About this Document

Latest update: January 10, 2024

Please cite this document as Michael Hahsler, A Probabilistic Comparison of Commonly Used Interest Measures for Association Rules, 2015, URL: https://mhahsler.github.io/arules/docs/measures

A PDF version of this document is available at https://mhahsler.github.io/arules/docs/measures.pdf. An annotated bibliography of association rules can be found at https://mhahsler.github.io/arules/docs/association_rules.html.

CC BY-SA 4.0 This work is licensed under the Creative Commons Attribution Share Alike 4.0 International License.

Code and Implementation

All measures discussed on this page are implemented in function interestMeasure() in the freely available software:

Corrections and Feedback

For corrections and missing measures on this page or in the implementation in the package arules, please open an issue on GitHub or contact me directly.


Agrawal, Imielinski, and Swami (1993) define association rule mining in the following way:

Let \(I=\{i_1, i_2,\ldots,i_m\}\) be a set of \(m\) binary attributes called items. Let \(D = \{t_1, t_2, \ldots, t_n\}\) be a set of transactions called the database. Each transaction \(t \in D\) has a unique transaction ID and contains a subset of the items in \(I\), i. e., \(t \subseteq I\). A rule is defined as an implication of the form \(X \Rightarrow Y\) where \(X, Y \subseteq I\) and \(X \cap Y = \emptyset\). The sets of items (for short itemsets) \(X\) and \(Y\) are called antecedent (left-hand side or LHS) and consequent (right-hand side or RHS) of the rule, respectively. Measures of importance (interest) can be defined for itemsets and rules. The support-confidence framework defines the measures support and confidence. Rules that satisfy a user-specified minimum thresholds on support and confidence are called association rules.

Interest measures are usually defined in terms of itemset support, here we also present them using probabilities and, where appropriate, counts. The probability \(P(E_X)\) of the event that all items in itemset \(X\) are contained in an arbitrarily chosen transaction can be estimated from a database \(D\) using maximum likelihood estimation (MLE) by

\[\hat{P}(E_X) = \frac{|\{t \in D; X \subseteq t\}|}{n}\]

where \(n_X = |\{t \in D; X \subseteq t\}|\) is the count of the number of transactions that contain the itemset \(X\) and \(n = |D|\) is the size (number of transactions) of the database. For conciseness of notation, we will drop the hat and the \(E\) from the notation for probabilities. We will use in the following \(P(X)\) to mean \(\hat{P}(E_X)\) and \(P(X \cap Y)\) to mean \(\hat{P}(E_X \cap E_Y) = \hat{P}(E_{X \cup Y})\), the probability of the intersection of the events \(E_X\) and \(E_Y\) representing the probability of the event that a transaction contains all items in the union of the itemsets \(X\) and \(Y\). The event notation should not be confused with the set notation used in measures like support, where \(supp(X \cup Y)\) means the support of the union of the itemsets \(X\) and \(Y\).

Note on probability estimation: The used probability estimates will be very poor for itemsets with low observed frequencies. This needs to be always taken into account since it affects most measured discussed below.

Note on null-transactions: Transaction datasets typically contain a large number of transactions that do not contain either \(X\) or \(Y\). These transactions are called null-transactions, and it is desirable that measures of rule strength are not influenced by a change in the number of null-transactions. However, most measures are affected by the number of null-transactions since the total number of transactions is used for probability estimation. Measures that are not influenced by a change in the number of null-transactions are called null-invariant (Tan, Kumar, and Srivastava 2004; Wu, Chen, and Han 2010).

Good overview articles about different association rule measures are

  • Tan, Kumar, and Srivastava (2004) Selecting the right objective measure for association analysis. Information Systems, 29(4):293-313, 2004

  • Geng and Hamilton (2006) Interestingness measures for data mining: A survey. ACM Computing Surveys, 38(3):9, 2006.

  • Lenca et al. (2007) Association Rule Interestingness Measures: Experimental and Theoretical Studies. Studies in Computational Intelligence (SCI) 43, 51–76, 2007.

Measures Defined on Itemsets


Reference: Agrawal, Imielinski, and Swami (1993)

\[ supp(X) = \frac{n_X}{n} = P(X) \]

Support is defined on itemsets and gives the proportion of transactions that contain \(X\). It is used as a measure of significance (importance) of an itemset. Since it uses the count of transactions, it is often called a frequency constraint. An itemset with support greater than a set minimum support threshold, \(supp(X) > \sigma\), is called a frequent or large itemset.

For rules the support defined as the support of all items in the rule, i.e., \(supp(X \Rightarrow Y) = supp(X \cup Y) = P(X \cap Y)\).

