InfoMiner+: Mining Partial Periodic Patterns with Gap Penalties

In this paper, we focus on mining periodic patterns allowing some degree of imperfection in the form of random replacement from a perfect periodic pattern. Information gain was proposed to identify patterns with events of vastly different occurrence frequencies and adjust for the deviation from a pattern. However, it does not take any penalty if there exists some gap between the pattern occurrences. In many applications, e.g., bio-informatics, it is important to identify subsequences that a pattern repeats perfectly (or near perfectly). As a solution, we extend the information gain measure to include a penalty for gaps between pattern occurrences. We call this measure as generalized information gain. Furthermore, we want to find subsequence S0 such that for a pattern P , the generalized information gain of P in S0 is high. This is particularly useful in locating repeats in DNA sequences. In this paper, we developed an effective mining algorithm, InfoMiner+, to simultaneously mine significant patterns and the associated subsequences.