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15th International Workshop on Research Issues in Data Engineering: Stream Data Mining and Applications (RIDE-SDMA'05)

3-4 April 2005

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  • 15th International Workshop on Research Issues in Data Engineering: Stream Data Mining and Applications - Cover

    Publication Year: 2005, Page(s): c1
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  • 15th International Workshop on Research Issues in Data Engineering: Stream Data Mining and Applications

    Publication Year: 2005
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  • 15th International Workshop on Research Issues in Data Engineering: Stream Data Mining and Applications - Copyright Page

    Publication Year: 2005, Page(s): iv
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  • 15th International Workshop on Research Issues in Data Engineering: Stream Data Mining and Applications - Table of contents

    Publication Year: 2005, Page(s):v - vi
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  • Preface

    Publication Year: 2005, Page(s): vii
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  • An efficient algorithm for incremental mining of association rules

    Publication Year: 2005, Page(s):3 - 10
    Cited by:  Papers (9)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (160 KB) | HTML iconHTML

    Incremental algorithms can manipulate the results of earlier mining to derive the final mining output in various businesses. This study proposes a new algorithm, called the New Fast UPdate algorithm (NFUP) for efficiently incrementally mining association rules from a large transaction database. NFUP is a backward method that only requires scanning incremental database. Rather than rescanning the o... View full abstract»

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  • Online mining (recently) maximal frequent itemsets over data streams

    Publication Year: 2005, Page(s):11 - 18
    Cited by:  Papers (15)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (216 KB) | HTML iconHTML

    A data stream is a massive, open-ended sequence of data elements continuously generated at a rapid rate. Mining data streams is more difficult than mining static databases because the huge, high-speed and continuous characteristics of streaming data. In this paper, we propose a new one-pass algorithm called DSM-MFI (stands for Data Stream Mining for Maximal Frequent Itemsets), which mines the set ... View full abstract»

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  • A clustering method using an irregular size cell graph

    Publication Year: 2005, Page(s):19 - 26
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (212 KB) | HTML iconHTML

    In this paper we propose a clustering method (data mining technique) called "FlexDice" for large high-dimensional datasets. The data structure used in FlexDice is a graph-structure. Its data structure and the data structure of Quadtree have a few same features, but they have some crucial differences. The most crucial difference is that the data structure of Quadtree is a tree-structure while the d... View full abstract»

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  • Using probabilistic latent semantic analysis for Web page grouping

    Publication Year: 2005, Page(s):29 - 36
    Cited by:  Papers (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (160 KB) | HTML iconHTML

    The locality of Web pages within a Web site is initially determined by the designer's expectation. Web usage mining can discover the patterns in the navigational behaviour of Web visitors, in turn, improve Web site functionality and service designing by considering users' actual opinion. Conventional Web page clustering technique is often utilized to reveal the functional similarity of Web pages. ... View full abstract»

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  • Maintaining knowledge-bases of navigational patterns from streams of navigational sequences

    Publication Year: 2005, Page(s):37 - 44
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (95 KB) | HTML iconHTML

    In this paper we explore an alternative design goal for navigational pattern discovery in stream environments. Instead of mining based on thresholds and returning the patterns that satisfy the specified threshold(s), we propose to mine without thresholds and return all identified patterns along with their support counts in a single pass. We utilize a sliding window to capture recent navigational s... View full abstract»

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  • Data mining approaches to software fault diagnosis

    Publication Year: 2005, Page(s):45 - 52
    Cited by:  Papers (5)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (208 KB) | HTML iconHTML

    Automatic identification of software faults has enormous practical significance. This requires characterizing program execution behavior and the use of appropriate data mining techniques on the chosen representation. In this paper we use the sequence of system calls to characterize program execution. The data mining tasks addressed are learning to map system call streams to fault labels and automa... View full abstract»

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  • Handling nominal features in anomaly intrusion detection problems

    Publication Year: 2005, Page(s):55 - 62
    Cited by:  Papers (6)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (184 KB) | HTML iconHTML

    Computer network data stream used in intrusion detection usually involve many data types. A common data type is that of symbolic or nominal features. Whether being coded into numerical values or not, nominal features need to be treated differently from numeric features. This paper studies the effectiveness of two approaches in handling nominal features: a simple coding scheme via the use of indica... View full abstract»

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  • Time-decaying Bloom Filters for data streams with skewed distributions

    Publication Year: 2005, Page(s):63 - 69
    Cited by:  Papers (14)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (616 KB) | HTML iconHTML

    Bloom Filters are space-efficient data structures for membership queries over sets. To enable queries for multiplicities of multi-sets, the bitmap in a Bloom Filter is replaced by an array of counters whose values increment on each occurrence. In a data stream model, however, data items arrive at varying rates and recent occurrences are often regarded as more significant than past ones. In most da... View full abstract»

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  • New estimation methods of Count-Min sketch

    Publication Year: 2005, Page(s):73 - 80
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1056 KB) | HTML iconHTML

    Count-Min sketch is an efficient approximate query tool for data stream. In this paper we address how to further improve its point query performance. Firstly, we modify the estimation method under cash register model. Our method will relieve error propagation. Secondly, we find better method under turnstile model and prove that our method is more efficient than that Count-Min sketch. These conclus... View full abstract»

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  • CDS-Tree: an effective index for clustering arbitrary shapes in data streams

    Publication Year: 2005, Page(s):81 - 88
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (136 KB) | HTML iconHTML

    Finding clusters of arbitrary shapes in data streams is a challenging work for advanced applications. An effective approach to clustering arbitrary shapes is the clustering algorithm based on space partition. However, it cannot be applied directly into data stream clustering since it costs large memory spaces while data stream processing has strict memory space limitation. In addition, it has low ... View full abstract»

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  • Author index

    Publication Year: 2005, Page(s): 89
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