Support’s main feature is that it possesses the downward closure property (anti-monotonicity), which means that all subsets of a frequent set are also frequent. This property (actually, the fact that no superset of an infrequent set can be frequent) is used to prune the search space (usually thought of as a lattice or tree of itemsets with increasing size) in level-wise algorithms (e.g., the Apriori algorithm).

The disadvantage of support is the rare item problem. Items that occur very infrequently in the data set are pruned, although they would still produce interesting and potentially valuable rules. The rare item problem is important for transaction data which usually have a very uneven distribution of support for the individual items (typical is a power-law distribution where few items are used all the time and most items are rarely used).

Range: \([0, 1]\)

Support Count

Alias: Absolute Support Count

Range: \([0, n]\) where \(n\) is the number of transactions.


Reference: Omiecinski (2003)

All-confidence is defined on itemsets (not rules) as

\[\textrm{all-confidence}(X) = \frac{supp(X)}{max_{x \in X}(supp(x))} = \frac{P(X)}{max_{x \in X}(P(x))} = min\{P(X|Y), P(Y|X)\}\]

where \(max_{x \in X}(supp(x \in X))\) is the support of the item with the highest support in \(X\). All-confidence means that all rules which can be generated from itemset \(X\) have at least a confidence of \(\textrm{all-confidence}(X)\). All-confidence possesses the downward-closed closure property and thus can be effectively used inside mining algorithms. All-confidence is null-invariant.

Range: \([0, 1]\)

Cross-Support Ratio

Reference: Xiong, Tan, and Kumar (2003)

Defined on itemsets as the ratio of the support of the least frequent item to the support of the most frequent item, i.e.,

\[\textrm{cross-support}(X) = \frac{min_{x \in X}(supp(x))}{max_{x \in X}(supp(x))}\]

a ratio smaller than a set threshold. Normally many found patterns are cross-support patterns which contain frequent as well as rare items. Such patterns often tend to be spurious.

Range: \([0, 1]\)

Measures Defined on Rules

Contingency Table

A \(2 \times 2\) contingency table with counts for rule \(X \Rightarrow Y\) in the transaction dataset. The counts are:

\(Y\) \(\overline{Y}\)
\(X\) \(n_{XY}\) \(n_{X\overline{Y}}\)
\(\overline{X}\) \(n_{\overline{X}Y}\) \(n_{\overline{X}\overline{Y}}\)

\(n_{XY}\) is the number of transactions that contain all items in \(X\) and \(Y\). All other measures for rules can be calculated using these counts.


Alias: Strength

Reference: Agrawal, Imielinski, and Swami (1993)

\[conf(X \Rightarrow Y) = \frac{supp(X \Rightarrow Y)}{supp(X)} = \frac{supp(X \cup Y)}{supp(X)} = \frac{n_{XY}}{n_X} = \frac{P(X \cap Y)}{P(X)} = P(Y | X)\]

Confidence is defined as the proportion of transactions that contain \(Y\) in the set of transactions that contain \(X\). This proportion is an estimate for the probability of seeing the rule’s consequent under the condition that the transactions also contain the antecedent.

Confidence is directed and gives different values for the rules \(X \Rightarrow Y\) and \(Y \Rightarrow X\). Association rules have to satisfy a minimum confidence constraint, \(conf(X \Rightarrow Y) \ge \gamma\).

Confidence is not downward closed and was developed together with support by Agrawal et al. (the so-called support-confidence framework). Support is first used to find frequent (significant) itemsets exploiting its downward closure property to prune the search space. Then confidence is used in a second step to produce rules from the frequent itemsets that exceed a min. confidence threshold.

A problem with confidence is that it is sensitive to the frequency of the consequent \(Y\) in the database. Caused by the way confidence is calculated, consequents with higher support will automatically produce higher confidence values even if there exists no association between the items.

Range: \([0, 1]\)

Added Value

Alias: AV, Pavillon Index, Centered Confidence

Reference: Tan, Kumar, and Srivastava (2004)

Quantifies how much the probability of \(Y\) increases when conditioning on the transactions that contain \(X\) Defined as

\[AV(X \Rightarrow Y)) = conf(X \Rightarrow Y) - supp(Y) = P(Y | X) - P(Y)\]

Range: \([-.5, 1]\)

Casual Confidence

Reference: Kodratoff (2001)

Confidence reinforced by negatives given by

\[\textrm{casual-conf} = \frac{1}{2} [conf(X \Rightarrow Y) + conf(\overline{X} \Rightarrow \overline{Y})] = \frac{1}{2} [P(Y|X) + P(\overline{Y}|\overline{X})]\]

Range: \([0, 1]\)

Casual Support

Reference: Kodratoff (2001)

