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Table 3.4. An example database for FP-growthTid Transaction Ordered Transaction100 {a, b, d, e, f} {b, d, f, a, e}200 {b, f, g} {b, f, g}300 {d, g, h, i} {d, g}400 {a, c, e, g, j} {g, a, e}500 {b, d, f} {b, d, f}3.1 Association Rule Mining 35header tablerootitem support node-linkb 3d 3f 3g 3a 2e 2d:2f:2b:3f:1g:1d:1g:1g:1a:1e:1a:1e:1Fig. 3.2. FP-tree of the example databaseconsists of the set of prefix paths in the FP-tree co-occurring with the suffix pattern), thenconstructing its conditional FP-tree and performing mining recursively on such a tree. Thepattern growth is achieved by the concatenation of the suffix pattern with the frequent patternsgenerated from a conditional FP-tree.Example 2. Let our example database be the database shown in Table 3.4 with min sup =2.First, the supports of all items are accumulated and all infrequent items are removed from thedatabase. The items in the transactions are reordered according to the support in descendingorder, resulting in the transformed database shown in Table 3.4. The FP-tree for this databaseis shown in Figure 3.2. The pseudo code of the FP-growth algorithm is presented in Algorithm3.3 [107].Although the authors of the FP-growth algorithm [107] claim that their algorithm does notgenerate any candidate itemsets, some works (e.g., [102]) have shown that the algorithm actuallygenerates many candidate itemsets since it essentially uses the same candidate generationtechnique as is used in Apriori but without its prune step. Another issue of FP-tree is that theconstruction of the frequent pattern tree is a time consuming activity.Algorithm 3.3: FP-growthInput:A transaction database D, a frequent pattern tree FP-tree, a user specified thresholdmin supOutput: Frequent itemsets F