SeqScout: Using a Bandit Model to Discover Interesting Subgroups in Labeled Sequences

Abstract

It is extremely useful to exploit labeled datasets not only to learn models but also to improve our understanding of a domain and its available targeted classes. The so-called subgroup discovery task has been considered for a long time. It concerns the discovery of patterns or descriptions, the set of supporting objects of which have interesting properties, e.g., they characterize or discriminate a given target class. Though many subgroup discovery algorithms have been proposed for transactional data, discovering subgroups within labeled sequential data and thus searching for descriptions as sequential patterns has been much less studied. In that context, exhaustive exploration strategies can not be used for real-life applications and we have to look for heuristic approaches. We propose the algorithm SeqScout to discover interesting subgroups (w.r.t. a chosen quality measure) from labeled sequences of itemsets. This is a new sampling algorithm that mines discriminant sequential patterns using a multi-armed bandit model. It is an anytime algorithm that, for a given budget, finds a collection of local optima in the search space of descriptions and thus subgroups. It requires a light configuration and it is independent from the quality measure used for pattern scoring. Furthermore, it is fairly simple to implement. We provide qualitative and quantitative experiments on several datasets to illustrate its added-value.

Anytime mining of sequential discriminative patterns in labeled sequences

“…To the best of our knowledge, the problem of mining discriminative sequences of itemsets agnostic of the chosen quality measure with sampling approaches has not been addressed yet in the literature, except in our recent conference paper [26]. Hereafter, we describe our methods SeqScout and MCTSExtent that compute topk non-redundant discriminative patterns.…”

“…Our research concerns search space exploration methods for labeled sequences of itemsets and not just sequences of items. We first describe the algorithm SeqScout that has been introduced in our conference paper [26]. SeqScout is based on sampling guided by a multi-armed bandit model followed by a generalization step and a phase of local optimization.…”

A Behavioral Pattern Mining Approach to Model Player Skills in Rocket League

“…There exists no mining algorithm for behavioral patterns as defined in this article. However, we recently introduced the SeqScout algorithm [10] that can mine discriminative patterns in sequences of itemsets. In the following, we first present the original version of SeqScout and then its slight adaptation to mine behavioral patterns.…”

“…The quality of this pattern, i.e., its discriminating power, is then computed with the chosen quality measure, the W RAcc in our case. Once the time budget has been reached, patterns are filtered to make sure they are nonredundant following Jaccard index, using a parameter θ (see [10], and the top-k are returned. To adapt this algorithm to our problem, we need to reconsider the generalisation step as complex event sequences also contain vectors of intervals.…”

“…6, we tested the influence of the parameter α from the generalisation step on the quality of patterns. Note that the W RAcc takes its values in [−0.25, 0.25] (see [10] for more information). Here we can see that we have an optimum for α = 0.8.…”