document: Towards parameter-free data mining 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 data-mining 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 state-of-the-art approaches in anomaly\/interestingness detection , classification , and clustering with empirical tests on time series\/DNA\/text \/ video datasets .
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