Support improved by negatives given by

\[\textrm{casual-supp} = supp(X \cup Y) + supp(\overline{X} \cup \overline{Y}) = P(X \cap Y) + P(\overline{X} \cap \overline{Y})\]

Range: \([0, 2]\)

Centered Confidence

Alias: relative accuracy, gain

Reference: Lavrač, Flach, and Zupan (1999)

\[CC(X \Rightarrow Y) = conf(X \Rightarrow Y) - supp(Y)\]

Range: \([-1, 1 - 1/n]\)

Certainty Factor

Alias: CF, Loevinger

Reference: Galiano et al. (2002)

The certainty factor is a measure of the variation of the probability that \(Y\) is in a transaction when only considering transactions with \(X\). An increasing CF means a decrease in the probability that \(Y\) is not in a transaction that \(X\) is in. Negative CFs have a similar interpretation.

\[CF(X \Rightarrow Y) = \frac{conf(X \Rightarrow Y)-supp(Y)}{supp(\overline{Y})} = \frac{P(Y|X)-P(Y)}{1-P(Y)}\]

Range: \([-1, 1]\) (0 indicates independence)


Reference: Brin, Motwani, and Silverstein (1997)

For the analysis of \(2 \times 2\) contingency tables, the chi-squared test statistic is a measure of the relationship between two binary variables (\(X\) and \(Y\)). The chi-squared test statistic is used as a test for independence between \(X\) and \(Y\). The chi-squared test statistic is:

\[ \begin{aligned} \textrm{chi-squared}(X \Rightarrow Y) & = \sum_i \frac{(O_i - E_i)^2}{E_i} \\ & = \frac{\left( n_{XY} - \frac{n_X n_Y}{n} \right)^2}{\frac{n_X n_Y}{n}} + \frac{\left( n_{\overline{X}Y} - \frac{n_{\overline{X}} n_Y}{n} \right)^2}{\frac{n_{\overline{X}} n_Y}{n}} + \frac{\left( n_{X\overline{Y}} - \frac{n_X n_{\overline{Y}}}{n} \right)^2}{\frac{n_X n_{\overline{Y}}}{n}} + \frac{\left( n_{\overline{X}\overline{Y}} - \frac{n_{\overline{X}} n_{\overline{Y}}}{n} \right)^2}{\frac{n_{\overline{X}} n_{\overline{Y}}}{n}} \\ & = n \frac{P(X \cap Y)P(\overline{X} \cap \overline{Y}) - P(X \cap \overline{Y})P(\overline{X} \cap Y)}{\sqrt{P(X)P(Y)P(\overline{X})P(\overline{Y})}} \end{aligned} \]

\(O_i\) is the observed count of contingency table cell \(i\) and \(E_i\) is the expected count given the marginals.
The statistic has approximately a \(\chi^2\) distribution with 1 degree of freedom (for a 2x2 contingency table). The critical value for \(\alpha=0.05\) is \(3.84\); higher chi-squared values indicate that the null-hypothesis of independence between LHS and the RHS should be rejected (i.e., the rule is not spurious). Larger chi-squared values indicate stronger evidence that the rule represents a strong relationship. The statistic can be converted into a p-value using the \(\chi^2\) distribution.

Notes: The contingency tables for some rules may contain cells with low expected values (less then 5) and thus Fisher’s exact test might be more appropriate. Each rule represents a statistical test, and correction for multiple comparisons may be necessary.

Range: \([0, \infty]\)

Collective Strength

Reference: Aggarwal and Yu (1998)

\[S(X) = \frac{1-v(X)}{1-E[v(X)]} \frac{E[v(X)]}{v(X)} = \frac{P(X \cap Y)+P(\overline{Y}|\overline{X})} {P(X)P(Y)+P(\overline{X})P(\overline{Y})} \]

where \(v(X)\) is the violation rate and \(E[v(X)]\) is the expected violation rate for independent items. The violation rate is defined as the fraction of transactions that contain some of the items in an itemset but not all. Collective strength gives 0 for perfectly negative correlated items, infinity for perfectly positive correlated items, and 1 if the items co-occur as expected under independence.

Problematic is that for items with medium to low probabilities, the observations of the expected values of the violation rate is dominated by the proportion of transactions that do not contain any of the items in \(X\). For such itemsets, collective strength produces values close to one, even if the itemset appears several times more often than expected together.

Range: \([0, \infty]\)

Confidence Boost

Reference: Balcázar (2013)

Confidence boost is the ratio of the confidence of a rule to the confidence of any more general rule (i.e., a rule with the same consequent but one or more items removed in the LHS).

\[\textrm{confidence-boost}(X \Rightarrow Y) = \frac{conf(X \Rightarrow Y)}{max_{X' \subset X}(conf(X' \Rightarrow Y))} = \frac{conf(X \Rightarrow Y)}{conf(X \Rightarrow Y) - improvement(X \Rightarrow Y)} \]

Values larger than 1 mean the new rule boosts the confidence compared to the best, more general rule. The measure is related to the improvement measure.

