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Source: Elaborated by the author. Figure 29 — Our proposed framework to extract features from popular games

Figure 29 Source: Elaborated by the author. Figure 29 — Our proposed framework to extract features from popular games

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Bibliotecarios responsaveis pela estrutura de catalogacdo da publicagdo de acordo com a AACR2: Glaucia Maria Saia Cristianini- CRB - 8/4938 J uliana de Souza Moraes - CRB - 8/6176
Bibliotecarios responsaveis pela estrutura de catalogacdo da publicagdo de acordo com a AACR2: Glaucia Maria Saia Cristianini- CRB - 8/4938 J uliana de Souza Moraes - CRB - 8/6176
Source: Adapted from Fayyad et al. (1996). Figure 1 — Knowledge Discovery in Databases |. Data Mining: the most vital step; it refers to the exploration and analysis performed to un-
Source: Adapted from Fayyad et al. (1996). Figure 1 — Knowledge Discovery in Databases |. Data Mining: the most vital step; it refers to the exploration and analysis performed to un-
Another characteristic of a graph is the type of its edges. There are two types: weighted and unweighted edges. Unweighted edges are connections without an explicit value, which means that all edges in the graph have the same weight, i.e., the same importance. On the other hand, weighted edges have explicit values, in which each edge e; € E has a value w; associated to it to represent the weight of the respective link. Thus, in a graph with weighted edges, it is possible to represent strong and weak connections quantitatively. For example, consider a player-game network where players evaluate games using numbers of stars, so that the more stars a player gives to a game, the better is the game in the player’s opinion.
Another characteristic of a graph is the type of its edges. There are two types: weighted and unweighted edges. Unweighted edges are connections without an explicit value, which means that all edges in the graph have the same weight, i.e., the same importance. On the other hand, weighted edges have explicit values, in which each edge e; € E has a value w; associated to it to represent the weight of the respective link. Thus, in a graph with weighted edges, it is possible to represent strong and weak connections quantitatively. For example, consider a player-game network where players evaluate games using numbers of stars, so that the more stars a player gives to a game, the better is the game in the player’s opinion.
of v is the sum of weights w; of all of its edges, as shown in Equation 2.4.
of v is the sum of weights w; of all of its edges, as shown in Equation 2.4.
Figure 5 — Relationship examples from Social Networks of Games
Figure 5 — Relationship examples from Social Networks of Games
Figure 6 — Types of player-game interactions on Super Mario Maker
Figure 6 — Types of player-game interactions on Super Mario Maker
Source — Nintendo. <http://supermariomaker.nintendo.com/> (accessed July 16, 2019). Figure 7 — Game styles in Super Mario Maker creator, i.e., maker, to be published online. 3.3.2. SMM Bookmark
Source — Nintendo. <http://supermariomaker.nintendo.com/> (accessed July 16, 2019). Figure 7 — Game styles in Super Mario Maker creator, i.e., maker, to be published online. 3.3.2. SMM Bookmark
Figure 8 - SMM Bookmark: Game level Source — SMM Bookmark. <https://supermariomakerbookmark.nintendo.net/> (accessed July 16, 2019). interactions, as shown in Figure 9. Note that allows to identify the latest player that beat the time record, besides the one that made the first clear, and who performed the latest plays, latest clears and latest likes. Additionally, SMM Bookmark makes available a website for each player with detailed activities. Figure 10 reports a player website, in which allows to access his/her name. nationality, number of stars received by levels developed, number of games played, number of games cleared. among others. nationality, number of stars received by levels developed, number of games played, number of Figure 9 — SMM Bookmark: Player-game interactions
Figure 8 - SMM Bookmark: Game level Source — SMM Bookmark. <https://supermariomakerbookmark.nintendo.net/> (accessed July 16, 2019). interactions, as shown in Figure 9. Note that allows to identify the latest player that beat the time record, besides the one that made the first clear, and who performed the latest plays, latest clears and latest likes. Additionally, SMM Bookmark makes available a website for each player with detailed activities. Figure 10 reports a player website, in which allows to access his/her name. nationality, number of stars received by levels developed, number of games played, number of games cleared. among others. nationality, number of stars received by levels developed, number of games played, number of Figure 9 — SMM Bookmark: Player-game interactions
