Sentence examples for frequent sequences from from inspiring English sources

At last, in the CEV phase, we extract candidate maximal frequent sequences from the discovered expected frequent sequences, and use a splitting-based technique to verify which candidates are actually frequent in the input database.

A sequence mining algorithm can extract frequent sequences from a corpus.

Then, it iteratively scans the data to collect the support count in order to select the length- k+1) frequent sequences from the length- k+1equent sequences.

Moreover, there are approaches that do not distinguish accidental (less frequent) sequences from sequences that can be really seen as patterns, providing less useful recommendations.

The number of cytosines in the 7-bp tract varied from 6 to 13 and the most frequent sequences for the D310 region are C7TC6, C8TC6 and C9TC6.

However, using these databases in the problem of mining sequential rules from frequent sequences, we consider each itemset in these databases is a data sequence.

The study in [18] has proposed a method for generating a complete set of sequential rules from frequent sequences and removing redundant rules in the post-mining phase.

In our approach (MSR_PreTree, IMSR_PreTree), the set of rules is generated from frequent sequences, which is organized and stored in a prefix-tree structure as illustrated in Fig. 1.

IMSR_PreTree also generates rules from frequent sequences stored in a prefix-tree but it prunes the sub trees which give non-significant rules very early in the process of rule generation and avoids tree scanning as much as possible.

After that, we generate all rules from those frequent sequences.

Table 2 show the result of sequential rule generation from all frequent sequences stored in the prefix-tree.