Browsing by Author "Wynne HSU"

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    Finding Interesting Patterns using User Expectations
    (1996-07-01T00:00:00Z) Bing LIU; Wynne HSU; Lai-Fun MUN; Hing-Yan LEE
    Abstract not available.
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    Identifying Interesting Missing Patterns
    (1996-08-01T00:00:00Z) Bing LIU; Wynne HSU; Lai-Fun MUN; Hing-Yan LEE
    Abstract not available.
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    Supporting Frequent Updates in R-Trees: A Bottom-Up Approach
    (2004-04-01T00:00:00Z) Mong Li LEE; Wynne HSU; Christian S. JENSEN; Bin CUI; Keng Lik TEO
    Advances in hardware-related technologies promise to enable new data management applications that monitor continuous processes. In these applications, enormous amounts of state samples are obtained via sensors and are streamed to a database. Further, updates are very frequent and may exhibit locality. While the R-tree is the index of choice for multi-dimensional data with low dimensionality, and is thus relevant to these applications, R-tree updates are also relatively inefficient. We present a bottom-up update strategy for R-trees that generalizes existing update techniques and aims to improve update performance. It has different levels of reorganization---ranging from global to local---during updates, avoiding expensive top-down updates. A compact main-memory summary structure that allows direct access to the R-tree index nodes is used together with efficient bottom-up algorithms. Empirical studies indicate that the bottom-up strategy outperforms the traditional top-down technique, leads to indices with better query performance, achieves higher throughput, and is scalable.
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    Tuple-Level Analysis for Identification of Interesting Patterns
    (1996-05-01T00:00:00Z) Bing LIU; Wynne HSU; Hing-Yan LEE; Lai-Fun MUN
    One of the important issues in data mining is the "interestingness" problem. Past research and applications have shown that in many situations a huge number of patterns can be discovered from a database. Most of these patterns are actually useless or uninteresting to the user. But because of the huge number of patterns, it is difficult for the user to identify those interesting to him/her. In this project, we propose a new technique to help the user identify interesting patterns. The user is first asked to provide his/her expected patterns according to his/her past knowledge and/or intuitive feelings. Given these expectations, the system uses a tuple-level fuzzy matching technique to analyze and rank the discovered patterns according to a number of interestingness measures. With this technique, the user can quickly focus on a subset of the discovered patterns with the most application values.