Source —- SMM Bookmark. <https://supermariomakerbookmark.nintendo.net/> (accessed July 16, 2019). Figure 10 - SMM Bookmark: Player’s statistics
Source —- SMM Bookmark. <https://supermariomakerbookmark.nintendo.net/> (accessed July 16, 2019). Figure 10 - SMM Bookmark: Player’s statistics
Figure 11 — Crawler: Data collection structure
Figure 11 — Crawler: Data collection structure
Figure 12 — Relational schema for SMMnet Relation COURSES stores static metadata about SMM levels. It includes the identifier of ‘ach level, besides a foreign key referring to the player who created the level, the timestamp of reation and other static information on the level. The corresponding temporal-related metadata is tored in relation COURSE-META. It includes counts of players, stars, tweets, clears and attempts yer level, besides a tag, the clear rate, a foreign key referring to the player that first cleared the evel and the timestamp of the data collection. Relation PLAYERS stores static information on he players, including their identifiers, avatar images, flags and names. Finally, relations PLAYS “LEARS, LIKES and RECORDS store player-game interactions, i.e., links of the SNG, through nany-to-many relationships between relations COURSES and PLAYERS with the timestamp of he data collection. The world time records in milliseconds are also stored in relation RECORDS
Figure 12 — Relational schema for SMMnet Relation COURSES stores static metadata about SMM levels. It includes the identifier of ‘ach level, besides a foreign key referring to the player who created the level, the timestamp of reation and other static information on the level. The corresponding temporal-related metadata is tored in relation COURSE-META. It includes counts of players, stars, tweets, clears and attempts yer level, besides a tag, the clear rate, a foreign key referring to the player that first cleared the evel and the timestamp of the data collection. Relation PLAYERS stores static information on he players, including their identifiers, avatar images, flags and names. Finally, relations PLAYS “LEARS, LIKES and RECORDS store player-game interactions, i.e., links of the SNG, through nany-to-many relationships between relations COURSES and PLAYERS with the timestamp of he data collection. The world time records in milliseconds are also stored in relation RECORDS
Table 2 — Relation cardinality in SMMnet
Table 2 — Relation cardinality in SMMnet
of the most traditional games in the Super Mario Bros series. Figure 13 and Table 3 report the proportions of levels per game style. As can be seen, 51.7% of the levels follow the game style Super Mario Bros U, 21.2% follow Super Mario Bros, 19.0% follow Super Mario World and 8.1% follow Super Mario Bros 3. Note that there is a high reference for the most recent game style Super Mario Bros U and for the oldest game style Super Mario Bros. In this sense, we presume that the most recent style prevails through the new visual and modern gameplay characteristics, while the oldest style stands out due to the memory of the most traditional games in the Super Mario Bros series.
of the most traditional games in the Super Mario Bros series. Figure 13 and Table 3 report the proportions of levels per game style. As can be seen, 51.7% of the levels follow the game style Super Mario Bros U, 21.2% follow Super Mario Bros, 19.0% follow Super Mario World and 8.1% follow Super Mario Bros 3. Note that there is a high reference for the most recent game style Super Mario Bros U and for the oldest game style Super Mario Bros. In this sense, we presume that the most recent style prevails through the new visual and modern gameplay characteristics, while the oldest style stands out due to the memory of the most traditional games in the Super Mario Bros series.
Figure 20 — Social Networks Gye, and Gjjze changing over time in Gyeyj+3, nothing changes. Therefore, this network can only grow over time.
Figure 20 — Social Networks Gye, and Gjjze changing over time in Gyeyj+3, nothing changes. Therefore, this network can only grow over time.
Source: Elaborated by the author. Figure 22 — Linear regression for a game level’s stream of like counts
Source: Elaborated by the author. Figure 22 — Linear regression for a game level’s stream of like counts
Delta Rank (DR) is the second feature extractor that we propose. While the previous extractor reports one global view on how the like counts of the game levels developed by a player grow over time, considering the whole temporal interval between the first measurement and the last one in each like-counting stream, the new extractor presents a higher-resolution view by describing the levels’ ascensions of likes between consecutive measurements in time. Intuitively, extractor DR infers features that describe quasi-instantaneous ascensions in like counts.
