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Mining Statistically Significant Substrings based on the Chi-Square Measure
Abstract
With the tremendous expansion of reservoirs of sequence data stored worldwide, efficient mining of large string databases in various domains including intrusion detection systems, player statistics, texts, and proteins, has emerged as a practical challenge. Searching for an unusual pattern within long strings of data is one of the foremost requirements for many diverse applications. Given a string, the problem is to identify the substrings that differ the most from the expected or normal behavior, i.e., the substrings that are statistically significant (or, in other words, less likely to occur due to chance alone). We first survey and analyze the different statistical measures available to meet this end. Next, we argue that the most appropriate metric is the chi-square measure. Finally, we discuss different approaches and algorithms proposed for retrieving the top-k substrings with the largest chi-square measure.
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