Concepts inAn efficient rigorous approach for identifying statistically significant frequent itemsets

Statistical significance

In statistics, a result is called statistically significant if it is unlikely to have occurred by chance. The phrase test of significance was coined by Ronald Fisher. As used in statistics, significant does not mean important or meaningful, as it does in everyday speech. Research analysts who focus solely on significant results may miss important response patterns which individually may fall under the threshold set for tests of significance.  more from Wikipedia

Efficiency (statistics)

In statistics, efficiency is a term used in the comparison of various statistical procedures and, in particular, it refers to a measure of the optimality of an estimator, of an experimental design or of an hypothesis testing procedure. Essentially, a more efficient estimator, experiment or test needs fewer samples than a less efficient one to achieve a given performance.  more from Wikipedia

False discovery rate

False discovery rate (FDR) control is a statistical method used in multiple hypothesis testing to correct for multiple comparisons. In a list of rejected hypotheses, FDR controls the expected proportion of incorrectly rejected null hypotheses. It is a less conservative procedure for comparison than the Bonferroni correction, with greater power than familywise error rate (FWER) control, at a cost of increasing the likelihood of obtaining type I errors.  more from Wikipedia

Poisson distribution

In probability theory and statistics, the Poisson distribution is a discrete probability distribution that expresses the probability of a given number of events occurring in a fixed interval of time and/or space if these events occur with a known average rate and independently of the time since the last event. (The Poisson distribution can also be used for the number of events in other specified intervals such as distance, area or volume.  more from Wikipedia

Association rule learning

In data mining, association rule learning is a popular and well researched method for discovering interesting relations between variables in large databases. Piatetsky-Shapiro describes analyzing and presenting strong rules discovered in databases using different measures of interestingness. Based on the concept of strong rules, Agrawal et al.  more from Wikipedia

Data mining

Data mining (the analysis step of the "Knowledge Discovery in Databases" process, or KDD), a relatively young and interdisciplinary field of computer science, is the process that results in the discovery of new patterns in large data sets. It utilizes methods at the intersection of artificial intelligence, machine learning, statistics, and database systems.  more from Wikipedia

Probability distribution

In probability theory, a probability mass, probability density, or probability distribution is a function that describes the probability of a random variable taking certain values. For a more precise definition one needs to distinguish between discrete and continuous random variables. In the discrete case, one can easily assign a probability to each possible value: when throwing a die, each of the six values 1 to 6 has the probability 1/6.  more from Wikipedia