Delta Rank (DR) is the second feature extractor that we propose. While the previous extractor reports one global view on how the like counts of the game levels developed by a player grow over time, considering the whole temporal interval between the first measurement and the last one in each like-counting stream, the new extractor presents a higher-resolution view by describing the levels’ ascensions of likes between consecutive measurements in time. Intuitively, extractor DR infers features that describe quasi-instantaneous ascensions in like counts.
Figure 23 — Delta values A, ; for a game c possible to identify the game’s greatest quasi-instantaneous ascension, which is A,» = 3 in our running example. Also, one can compute the entropy S,, of A, to quantify how random are the quasi-instantaneous ascensions of likes for game c; in the example, it is Sa. = 1.33. The proposed extractor DR uses these values as a basis to infer features for a player p of interest. proposed extractor DR uses these values as a basis to infer features for a player p of interest.
Figure 23 — Delta values A, ; for a game c possible to identify the game’s greatest quasi-instantaneous ascension, which is A,» = 3 in our running example. Also, one can compute the entropy S,, of A, to quantify how random are the quasi-instantaneous ascensions of likes for game c; in the example, it is Sa. = 1.33. The proposed extractor DR uses these values as a basis to infer features for a player p of interest. proposed extractor DR uses these values as a basis to infer features for a player p of interest.
Figure 24 illustrates this idea for the same like-counting stream of a fictitious game c used as an example in the previous section. Once again, we consider consecutive time in- stants between ft) and 17, thus @..; = 45.0°, 0... = 71.6°, 0.3 = 63.4°, @..4 =0.0°, 05 = 45.0° and 0,6 = 0.0°. The angle measurements 0,,; vary within interval [0°, 90°); large values indicate high like-counting growth between two sequential events. Compared with the plain count of new likes A, ; used in the previous extractor — see Figure 23, the angle values emphasize differences between intervals with few new likes, while they also attenuate differences when the counts between intervals with few new likes, while they also attenuate differences when the counts
Figure 24 illustrates this idea for the same like-counting stream of a fictitious game c used as an example in the previous section. Once again, we consider consecutive time in- stants between ft) and 17, thus @..; = 45.0°, 0... = 71.6°, 0.3 = 63.4°, @..4 =0.0°, 05 = 45.0° and 0,6 = 0.0°. The angle measurements 0,,; vary within interval [0°, 90°); large values indicate high like-counting growth between two sequential events. Compared with the plain count of new likes A, ; used in the previous extractor — see Figure 23, the angle values emphasize differences between intervals with few new likes, while they also attenuate differences when the counts between intervals with few new likes, while they also attenuate differences when the counts
Figure 24 — Coefficients of angle @,; for a game c Algorithm 3 is the pseudo-code for our extractor CA. As shown in Lines 12, 15 and 16 he algorithm represents a player p of interest as the union of 0. = {6..1,0¢2,... 6, likes,|—1} or each of his/her games c, so that @ = 6; U @ U ...@c,). Then, it analyzes set @ to extract he following features: 8, Og and 0,q,x. The first two features are respectively the mean and he standard deviation of @,.; values in set 0; see Lines 18 and 19. These features summarize vith statistical measurements the general pattern seen in angle-based representations 0,.; of uasi-instantaneous like-counting ascensions occurring for games developed by player p. Finally eature 9,qnx 18 in fact an array with L values, where L € Nxo is the same parameter used by the revious extractors. As can be seen in Line 20, 6,47, contains the L highest angle values @,,; in et 0; that is, O,ank = |max)(@), max2(@),...max,(@)]. Intuitively, O,ang captures extreme cases f games developed by player p with very fast growth in their streams of like counts, especially ocused on like-counting ascensions that come right after temporal intervals with few new likes measurements 6, ; extracted from games to infer features that describe their maker. of new likes are elevated. For example, note that the absolute difference in plain count of new likes between intervals |t4,t5) and [t5,t¢) is equal to the corresponding difference for |t),t3) and [t3,t4), thus [Ac 4 — Ac.5| = |Ac.2 — Ac3| = 1. In spite of this fact, the corresponding angle-based differences are not the same, i.e., |O¢,4 — 8,5] > |Oc,2 — 9.3], since the counts of new likes are smaller in intervals [t4,ts) and |ts,t¢) than their peers in intervals |t2,f3) and [f3,t4). Additionally, the angle-based measurement 6, takes into account the size of the interval analyzed, that is ti+1 — tj, aS opposed to the corresponding count of new likes A,;. Due to the aforementioned distinctions between angles and plain counts of new likes, we propose to take advantage of one novel view on quasi-instantaneous game ascensions with extractor CA; it uses angle-based a ¥ = oe 7 < pe ae
