A Mixed Similarity Measure for Data with Numeric, Symbolic and Ordinal Attributes(Artificial Intelligence I)
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概要
- 論文の詳細を見る
Many methods of knowledge discovery in databases are distance-based, such as instance-based learning or clustering where similarity measures between objects plays an essential role. Besides, it is known that most our real-word data not only contain numeric, symbolic, and ordinal attributes individually but also carry all of them in mixed way. Therefore, a Mixed Similarity Measure (MSM) for numeric and symbolic attributes is not enough for various data process. Moreover, the high cost of O(n^2logn^2) and O(n^2) for time and complexities of the existing algorithms do not allow the MSM to be applied to large datasets in KDD. As a result, we have proposed a fast algorithm to compute the Goodairs MSM for numeric and symbolic attributes in a linear complexity. In this paper, as an extension of the MSM, we consider an MSM for numeric, symbolic and ordinal attributes and describe a fast algorithm for MSM with a linear complexity as well. The experimental results show that the proposed MSM is also better than MSM and C4.5/See5.0 for the classification problem.
- 一般社団法人情報処理学会の論文
- 2004-12-04
著者
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Binh Nguyen
Department Of Information And Computer Sciences Toyohashi University Of Technology
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Binh Nguyen
Hanoi University Of Technology
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Phuong Nguyen
Hanoi University Of Technology
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Cuong Than
Hanoi University of Technology
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Bao Ho
Japan Advanced Institute of Science and Technology
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Than Van
Hanoi University Of Technology
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Nguyen Ngoc
Hanoi University of Technology
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Nguyen Thanh
Hanoi University of Technology
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