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Computer

Issue 8 • Date Aug 1999

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Displaying Results 1 - 8 of 8
  • Chameleon: hierarchical clustering using dynamic modeling

    Publication Year: 1999, Page(s):68 - 75
    Cited by:  Papers (349)  |  Patents (6)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1620 KB)

    Clustering is a discovery process in data mining. It groups a set of data in a way that maximizes the similarity within clusters and minimizes the similarity between two different clusters. Many advanced algorithms have difficulty dealing with highly variable clusters that do not follow a preconceived model. By basing its selections on both interconnectivity and closeness, the Chameleon algorithm ... View full abstract»

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  • Mining very large databases

    Publication Year: 1999, Page(s):38 - 45
    Cited by:  Papers (31)  |  Patents (20)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (1156 KB)

    Established companies have had decades to accumulate masses of data about their customers, suppliers, products and services, and employees. Data mining, also known as knowledge discovery in databases, gives organizations the tools to sift through these vast data stores to find the trends, patterns, and correlations that can guide strategic decision making. Traditionally, algorithms for data analys... View full abstract»

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  • Mining the Web's link structure

    Publication Year: 1999, Page(s):60 - 67
    Cited by:  Papers (116)  |  Patents (55)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (328 KB)

    The Web is a hypertext body of approximately 300 million pages that continues to grow at roughly a million pages per day. Page variation is more prodigious than the data's raw scale: taken as a whole, the set of Web pages lacks a unifying structure and shows far more authoring style and content variation than that seen in traditional text document collections. This level of complexity makes an &ld... View full abstract»

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  • Interactive data analysis: the Control project

    Publication Year: 1999, Page(s):51 - 59
    Cited by:  Papers (31)  |  Patents (17)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (552 KB)

    Data analysis is fundamentally an iterative process in which you issue a query, receive a response, formulate the next query based on the response, and repeat. You usually don't issue a single, perfectly chosen query and get the information you want from a database; indeed, the purpose of data analysis is to extract unknown information, and in most situations, there is no one perfect query. People... View full abstract»

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  • The future of systems research

    Publication Year: 1999, Page(s):27 - 33
    Cited by:  Papers (27)  |  Patents (1)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (280 KB)

    After 20 years in academia and the Silicon Valley, the new Provost of Stanford University calls for a shift in focus for systems research. Performance-long the centerpiece-needs to share the spotlight with availability, maintainability, and other qualities. Although performance increases over the past 15 years have been truly amazing, it will be hard to continue these trends by sticking to the bas... View full abstract»

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  • Exposing the mythical MIPS year

    Publication Year: 1999, Page(s):22 - 26
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (112 KB)

    MIPS (million instructions per second) has been the most popular measure for computer speed since its inception, and people use it as the primary basis for comparing the speed of different computers. However, the author contends that using the MIPS year as a measurement of the amount of effort needed to break and compare cryptographic keys has led to some very inaccurate estimates. The MIPS year m... View full abstract»

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  • Data mining: from serendipity to science

    Publication Year: 1999, Page(s):34 - 37
    Cited by:  Papers (8)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (248 KB)

    The idea of unsupervised learning from basic facts (axioms) or from data has fascinated researchers for decades. Knowledge discovery engines try to extract general inferences from facts or training data. Statistical methods take a more structured approach, attempting to quantify data by known and intuitively understood models. The problem of gleaning knowledge from existing data sources poses a si... View full abstract»

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  • Constraint-based, multidimensional data mining

    Publication Year: 1999, Page(s):46 - 50
    Cited by:  Papers (28)  |  Patents (5)
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (332 KB)

    Although many data-mining methodologies and systems have been developed in recent years, the authors contend that by and large, present mining models lack human involvement, particularly in the form of guidance and user control. They believe that data mining is most effective when the computer does what it does best-like searching large databases or counting-and users do what they do best, like sp... View full abstract»

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Aims & Scope

Computer, the flagship publication of the IEEE Computer Society, publishes peer-reviewed articles written for and by computer researchers and practitioners representing the full spectrum of computing and information technology, from hardware to software and from emerging research to new applications. 

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Meet Our Editors

Editor-in-Chief
Sumi Helal
University of Florida
sumi.helal@gmail.com