Figure 24 — Coefficients of angle @,; for a game c Algorithm 3 is the pseudo-code for our extractor CA. As shown in Lines 12, 15 and 16 he algorithm represents a player p of interest as the union of 0. = {6..1,0¢2,... 6, likes,|—1} or each of his/her games c, so that @ = 6; U @ U ...@c,). Then, it analyzes set @ to extract he following features: 8, Og and 0,q,x. The first two features are respectively the mean and he standard deviation of @,.; values in set 0; see Lines 18 and 19. These features summarize vith statistical measurements the general pattern seen in angle-based representations 0,.; of uasi-instantaneous like-counting ascensions occurring for games developed by player p. Finally eature 9,qnx 18 in fact an array with L values, where L € Nxo is the same parameter used by the revious extractors. As can be seen in Line 20, 6,47, contains the L highest angle values @,,; in et 0; that is, O,ank = |max)(@), max2(@),...max,(@)]. Intuitively, O,ang captures extreme cases f games developed by player p with very fast growth in their streams of like counts, especially ocused on like-counting ascensions that come right after temporal intervals with few new likes measurements 6, ; extracted from games to infer features that describe their maker. of new likes are elevated. For example, note that the absolute difference in plain count of new likes between intervals |t4,t5) and [t5,t¢) is equal to the corresponding difference for |t),t3) and [t3,t4), thus [Ac 4 — Ac.5| = |Ac.2 — Ac3| = 1. In spite of this fact, the corresponding angle-based differences are not the same, i.e., |O¢,4 — 8,5] > |Oc,2 — 9.3], since the counts of new likes are smaller in intervals [t4,ts) and |ts,t¢) than their peers in intervals |t2,f3) and [f3,t4). Additionally, the angle-based measurement 6, takes into account the size of the interval analyzed, that is ti+1 — tj, aS opposed to the corresponding count of new likes A,;. Due to the aforementioned distinctions between angles and plain counts of new likes, we propose to take advantage of one novel view on quasi-instantaneous game ascensions with extractor CA; it uses angle-based a ¥ = oe 7 < pe ae
Note that extractors LR, DR and CA share an input parameter L € Nxo that determines the quantity of ranked values to be returned. Each extractor has its own ranking particularities, as discussed before, but, whenever L is larger than the number of values available to be ranked, all >xtractors complete the rankings with zeros. Also, note that the extractors build independently a set of game levels C, for each player p of interest, and like-counting streams likes, for the games developed by p. Obviously, the combined extractor F477, builds each Cy and each like, only once; it does not create these structures redundantly for individual extractors. discussed before, but, whenever L is larger than the number of values available to be ranked, all
Note that extractors LR, DR and CA share an input parameter L € Nxo that determines the quantity of ranked values to be returned. Each extractor has its own ranking particularities, as discussed before, but, whenever L is larger than the number of values available to be ranked, all >xtractors complete the rankings with zeros. Also, note that the extractors build independently a set of game levels C, for each player p of interest, and like-counting streams likes, for the games developed by p. Obviously, the combined extractor F477, builds each Cy and each like, only once; it does not create these structures redundantly for individual extractors. discussed before, but, whenever L is larger than the number of values available to be ranked, all
Figures 25, 26 and 27 summarize the results for extractors LR, DR and CA, respective: ach figure reports accuracy for the top-6 best classifiers, among the 28 techniques studied, whe sing features from one of our extractors. For the reader’s convenience, the results are show oth in linear plots and in heat map plots. As can be seen, the best accuracy for each extract anges from 67.0% to 74.0%; it was obtained with values between 8 and 10 for parameter pecifically, the best configuration per extractor was: (1) LR — best accuracy of 67.0% obtaine y the DecisionTreeClassifier with 20 features, that is L = 8; (ii) DR — best accuracy of 69.0) btained by the DecisionTreeClassifier when analyzing 25 features, i.e., L = 10, and; (i11) DA 4.0% accuracy obtained by the GradientBoostingClassifier with only 11 features, so L = or extractor DR, it is worth noting that the average accuracy of classifiers increased togeth ‘ith L. However, when L becomes higher than the number of values available to be ranked, sor atures must receive zero values. In this sense, L cannot be much greater than 10. features must receive zero values. In this sense, L cannot be much greater than 10.
