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In the mining algorithm phase, a recursion method to construct a direct association pattern tree is addressed with an asymmetric mutual information table, and a recursive mining algorithm to find frequent items.
In a pattern growth algorithm, a pattern tree is constructed, where each node corresponds to a pattern (Fig. 2).
To avoid generation of useless patterns, the pattern tree is pruned as soon as the support falls below 2. Also, the tree is pruned if there is no string in I X) that matches X with zero offset.
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Given a pattern X t of length t, its children in the pattern tree are generated similarly as follows.
In experimental studies, we compare our method to a state-of-the-art tree-based classifier for learning from data streams, showing that evolving pattern trees are competitive in terms of performance while typically producing smaller and more compact models.
In [8], the TMP (Temporal Movement Patterns)—Tree was proposed to efficiently discover the temporal movement patterns of objects in wireless sensor networks.
Firstly, the CP (Colossal Pattern -tree is designed.
A new data structure called pattern-inversion tree is proposed to store related information so that only one scan of database is required.
With 28 different lighting patterns, this tree is far better adapted for Australia's Southern Hemisphere winters than an imported evergreen.
In this paper, the high utility pattern tree (HUP tree) is designed and the HUP-growth mining algorithm is proposed to derive high utility patterns effectively and efficiently.
These approaches, aiming to produce a tree-like pattern whether the tree is the right model for representing evolution or not, are derived from a tree-thinking perspective.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com