Range: \([0, \infty]\) (\(>1\) indicates a rule with confidence boost)


Reference: Brin et al. (1997)

\[\mathrm{conviction}(X \Rightarrow Y) =\frac{1-supp(Y)}{1-conf(X \Rightarrow Y)} = \frac{P(X)P(\overline{Y})}{P(X \cap \overline{Y})}\]

where \(\overline{Y} = E_{\neg Y}\) is the event that \(Y\) does not appear in a transaction. Conviction was developed as an alternative to confidence which was found to not capture the direction of associations adequately. Conviction compares the probability that \(X\) appears without \(Y\) if they were dependent on the actual frequency of the appearance of \(X\) without \(Y\). In that respect, it is similar to lift (see the section about lift on this page). However, in contrast to lift, it is a directed measure since it also uses the information of the absence of the consequent. An interesting fact is that conviction is monotone in confidence and lift.

Range: \([0, \infty]\) (1 indicates independence; rules that always hold have \(\infty\))


Reference: Tan, Kumar, and Srivastava (2004)

Cosine is a null-invariant measure of correlation between the items in \(X\) and \(Y\) defined as

\[\mathrm{cosine}(X \Rightarrow Y) = \frac{supp(X \cup Y)}{\sqrt{(supp(X)supp(Y))}} = \frac{P(X \cap Y)}{\sqrt{P(X)P(Y)}} = \sqrt{P(X | Y) P(Y | X)}\]

Range: \([0, 1]\) (\(0.5\) means no correlation)


Alias: LHS Support

It measures the probability that a rule \(X \Rightarrow Y\) applies to a randomly selected transaction. It is estimated by the proportion of transactions that contain the antecedent of the rule \(X \Rightarrow Y\). Therefore, coverage is sometimes called antecedent support or LHS support.

\[\mathrm{cover}(X \Rightarrow Y) = supp(X) = P(X)\]

Range: \([0, 1]\)

Descriptive Confirmed Confidence

Reference: Tan, Kumar, and Srivastava (2004)

Confidence confirmed by the confidence of the negative rule.

\[\textrm{confirmed-conf} = conf(X \Rightarrow Y) - conf(X \Rightarrow \overline{Y}) = P(Y|X) - P(\overline{Y}|X)\]

Range: \([-1, 1]\)

Difference of Confidence

Alias: DOC, Difference of Proportions

Reference: Hofmann and Wilhelm (2001)

The difference of confidence is the difference of the proportion of transactions containing \(Y\) in the two groups of transactions that do and do not contain \(X\). For the analysis of \(2 \times 2\) contingency tables, this measure of the relationship between two binary variables is typically called the difference of proportion. It is defined as \[ \mathrm{doc}(X \Rightarrow Y) = conf(X \Rightarrow Y) - conf(\overline{X} \Rightarrow Y) = P(Y|X) - P(Y|\overline{X}) = n_{XY} / n_X - n_{\overline{X}Y} / n_{\overline{X}} \]

Range: \([-1, 1]\) (0 means statistical independence)

Example and Counter-Example Rate

Example rate reduced by the counter-example rate.

Defined as \[\mathrm{ecr}(X \Rightarrow Y) = \frac{n_{XY} - n_{X\overline{Y}}}{n_{XY}} = \frac{P(X \cap Y) - P(X \cap \overline{Y})}{P(X \cap Y)} = 1 - \frac{1}{sebag(X \Rightarrow Y)} \]

The measure is related to the Sebag-Schoenauer Measure.

Range: \([0, 1]\)

Fisher’s Exact Test

Reference: Hahsler and Hornik (2007)

If \(X\) and \(Y\) are independent, then the \(n_{XY}\) is a realization of the random variable \(C_{XY}\) which has a hypergeometric distribution with \(n_Y\) draws from a population with \(n_X\) successes and \(n_{\overline{X}}\) failures. The p-value for Fisher’s one-sided exact test giving the probability of observing a contingency table with a count of at least \(n_{XY}\) given the observed marginal counts is

\[ \textrm{p-value} = P(C_{XY} \ge n_{XY}) \]

The p-value is related to hyper-confidence. Compared to the Chi-squared test, Fisher’s exact test also applies when cells have low expected counts. Note that each rule represents a statistical test, and correction for multiple comparisons may be necessary.

