Mining Statistically Significant Substrings Based on the Chi-Square Measure

TL;DR

This paper introduces efficient heuristics for mining statistically significant substrings using the chi-square measure, outperforming existing algorithms in runtime while maintaining high accuracy.

Contribution

It proposes two novel heuristics for extracting top-k significant substrings based on chi-square, with improved efficiency and high approximation quality.

Findings

Algorithms outperform competitors in runtime

High approximation ratio (>0.96) achieved

Effective for diverse data types like text and proteins

Abstract

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.