@article{ref1,
title="Association rule mining with mostly associated sequential patterns",
journal="Expert systems with applications",
year="2015",
author="Soysal, Omer M.",
volume="42",
number="5",
pages="2582-2592",
abstract="In this paper, we address the problem of mining structured data to find potentially useful patterns by association rule mining. Different than the traditional find-all-then-prune approach, a heuristic method is proposed to extract mostly associated patterns (MASPs). This approach utilizes a maximally-association constraint to generate patterns without searching the entire lattice of item combinations. This approach does not require a pruning process. The proposed approach requires less computational resources in terms of time and memory requirements while generating a long sequence of patterns that have the highest co-occurrence. Furthermore, k-item patterns can be obtained thanks to the sub-lattice property of the MASPs. In addition, the algorithm produces a tree of the detected patterns; this tree can assist decision makers for visual analysis of data. The outcome of the algorithm implemented is illustrated using traffic accident data. The proposed approach has a potential to be utilized in big data analytics.<p /> <p>Language: en</p>",
language="en",
issn="0957-4174",
doi="10.1016/j.eswa.2014.10.049",
url="http://dx.doi.org/10.1016/j.eswa.2014.10.049"
}