Pattern evaluation method for frequent pattern mining-data mining

Frequent pattern mining can be a lot of patterns, but judging whether a pattern is interesting requires a pattern evaluation method. The common pattern evaluation methods are described below. (Hypothetical set of items A, B)

1. Support Degree

The ratio of the number of tuples in the item set A and B to the number of all tuples, typically P (a∪b).

2. Reliability

The confidence level of mode a--> B is P (b| A

3. Lifting Degree

Lift (A, B) = P (a∪b)/(P () *p (B)), the elevation greater than 1 is positive correlation, less than 1 is negative correlation, equals 1 is independent.

4, the card side measurement

5. Full confidence degree

All_conf (A, B) = Min{p (a| B), P (b| A)}

6. Maximum confidence level

Max_conf (A, B) = Max{p (a| B), P (b| A)}

7, Kulczynski measurement

KULC (A, B) = 1/(2* P (a| B) + P (b| A

8, cosine measurement

cosine = sqrt (P (a| B) * P (b| A))

In these measurements, only the elevation and the cardholder measures are not 0 invariant measures, but other metrics also face an imbalance problem, introducing an imbalance ratio:

Where the SUP represents the confidence level.