Sampling Technique for Better Decision Trees

    Page(s): 24 - 27
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (280 KB) |  | HTML iconHTML  

    Since data mining problems contain a large amount of data, sampling is a necessity for the success of the task. Decision trees have been developed for prediction, and finding decision trees with smaller error rates has been a major task for their success. This paper suggests a structural sampling technique that is based on a generated decision tree, where the tree is generated based on fast and dirty tree generation algorithm. Experiments with several sample sizes and representative decision tree algorithms showed that the method is more effective with respect to decision tree size and error rate than conventional random sampling method especially for small sample size. View full abstract»

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  • Application of Transfer Regression to TCP Throughput Prediction

    Page(s): 28 - 33
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (341 KB) |  | HTML iconHTML  

    In this paper we present the application of the transform regression (TR) algorithm to predict file transfer time. We consider the files that are transferred from Web servers using HTTP protocol. We identify parameters having impact on file transfer time and use them as inputs to transform regression. We compare the results of TR-based prediction with the moving averages methods. View full abstract»

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  • Motif-Based Analysis of Social Position Influence on Interconnection Patterns in Complex Social Network

    Page(s): 34 - 39
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (541 KB) |  | HTML iconHTML  

    Motifs are small subgraphs showing statistically significant occurrence in given network. Motif analysis helps to insight into the local topology and functions of complex networks. The social position measure is interpreted as the importance of the node (user) within the network. We propose to fuse motif analysis with the social position assessment by colouring the nodes according to the measured position. As the distribution of discovered coloured motifs is utilized to mine the interconnection patterns between nodes, the results allow us to evaluate the influence of social position on the local topology of network connections. The experiment was carried out on the large social network derived from email communication. View full abstract»

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  • Deriving Conceptual Schema from XML Databases

    Page(s): 40 - 45
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (424 KB) |  | HTML iconHTML  

    In this paper, two concepts from different research areas are addressed together, namely functional dependency (FD) and multidimensional association rule (MAR). FD is a class of integrity constraints that have gained fundamental importance in relational database design. MAR is a class of patterns which has been studied rigorously in data mining. We employ MAR to mine the interesting rules from XML databases. The mined interesting rules are considered as candidate FDs whose all confidence itemsets are 100%. To prune the weak rules, we pay attention to support and correlation itemsets. The final strong rules are used to generate an object-role model conceptual schema diagram. View full abstract»

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  • Mining Multilevel Association Rules on RFID Data

    Page(s): 46 - 50
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (620 KB) |  | HTML iconHTML  

    In SCM, the problem with RFID data is that the volume increases according to time and location, thus, resulting in an enormous degree of data duplication. Therefore it is difficult to extract useful knowledge hidden in data using existing association rule mining techniques, or analyze data using statistical techniques or queries. However, strong associations discovered at high concept levels may represent common sense knowledge and RFID data represented as a concept hierarchy has the property that the data size at the lowest level increases in proportion to the item group. This paper has two aims. Firstly, we use time generalization to eliminate data duplication. Generalization is useful in data mining since they permit the discovery of knowledge at different levels of abstraction, such as multilevel association rules. Secondly, to reduce the complexity of rule generation by examining association rules limited to the level of interest of the consumer, not all concept hierarchy level on a each concept level have its own level passage threshold. As a result, rule generation time is reduced and the query speed is significantly accelerated, due to filtering of data. View full abstract»

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  • A Novel Approach to Keyword Extraction for Contextual Advertising

    Page(s): 51 - 56
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (412 KB) |  | HTML iconHTML  

    Online advertising has now turned to be one of the major revenue sources for today's Internet companies. Among the different channels of advertising, contextual advertising takes the great part. There are already lots of studies done for the keyword extraction problem in contextual advertising for English, however, little has been conducted for Chinese, which is mainly different from English linguistically. In this paper, we focus on the problem of Chinese advertising keywords extraction and propose a novel approach based on the idea of classification. We adopt C4.5 as the classifier model and select appropriate features with Chinese linguistic characteristic taken into consideration. The experimental results indicate that our approach is promising. View full abstract»

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  • Class Prediction and Pattern Discovery in Microarray Data - Artificial Intelligence and Algebraic Methods

    Page(s): 57 - 60
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (76 KB) |  | HTML iconHTML  

    In the paper we present a brief survey of our results in processing of data from DNA microarray experiments obtained in our collaborative research with M.C. Sklodowska Centre of Oncology. Our experience therefore is strictly connected with problems resulting from cancer diagnosis and therapy but many results have more general issue. We focus our attention on three important stages of microarray data processing e.g. class prediction, gene selection and pattern discovery. View full abstract»

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  • Applying Classification Problems via a Data Mining Approach Based on a Cerebellar Model Articulation Controller

    Page(s): 61 - 66
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (456 KB) |  | HTML iconHTML  

    Although applied to classification, neural network (NN) classifiers have certain limitations, including slow training time, complex interpretation and difficult implementation in terms of optimal network topology. To overcome these disadvantages, this study presents an efficient and simple classifier based on the cerebellar model articulation controller NN (CMAC NN), which has the advantages of very fast learning, reasonable generalization ability and robust noise resistance. The performance of the proposed CMAC NN classifier is measured using PROBEN1 benchmark data sets taken from the UCI Machine Learning Repository for diabetes and glass, each of which include, respectively, three permutations of the available patterns. Numerical results show that the proposed CMAC NN classifier was efficient for tested data sets. Therefore, the CMAC NN classifier can be considered as a data mining tool to classification. View full abstract»

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  • Web Page Element Classification Based on Visual Features

    Page(s): 67 - 72
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (287 KB) |  | HTML iconHTML  

    When applying the traditional data mining methods to World Wide Web documents, the typical problem is that a normal Web page contains a variety of information of different kinds in addition to its main content. This additional information such as navigation, advertisement or copyright notices negatively influences the results of the data mining methods as for example the content classification. In this paper, we present a method of interesting area detection in a Web page. This method is inspired by an assumed human reader approach to this task. First, basic visual blocks are detected in the page and subsequently, the purpose of these blocks is guessed based on their visual appearance. We describe a page segmentation method used for the visual block detection, we propose a way of the block classification based on the visual features and finally, we provide an experimental evaluation of the method on real-world data. View full abstract»

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  • Select-Response Grouping Proof for RFID Tags

    Page(s): 73 - 77
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (350 KB) |  | HTML iconHTML  

    In this paper, we investigate a scenario of RFID applications referred to enable a group of RFID tags which have been scanned simultaneously by a reading device, is literally called grouping proof problems. After examining the existing ldquoYoking Proofrdquo protocols of RFID, this paper proposes a protocol called ldquoSelect-Responserdquo Grouping Proof. Instead of waiting the computation result from the tags as previous protocols, the new protocol uses a new mechanism that the reader actively selects the demanded tags to fulfill the verification. With this fundamental change, our protocol neutralizes the threats of denial of service attack, which is suffered by the ldquoYoking Proofrdquo protocols, and provide collision-free and missing tag identification properties, which would offer great help in the practical applications. View full abstract»

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  • An Approach to Identity Theft Detection Using Social Network Analysis

    Page(s): 78 - 81
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (321 KB) |  | HTML iconHTML  

    The paper presents a method of automatic detection of identity theft related event. The method is based on the social network analysis. The protection of electronic identity in contemporary information society is one of the greatest challenges in security research. The main idea about method of identity theft detection described in the paper is that identity theft events are related to change of the subject's behavior as the node of the social network (or several different networks). In consequence identity theft may be detected similarly as the other types of malicious activities by analyzing the change of observed behavior patterns. View full abstract»

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