Frequent Itemset Mining was proposed by Agrawal in 1993, as an algorithm for extracting frequent itemsets from large databases [33].
Sample text:```In this sense, accidentally encountered information is also always algorithmically framed and often personalised by for example previous searches and geographical location (e.g. Abel et al., 2011; Feuz et al., 2011; Hannak et al., 2013).```
Sample intent: background
Sample text:```In this study, we used the Knowledge Map Concept Indexer (KMCI) (Denny et al., 2009), a general-purpose NLP engine, to process all paper titles from American Journal of Epidemiology (AJE).```
Sample intent: background



Sample text:```Over the last few years, many different local interest point detectors (like Harris3D [16], Cuboid [6], Hessian [35] or Dense sampling [33]) and many spatio-temporal descriptors (like HOG [17], HOG3D [14], HOF [17], Cuboid [6] or ESURF [35]) have been proposed.```
Sample intent: method



Sample text:```The merging is based on the increase in the weighted-bycounts entropy [1] and is stopped by a threshold indicating the minimum number of training samples required to estimate a weight distribution.```
Sample intent: method



Sample text:```The results for veridical recall replicated previous results in the literature (Bal-
lardini et al., 2008; McDermott & Watson, 2001), with the average recall of studied words increas-
ing monotonically as SOA increased.```
Sample intent: result



Sample text:```While this method is known to be generally reliable, there are some questions about the representativeness of the data used [25].```
Sample intent: background
Sample text:```05, minimum cluster size 64 voxels) [Woo et al., 2014] for the primary analysis of regions showing increasing activity with distance from the home location.```
Sample intent: method



Sample text:```The results for veridical recall replicated previous results in the literature (Bal-
lardini et al., 2008; McDermott & Watson, 2001), with the average recall of studied words increas-
ing monotonically as SOA increased.```
Sample intent: result



Sample text:```In this study, we generalized previous results regarding the quasi-Gaussian shape and the scalar property using the SBF model [13,21,52].```
Sample intent: result



Sample text:```…between output and input, CNF achieves almost 10% relative improvement over CRF. CNF also outperforms other methods including SVMpro (Hua and Sun, 2001), SVMpsi (Kim and Park, 2003), YASSPP (Karypis, 2006), PSIPRED (Jones, 1999), SPINE (Dor and Zhou, 2007) and TreeCRFpsi (Dietterich et al., 2004).```
Sample intent: background



Sample text:```Our results confi rm previous studies that reached the same conclusion (Boushey et al 1994; Vermeulen et al 2000; Till et al 2005; Marcucci et al 2005).```
Sample intent: result



Sample text:```This section contains a generalisation of the basic results on multifractal products of stochastic processes developed in Kahane (1985), Kahane (1987) and Mannersalo et al (2002).```
Sample intent: method



Sample text:```Discrepancies analogous to those between our results and those of simulations have, indeed, recently been observed in the case of the binary perceptron [18], the pspin interaction spin glass [19], and in the case of fluctuating manifolds in random media [20], where dynamically frozen phases were observed in regions of parameter space in which static approaches yielded ergodic phases.```
Sample intent: result



Sample text:```The merging is based on the increase in the weighted-bycounts entropy [1] and is stopped by a threshold indicating the minimum number of training samples required to estimate a weight distribution.```
Sample intent: method



Sample text:```In this study, we used the Knowledge Map Concept Indexer (KMCI) (Denny et al., 2009), a general-purpose NLP engine, to process all paper titles from American Journal of Epidemiology (AJE).```
Sample intent: background



Sample text:```In this sense, accidentally encountered information is also always algorithmically framed and often personalised by for example previous searches and geographical location (e.g. Abel et al., 2011; Feuz et al., 2011; Hannak et al., 2013).```
Sample intent: background