Towards parameter-free data mining

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Towards parameter-free data mining (2004)

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by Eamonn Keogh , Stefano Lonardi , Chotirat Ann Ratanamahatana

Venue:	In: Proc. 10th ACM SIGKDD Intn’l Conf. Knowledge Discovery and Data Mining
Citations:	145 - 19 self

BibTeX

@INPROCEEDINGS{Keogh04towardsparameter-free,
    author = {Eamonn Keogh and Stefano Lonardi and Chotirat Ann Ratanamahatana},
    title = {Towards parameter-free data mining},
    booktitle = {In: Proc. 10th ACM SIGKDD Intn’l Conf. Knowledge Discovery and Data Mining},
    year = {2004},
    pages = {206--215},
    publisher = {ACM Press}
}

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Abstract

Most data mining algorithms require the setting of many input parameters. Two main dangers of working with parameter-laden algorithms are the following. 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 process. Data mining algorithms should have as few parameters as possible, ideally none. A parameter-free algorithm would limit our ability to impose our prejudices, expectations, and presumptions on the problem at hand, and would let the data itself speak to us. In this work, we show that recent results in bioinformatics and computational theory hold great promise for a parameter-free datamining paradigm. The results are motivated by observations in Kolmogorov complexity theory. However, as a practical matter, they can be implemented using any off-the-shelf compression algorithm with the addition of just a dozen or so lines of code. We will show that this approach is competitive or superior to the stateof-the-art approaches in anomaly/interestingness detection, classification, and clustering with empirical tests on time series/DNA/text/video datasets.

Keyphrases

parameter-free data mining data mining algorithm computational theory hold great promise stateof-the-art approach parameter-free algorithm incorrect setting true pattern spurious pattern video datasets anomaly interestingness detection off-the-shelf compression algorithm many input parameter insidious problem kolmogorov complexity theory main danger practical matter recent result empirical test parameter-free datamining paradigm parameter-laden algorithm time series dna data mining process

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