# MISFP-Growth: Hadoop-Based Frequent Pattern Mining with Multiple Item Support
## 3 Hadoop-Based Frequent Pattern Mining Architecture
Step 5 builds a conditional MISFP-tree for each suffix item using the MISFP-growth algorithm. For each sub-transaction DB, the mappers in 4 output the frequency patterns that are greater than MIN-MIS, and send them to the reducer nodes for further aggregation. Step 6: the aggregation of frequency pattern results by reducers. In Step 6, the reducer nodes check the merged results for duplicates and remove the repeated items from the output results. Step 7: obtain the useful association rules. As shown in 5 of Figure 1, the MISFP-growth algorithm then discovers association rules by the mining FP-tree. Item support exerts a substantial influence on the results of FP mining. It is difficult but crucial to set appropriate item support thresholds. Often, item support is either determined subjectively by decision makers or it is established by trial and error through recurring adjustments. However, as the dataset is large, such a readjustment process becomes time-consuming with lots of loading. To resolve the constraints and complications of multiple item support setting, we propose the concept of classification of items (COI), which categorizes items of higher homogeneity into the same class. The main idea of COI is that the support of a specific item is equal to the support of a particular  
Figure 1: Hadoop-based frequent pattern growth mining architecture. class that the item belongs to. The concept of COI enables decision makers to set support for different items by setting class support individually. The algorithm and parameter definition of COI are listed in Figure 2 and Table 2. The support of \(j\)th item is obtained by: \(\sum X_{ij}\in\{1,\,n+1\}\), \(\forall_{j}=1,\,2,\,\ldots,\,m\). The pseudo code of COI is shown in Figure 2. For illustration, assume that ten items (including: glass cleaner, shower gel, chocolate, whiskey, cleaning rags, cleansing milk, chewing gum, broom, shampoo, and popcorn) are represented by I\({}_{1}\), I\({}_{2}\),..., I\({}_{10}\), as shown in Table 3. There are three support levels for the product classes: C\({}_{1}\): home cleaning = 0.1, C\({}_{2}\): beauty cosmetic = 0.15, and C\({}_{3}\): snacks = 0.2. Take I\({}_{1}\) as an example: as X\({}_{11}\) = 1 represents I\({}_{1}\) is categorized as the C\({}_{1}\) class, then the item support of I\({}_{1}\) is equal to 0.1. Conversely, looking at I\({}_{4}\) as an example: as X\({}_{14}\) = 4, from n + 1, this means that I\({}_{4}\) does not belong to any product class. Therefore, decision makers must manually define item support, in this example, the item support value of I\({}_{4}\) = 0.05. Thus, COI can quickly set the multiple item support thresholds. In this example, COI categorized items as follows: \(\{\)I\({}_{1}\), I\({}_{5}\), I\({}_{8}\): C\({}_{1}\) = 0.1\(\}\), I\({}_{2}\), I\({}_{6}\), I\({}_{9}\): C\({}_{2}\) = 0.15\(\}\), I\({}_{3}\), I\({}_{7}\), I\({}_{10}\): C\({}_{3}\) = 0.2\(\}\), and \(\{\)I\({}_{4}\): C\({}_{4}\) = 0.05\(\}\). Finally, confidence is used as an indicator to validate the meaning and reference values of the mining results [26; 27]. However, higher confidence does not necessarily indicate that the rule represents a strong correlation. To this end, we used the _lift_ indicator to evaluate the correlation and accuracy between items and association rules [28].