Range: \([0, 1]\) (p-value scale)

Generalized Improvement

Reference: Hahsler et al. (2023)

Generalizes the improvement measure to arbitrary interest measures.

\[ \mathrm{generalizedImprovement}(X \Rightarrow Y) = min_{X' \subset X}(M(X \Rightarrow Y) - M(X' \Rightarrow Y)) \]

where \(M\) can be any measure that increases with interestingness. The original definition of improvement uses the measure confidence.

Range: \([-\infty, +\infty]\) (the actual range depends on the used measure)

Generalized Increase Ratio

Reference: Hahsler et al. (2023)

Generalizes lift increase to arbitrary interest measures.

\[ \mathrm{INC}(X \Rightarrow Y) = min_{X' \subset X} \left[ \frac{M(X \Rightarrow Y)}{M(X' \Rightarrow Y)} \right] \]

where \(M\) can be any measure of interestingness. The original definition of lift increase uses the measure lift.

Range: \([0, +\infty]\) (\(> 1\) means an increase)

Gini Index

Reference: Tan, Kumar, and Srivastava (2004)

The Gini index measures quadratic entropy as

\[\mathrm{gini}(X \Rightarrow Y) = P(X) [P(Y|X)^2+P(\overline{Y}|X)^2] + P(\overline{X}) [P(B|\overline{X})^2+P(\overline{Y}|\overline{X})^2] - P(Y)^2 - P(\overline{Y})^2 \]

Range: \([0, 1]\) (0 means that the rule does not provide any information for the dataset)


Reference: Hahsler and Hornik (2007)

The confidence level for observation of too high/low counts for rules \(X \Rightarrow Y\) using the hypergeometric model. Since the counts are drawn from a hypergeometric distribution (represented by the random variable \(C_{XY}\) with known parameters given by the counts \(n_X\) and \(n_Y\), we can calculate a confidence interval for the observed counts \(n_{XY}\) stemming from the distribution. Hyper-confidence reports the confidence level as

\[ \textrm{hyper-conf}(X \Rightarrow Y) = 1 - P[C_{XY} \ge n_{XY} | n_X, n_Y] \]

A confidence level of, e.g., \(> 0.95\) indicates that there is only a 5% chance that the high count for the rule has occurred randomly. Hyper-confidence is equivalent to the statistic used to calculate the p-value in Fisher’s exact test. Note that each rule represents a statistical test and correction for multiple comparisons may be necessary.

Hyper-Confidence can also be used to evaluate that \(X\) and \(Y\) are complementary (i.e., the count is too low to have occurred randomly).

\[ \textrm{hyper-conf}_\textrm{complement}(X \Rightarrow Y) = 1 - P[C_{XY} < n_{XY} | n_X, n_Y] \]

Range: \([0, 1]\)


Reference: Hahsler and Hornik (2007)

Adaptation of the lift measure where instead of dividing by the expected count under independence (\(E[C_{XY}] = n_X / n \times n_Y / n\)) a higher quantile of the hypergeometric count distribution is used. This is more robust for low counts and results in fewer false positives when hyper-lift is used for rule filtering. Hyper-lift is defined as:

\[ \textrm{hyper-lift}_\delta(X \Rightarrow Y) = \frac{n_{XY}}{Q_{\delta}[C_{XY}]} \]

where \(n_{XY}\) is the number of transactions containing \(X\) and \(Y\) and \(Q_{\delta}[C_{XY}]\) is the \(\delta\)-quantile of the hypergeometric distribution with parameters \(n_X\) and \(n_Y\).
\(\delta\) is typically chosen to use the 99 or 95% quantile.

Range: \([0, \infty]\) (1 indicates independence)

Imbalance Ratio

Alias: IR

Reference: Wu, Chen, and Han (2010)

Measures the degree of imbalance between two events that the LHS and the RHS are contained in a transaction. The ratio is close to 0 if the conditional probabilities are similar (i.e., very balanced) and close to 1 if they are very different. It is defined as

\[ \mathrm{IB}(X \Rightarrow Y) = \frac{|P(X|Y) - P(Y|X)|}{P(X|Y) + P(Y|X) - P(X|Y)P(Y|X))} = \frac{|supp(X) - supp(Y)|}{supp(X) + supp(Y) - supp(X \cup Y)} \]

Range: \([0, 1]\) (0 indicates a balanced, typically uninteresting rule)

Implication Index

Reference: Gras et al. (1996)

A variation of the Lerman similarity defined as

\[ \mathrm{gras}(X \Rightarrow Y) = \sqrt{N} \frac{supp(X \cup \overline{Y}) - supp(X)supp(\overline{Y})}{\sqrt{supp(X)supp(\overline{Y})}} \]

Range: \([0, 1]\)


Reference: MS Analysis Services: Microsoft Association Algorithm Technical Reference.

