Fast updating frequent itemset

They are based on a FP-tree with each node encoding with pre-order traversal and post-order traversal.

Compared with Node-lists, N-lists and Nodesets are more efficient.

To solve the second problem they proposed devising a dynamic FP-tree reordering algorithm, and employing this algorithm whenever a “promotion” to a higher order of at least one item is detected.

fast updating frequent itemset-21

First the projected tree for D is recursively processed, projecting trees for DA, DE and DB.The original example can be viewed in To build the FP-Tree, frequent items support are first calculated and sorted in decreasing order resulting in the following list: .Thereafter, the FP-Tree is iteratively constructed for each transaction, using the sorted list of items as shown in Figure 2. As presented in Figure 3, the initial call to FP-Growth uses the FP-Tree obtained from the Algorithm 1, presented in Figure 2 (f), to process the projected trees in recursive calls to get the frequent patterns in the transactions presented before.After constructing the FP-Tree it’s possible to mine it to find the complete set of frequent patterns.To accomplish this job, Han in When the FP-tree contains a single prefix-path, the complete set of frequent patterns can be generated in three parts: the single prefix-path P, the multipath Q, and their combinations (lines 01 to 03 and 14).

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