The effectiveness of the proposed clustering technique is more visible in dense databases.
It is efficient on dense databases but not on sparse ones.
First, it is easier to store because its cardinality is much smaller than the size of the class of all frequent itemsets, especially for dense databases.
In the case of dense databases, tries are used as this type of base generates few branches and intensifies mining efficiency.
This study presents an adaptive mechanism to select suitable data structures depending on database densities: the Frequent Pattern List (FPL) for sparse databases, and the Transaction Pattern List (TPL) for dense databases.
Our proposed method, named ShaFEM, combines two mining strategies and applies the most appropriate one to each data subset of the database to efficiently adapt to the data characteristics and run fast on both sparse and dense databases.
The FP-growth algorithm uses an FP-tree data structure, which creates a sub-tree for each recursive call and favours the mining of frequent itemsets from dense databases [2].
Extensive experiments on real-life and synthetic databases show that FMaxSM, FGenCloSM and MaxGenCloSM are up to two orders of magnitude faster than the state-of-the-art algorithms and that the proposed algorithms consume much less memory, especially for low minimum support thresholds and for dense databases.
For example, a recent dense-database study of the transitive causative construction (Theakston, Maslen, Lieven, & Tomasello, 2012) failed to find a single overgeneralization error of the type observed in Bowerman's (1988) diary study (* He giggled me, *She came it over there and * I'm just gonna fall this on her).
However, to obtain a detailed fault shape and to discuss the cause of a series of earthquakes tectonically, in addition to discussions on change of the Bouguer density, it is effective to use a dense gravity database or to conduct gravity gradiometer survey and seismic reflection survey around fault zones in the future studies.
Some algorithms, such as H-Mine [12] and OpportuneProject [13], make use of two types of structures to mine dense and sparse databases.