In the Microsoft Association Algorithm Technical Reference, confidence is called “probability,” and a measure called importance is defined as the log-likelihood of the right-hand side of the rule, given the left-hand side of the rule:

\[ \mathrm{importance}(X \Rightarrow Y) = log_{10}(L(X \Rightarrow Y) / L(X \Rightarrow \overline{Y})) \]

where \(L\) is the Laplace corrected confidence.

Range: \([-\infty, \infty]\)


Reference: Bayardo, Agrawal, and Gunopulos (2000)

The improvement of a rule is the minimum difference between its confidence and the confidence of any proper sub-rule with the same consequent. A large positive value indicate that the more specific rule (with an additional item in the LHS) improves the confidence and should be kept. Improvement is often used to filter redundant rules.

\[ \mathrm{improvement}(X \Rightarrow Y) = min_{X' \subset X}(conf(X \Rightarrow Y) - conf(X' \Rightarrow Y)) \]

Range: \([0, 1]\)

Jaccard Coefficient

Reference: Tan, Kumar, and Srivastava (2004)

A null-invariant measure for dependence using the Jaccard similarity index between the two sets of transactions that contain the items in \(X\) and \(Y\), respectively. Defined as

\[ \mathrm{jaccard}(X \Rightarrow Y) = \frac{supp(X \cup Y)}{supp(X) + supp(Y) - supp(X \cup Y)} = \frac{P(X \cap Y)}{P(X)+P(Y)-P(X \cap Y)} \]

Range: \([0, 1]\)


<a href= Smyth and Goodman (1991)

The J-measure is a scaled version of cross entropy to measure the information content of a rule.

\[ J(X \Rightarrow Y) = P(X \cap Y) log\left(\frac{P(Y|X)}{P(Y)}\right) + P(X \cap \overline{Y})log\left(\frac{P(\overline{Y}|X)}{P(\overline{Y})}\right) \]

Range: \([0, 1]\) (0 means that \(X\) does not provide information for \(Y\))


Alias: Cohen’s \(\kappa\)

Reference: Tan, Kumar, and Srivastava (2004)

Cohen’s kappa coefficient of the rule (seen as a classifier) given as the rules observed rule accuracy (i.e., confidence) corrected by the expected accuracy (of a random classifier). Kappa is defined as

\[ \kappa(X \Rightarrow Y) = \frac{P(X \cap Y) + P(\overline{X} \cap \overline{Y}) - P(X)P(Y) - P(\overline{X})P(\overline{Y})}{1- P(X)P(Y) - P(\overline{X})P(\overline{Y})} \]

Range: \([-1,1]\) (0 means the rule is not better than a random classifier)


Reference: Tan, Kumar, and Srivastava (2004)

Defined as a scaled version of the added value measure.

\[ \begin{aligned} \mathrm{klosgen}(X \Rightarrow Y) & = \sqrt{supp(X \cup Y)}\,(conf(X \Rightarrow Y) - supp(Y)) \\ & = \sqrt{P(X \cap Y)}\, (P(Y|X) - P(Y)) \\ & = \sqrt{P(X \cap Y)}\, AV(X \Rightarrow Y) \end{aligned} \]

Range: \([-1, 1]\) (0 for independence)


Reference: Wu, Chen, and Han (2010)

Calculate the null-invariant Kulczynski measure with a preference for skewed patterns.

\[ \begin{aligned} \mathrm{kulc}(X \Rightarrow Y) & = \frac{1}{2} \left(conf(X \Rightarrow Y) + conf(Y \Rightarrow X) \right) = \frac{1}{2} \left(\frac{supp(X \cup Y)}{supp(X)} + \frac{supp(X \cup Y)}{supp(Y)} \right) \\ & = \frac{1}{2} \left(P(X | Y) + P(Y | X) \right) \end{aligned} \]

Range: \([0, 1]\) (0.5 means neutral and typically uninteresting)


Alias: Goodman-Kruskal’s \(\lambda\), Predictive Association

Reference: Tan, Kumar, and Srivastava (2004)

Goodman and Kruskal’s lambda assesses the association between the LHS and RHS of the rule.

\[ \lambda(X \Rightarrow Y) = \frac{\Sigma_{x \in X} max_{y \in Y} P(x \cap y) - max_{y \in Y} P(y)} {n - max_{y \in Y} P(y)} \]

Range: \([0, 1]\)

Laplace Corrected Confidence

Alias: Laplace Accuracy, L

Reference: Tan, Kumar, and Srivastava (2004)

\[ L(X \Rightarrow Y) = \frac{n_{XY}+1}{n_X+k}, \]

where \(k\) is the number of classes in the domain. For association rule \(k\) is often set to 2. It is an approximate measure of the expected rule accuracy representing 1 - the Laplace expected error estimate of the rule. The Laplace corrected accuracy estimate decreases with lower support to account for estimation uncertainty with low counts.

