Li Wei Sang-hee's scientific contributions

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Publications (1)

Fig. 2 Thirty-six time series (in 18 pairs) clustered using the approach proposed in this paper
Fig. 3 Two clusterings on samples from two records from the MIT-BIH Arrhythmia Database (Left) Our approach (Right) Euclidean distance  
Fig. 4 Robot sensors clustered using CDM
Table 4 Classification error rates (%) for all four datasets
Table 6 Classification error rates (%) for all methods

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Data Min Knowl Disc DOI 10.1007/s10618-006-0049-3 Compression-based data mining of sequential data
  • Article
  • Full-text available

January 2005

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270 Reads

Eamonn Keogh

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Stefano Lonardi Chotirat

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Ann Ratanamahatana

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[...]

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Abstract The vast majority of data mining algorithms require the setting of many input parameters. The dangers of working with parameter-laden algorithms are twofold. First, incorrect settings may cause an algorithm to fail in finding the true patterns. Second, a perhaps more insidious problem is that the algorithm may report spurious patterns that do not really exist, or greatly overestimate the significance of the reported patterns. This is especially likely when the user fails to understand the role of parameters in the data mining Responsible editor: Johannes Gehrke.

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