  Given the vast reservoirs of data stored worldwide, efficient mining of data
from a large information store has emerged as a great challenge. Many databases
like that of intrusion detection systems, web-click records, player statistics,
texts, proteins etc., store strings or sequences. Searching for an unusual
pattern within such long strings of data has emerged as a requirement for
diverse applications. Given a string, the problem then is to identify the
substrings that differs the most from the expected or normal behavior, i.e.,
the substrings that are statistically significant. In other words, these
substrings are less likely to occur due to chance alone and may point to some
interesting information or phenomenon that warrants further exploration. To
this end, we use the chi-square measure. We propose two heuristics for
retrieving the top-k substrings with the largest chi-square measure. We show
that the algorithms outperform other competing algorithms in the runtime, while
maintaining a high approximation ratio of more than 0.96.