Figures 25, 26 and 27 summarize the results for extractors LR, DR and CA, respective: ach figure reports accuracy for the top-6 best classifiers, among the 28 techniques studied, whe sing features from one of our extractors. For the reader’s convenience, the results are show oth in linear plots and in heat map plots. As can be seen, the best accuracy for each extract anges from 67.0% to 74.0%; it was obtained with values between 8 and 10 for parameter pecifically, the best configuration per extractor was: (1) LR — best accuracy of 67.0% obtaine y the DecisionTreeClassifier with 20 features, that is L = 8; (ii) DR — best accuracy of 69.0) btained by the DecisionTreeClassifier when analyzing 25 features, i.e., L = 10, and; (i11) DA 4.0% accuracy obtained by the GradientBoostingClassifier with only 11 features, so L = or extractor DR, it is worth noting that the average accuracy of classifiers increased togeth ‘ith L. However, when L becomes higher than the number of values available to be ranked, sor atures must receive zero values. In this sense, L cannot be much greater than 10. features must receive zero values. In this sense, L cannot be much greater than 10.
Source: Research data. Table 6 — Experiments: evaluation of the top-3 best classifiers in each experiment Table 6 reports detailed results for the top-3 best classifiers; besides accuracy, it includes neasurements for the additional metrics precision, recall and F 1-score. As can be seen, the same yatterns discussed in the previous paragraph about the values of accuracy are also apparent in the idditional metrics of evaluation. In summary, our feature extractors LR, DR and CA presented iverage results for a classification task, with approximately 70% accuracy, 60% precision. 0% recall and 60% F'1-score. So far, let us highlight extractor CA; compared with the other >xtractors, it created less attributes and still reached the highest performance. extractors, it created less attributes and still reached the highest performance.
Source: Research data. Table 6 — Experiments: evaluation of the top-3 best classifiers in each experiment Table 6 reports detailed results for the top-3 best classifiers; besides accuracy, it includes neasurements for the additional metrics precision, recall and F 1-score. As can be seen, the same yatterns discussed in the previous paragraph about the values of accuracy are also apparent in the idditional metrics of evaluation. In summary, our feature extractors LR, DR and CA presented iverage results for a classification task, with approximately 70% accuracy, 60% precision. 0% recall and 60% F'1-score. So far, let us highlight extractor CA; compared with the other >xtractors, it created less attributes and still reached the highest performance. extractors, it created less attributes and still reached the highest performance.
Figure 30 — Our proposal for feature aggregation Source: Elaborated by the author.
Figure 30 — Our proposal for feature aggregation Source: Elaborated by the author.
Figure 31 — Spotting subspace clusters
Figure 31 — Spotting subspace clusters
Source: Research data. Table 7 — Number of clusters returned by CLIQUE versus the dimensionality of their subspaces On the other hand, CLIQUE inferred a very large number of clusters; that is, 3,573 lusters. They exist in subspaces whose dimensionalities go from 1 up to 9. Table 7 reports he number of clusters identified versus the dimensionality of their corresponding subspaces. Note that it would be a very exhaustive task to manually analyze all clusters. It would also be robably meaningless, especially because many clusters may have been created due to default onfigurations that the SMM system makes to every new game level. Thus, we perform one runing procedure to filter out irrelevant clusters automatically. It is described in the next section. pruning procedure to filter out irrelevant clusters automatically. It is described in the next section.
Source: Research data. Table 7 — Number of clusters returned by CLIQUE versus the dimensionality of their subspaces On the other hand, CLIQUE inferred a very large number of clusters; that is, 3,573 lusters. They exist in subspaces whose dimensionalities go from 1 up to 9. Table 7 reports he number of clusters identified versus the dimensionality of their corresponding subspaces. Note that it would be a very exhaustive task to manually analyze all clusters. It would also be robably meaningless, especially because many clusters may have been created due to default onfigurations that the SMM system makes to every new game level. Thus, we perform one runing procedure to filter out irrelevant clusters automatically. It is described in the next section. pruning procedure to filter out irrelevant clusters automatically. It is described in the next section.