Range: \([0, 1]\)

Least Contradiction

Reference: Azé and Kodratoff (2002)

\[ \textrm{least-contradiction}(X \Rightarrow Y) = \frac{supp(X \cup Y) - supp(X \cup \overline{Y})}{supp(Y)} = \frac{P(X \cap Y) - P(X \cap \overline{Y})}{P(Y)} \]

Range: \([-\infty, 1]\)

Lerman Similarity

Reference: Lerman, I.C. (1981). Classification et analyse ordinale des donnees. Paris.

Defined as

\[ \mathrm{lerman}(X \Rightarrow Y) = \frac{n_{XY} - \frac{n_X n_Y}{n}}{\sqrt{\frac{n_X n_Y}{n}}} = \sqrt{n} \frac{supp(X \cup Y) - supp(X)supp(Y)}{\sqrt{supp(X)supp(Y)}} \]

Range: \([0, 1]\)


Alias: Piatetsky-Shapiro, PS

Reference: Piatetsky-Shapiro (1991) \[ \mathrm{PS}(X \Rightarrow Y) = leverage(X \Rightarrow Y) = supp(X \Rightarrow Y) - supp(X)supp(Y) = P(X \cap Y) - P(X)P(Y) \]

Leverage measures the difference of \(X\) and \(Y\) appearing together in the data set and what would be expected if \(X\) and \(Y\) were statistically dependent. The rationale in a sales setting is to find out how many more units (items \(X\) and \(Y\) together) are sold than expected from the independent sells.

Using minimum leverage thresholds incorporates at the same time an implicit frequency constraint. E.g., for setting a min. leverage thresholds to 0.01% (corresponds to 10 occurrences in a data set with 100,000 transactions) one first can use an algorithm to find all itemsets with min. support of 0.01% and then filter the found item sets using the leverage constraint. Because of this property, leverage also can suffer from the rare item problem.

Leverage is a unnormalized version of the phi correlation coefficient.

Range: \([-1, 1]\) (0 indicates independence)


Alias: Interest, interest factor

Reference: Brin et al. (1997)

Lift was originally called interest by Brin et al. Later, lift, the name of an equivalent measure popular in advertising and predictive modeling became more common. Lift is defined as

\[ \textrm{lift}(X \Rightarrow Y) = \textrm{lift}(Y \Rightarrow X) = \frac{conf(X \Rightarrow Y)}{supp(Y)} = \frac{P(Y | X)}{P(Y)} = \frac{P(X \cap Y)}{P(X)P(Y)} = n \frac{n_{XY}}{n_X n_Y} \]

Lift measures how many times more often \(X\) and \(Y\) occur together than expected if they were statistically independent. A lift value of 1 indicates independence between \(X\) and \(Y\). For statistical tests, see the Chi-squared test statistic, Fisher’s exact test, and hyper-confidence.

Lift is not downward closed and does not suffer from the rare item problem. However, lift is susceptible to noise in small databases. Rare itemsets with low counts (low probability), which by chance occur a few times (or only once) together, can produce enormous lift values.

Range: \([0, \infty]\) (1 means independence)

Lift Increase

Reference: López et al. (2014)

Related to the improvement measure but uses lift. It divides the lift of a rules by the largest lift of any proper sub-rule with the same consequent.

\[ \mathrm{LIC}(X \Rightarrow Y) = min_{X' \subset X} \left[ \frac{lift(X \Rightarrow Y)}{lift(X' \Rightarrow Y)} \right] \]

López et al. (2014) suggests that rules need to satisfy \(LIC > 1.05\) to justify adding an item to the antecedent.

Range: \([0, \infty]\) (\(>1\) means an increase)


Reference: Tan, Kumar, and Srivastava (2004)

Symmetric, null-invariant version of confidence defined as

\[ \textrm{maxConf}(X \Rightarrow Y) = max\{ conf(X \Rightarrow Y),\ conf(Y \Rightarrow X) \} = max\{ P(Y | X),\ P(X | Y) \} \]

Range: \([0, 1]\)

Mutual Information

Alias: Uncertainty

Reference: Tan, Kumar, and Srivastava (2004)

Mutual information measures the information obtained about Y by observing X.