Table 8 — Top-25 clusters according to their sizes In the previous paragraph, we aimed at removing irrelevant clusters that were formed by the absence of game objects. Now, we focus on pruning irrelevant clusters based on their sizes; that is, based on the number of game levels that they contain. Figure 33 reports the rank of clusters sorted according to their sizes. As can be seen, there are a few large clusters that do not present much information because they include nearly all of the 720 game levels of our dataset. With that in mind, we propose to filter large clusters out of our analysis. Specifically, we pruned the 25 largest clusters. Note that their sizes vary from 718 to 583, while the 26” largest cluster has only 70 levels. For the sake of completeness, these clusters are detailed in Table 8. has only 70 levels. For the sake of completeness, these clusters are detailed in Table 8. NARI CEE CEE TE Ee Ia RUS eV VV EDO EEE ge 4 YB OEM TD EEE, PE SY bd reports the updated rank of clusters according to their sizes. Note that many tiny clusters also exist; they tend to be irrelevant due to their low representativity. With that in mind, we propose to use the rule of thumb 80/20 that comes from the Pareto principle (MANDELBROT, 1960) to perform one final round of pruning. The Pareto principle states that, for many events, roughly 80% of the effects come from 20% of the causes. It has been shown that many natural phenomena exhibit this property (NEWMAN, 2005). Due to this fact, the rule 80/20 is commonly used to select the most relevant examples, i.e., to “cut the ong tail’, from data that exhibit skewed distributions, like the one shown in Figure 34. Therefore, we selected for our analysis the 59 largest clusters
Table 8 — Top-25 clusters according to their sizes In the previous paragraph, we aimed at removing irrelevant clusters that were formed by the absence of game objects. Now, we focus on pruning irrelevant clusters based on their sizes; that is, based on the number of game levels that they contain. Figure 33 reports the rank of clusters sorted according to their sizes. As can be seen, there are a few large clusters that do not present much information because they include nearly all of the 720 game levels of our dataset. With that in mind, we propose to filter large clusters out of our analysis. Specifically, we pruned the 25 largest clusters. Note that their sizes vary from 718 to 583, while the 26” largest cluster has only 70 levels. For the sake of completeness, these clusters are detailed in Table 8. has only 70 levels. For the sake of completeness, these clusters are detailed in Table 8. NARI CEE CEE TE Ee Ia RUS eV VV EDO EEE ge 4 YB OEM TD EEE, PE SY bd reports the updated rank of clusters according to their sizes. Note that many tiny clusters also exist; they tend to be irrelevant due to their low representativity. With that in mind, we propose to use the rule of thumb 80/20 that comes from the Pareto principle (MANDELBROT, 1960) to perform one final round of pruning. The Pareto principle states that, for many events, roughly 80% of the effects come from 20% of the causes. It has been shown that many natural phenomena exhibit this property (NEWMAN, 2005). Due to this fact, the rule 80/20 is commonly used to select the most relevant examples, i.e., to “cut the ong tail’, from data that exhibit skewed distributions, like the one shown in Figure 34. Therefore, we selected for our analysis the 59 largest clusters
Table 9 — Final set of clusters selected for analysis
Table 9 — Final set of clusters selected for analysis
Source: Elaborated by the author.
Source: Elaborated by the author.
5.3.1.3 Analyzing Clusters of Similar Game Levels
5.3.1.3 Analyzing Clusters of Similar Game Levels
Table 10 — Frequent itemsets sorted by their support study, a frequent itemset refers to one combination of game objects that occurs in many game levels. Provided that there are 720 levels, the aforementioned procedure allowed us to identify every combination of objects that appears in at least 36 distinct levels. In total, 93,825 com- binations were identified. Among them, only seven combinations were also considered to be interesting in the clustering-based analysis. They are reported in Table 10. at least half of the occurrences of X. In total, 20 rules were found. They are reported in Table 11
Table 10 — Frequent itemsets sorted by their support study, a frequent itemset refers to one combination of game objects that occurs in many game levels. Provided that there are 720 levels, the aforementioned procedure allowed us to identify every combination of objects that appears in at least 36 distinct levels. In total, 93,825 com- binations were identified. Among them, only seven combinations were also considered to be interesting in the clustering-based analysis. They are reported in Table 10. at least half of the occurrences of X. In total, 20 rules were found. They are reported in Table 11
Table 11 — Association rules sorted by their confidence
Table 11 — Association rules sorted by their confidence
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