\[ \begin{aligned} M(X \Rightarrow Y) & = \frac{\sum_{i \in \{X, \overline{X}\}} \sum_{j \in \{Y, \overline{Y}\}} \frac{n_{ij}}{n} log \frac{n_{ij}}{n_i n_j}}{min(-\sum_{i \in \{X, \overline{X}\}} \frac{n_i}{n} log \frac{n_i}{n}, -\sum_{j \in \{Y, \overline{Y}\}} \frac{n_j}{n} log \frac{n_j}{n})} \\ & = \frac{\sum_{i \in \{X, \overline{X}\}} \sum_{j \in \{Y, \overline{Y}\}} P(i \cap j) log \frac{P(i \cap j)}{P(i) P(j)}}{min(-\sum_{i \in \{X, \overline{X}\}} P(i) log P(i), -\sum_{j \in \{Y, \overline{Y}\}} P(j) log P(j))} \end{aligned} \]

Range: \([0, 1]\) (0 means that X does not provide information for Y)

Odds Ratio

Reference: Tan, Kumar, and Srivastava (2004)

For the analysis of \(2 \times 2\) contingency tables, the odds ratio is a measure of the relationship between two binary variables. It is defined as the ratio of the odds of a transaction containing Y in the groups of transactions that do and do not contain \(X\).

\[ \mathrm{OR}(X \Rightarrow Y) = \frac{\frac{P(Y | X)}{1 - P(Y | X)}}{\frac{P(Y | \overline{X})}{1 - P(Y | \overline{X})}} = \frac{\frac{conf(X \Rightarrow Y)}{1 - conf(X \Rightarrow Y)}}{\frac{conf(\overline{X} \Rightarrow Y)}{1 - conf(\overline{X} \Rightarrow Y)}} = \frac{n_{XY} n_{\overline{X}\overline{Y}}} {n_{X\overline{Y}} n_{\overline{X}Y}} \]

A confidence interval around the odds ratio can be calculated (Li et al. 2014) using a normal approximation. \[ \omega = z_{\alpha/2} \sqrt{\frac{1}{n_{XY}} + \frac{1}{n_{X\overline{Y}}} + \frac{1}{n_{\overline{X}Y}} + \frac{1}{n_{\overline{X}\overline{Y}}}} \]

\[ \mathrm{CI}(X \Rightarrow Y) = [OR(X \Rightarrow Y) \exp(-\omega), OR(X \Rightarrow Y) \exp(\omega)] \]

where \(\alpha/2\) is the critical value for a confidence level of \(1-\alpha\).

Range: \([0, \infty]\) (1 indicates that Y is not associated with X)

Phi Correlation Coefficient

Reference: Tan, Kumar, and Srivastava (2004)

The Phi correlation coefficient between the transactions containing X and Y represented as two binary vectors. Phi correlation is equivalent to Pearson’s Product Moment Correlation Coefficient \(\rho\) with 0-1 values and related to the chi-squared test statistics for \(2 \times 2\) contingency tables.

\[ \phi(X \Rightarrow Y) = \frac{n n_{XY} - n_Xn_Y}{\sqrt{n_X n_Y n_{\overline{X}} n_{\overline{Y}}}} = \frac{P(X \cap Y) - P(X)P(Y)}{\sqrt{P(X) (1 - P(X)) P(Y) (1 - P(Y))}} \]

In machine learning, Phi correlation is also known as the Matthews correlation coefficient (MCC). The magnitude of the correlation is also related to the chi-squared statistic:

\[ |\phi(X \Rightarrow Y)| = \sqrt{\frac{\chi^2}{n}} \]

Range: \([-1, 1]\) (0 when X and Y are independent)}


Reference: Diatta, Ralambondrainy, and Totohasina (2007)

Defined as the support of the counter examples.

\[ \mathrm{ralambondrainy}(X \Rightarrow Y) = \frac{n_{X\overline{Y}}}{n} = supp(X \Rightarrow Y) = P(X \cap \overline{Y}) \]

Range: \([0, 1]\) (smaller is better)

Relative Linkage Disequilibrium

Reference: Kenett and Salini (2008)

RLD is an association measure motivated by indices used in population genetics. It evaluates the deviation of the support of the whole rule from the support expected under independence given the supports of X and Y.

\[D = \frac{n_{XY} n_{\overline{X}\overline{Y}} - n_{X\overline{Y}} n_{\overline{X}Y}}{n}\]

\[\mathrm{RLD} = \begin{cases} D / (D + min(n_{X\overline{Y}}, n_{\overline{X}Y})) \quad\textrm{if}\quad D>0 \\ D / (D - min(n_{XY}, n_{\overline{X}\overline{Y}})) \quad\textrm{otherwise.}\\ \end{cases}\]

Range: \([0, 1]\)

Relative Risk

Reference: Sistrom and Garvan (2004)

For the analysis of \(2 \times 2\) contingency tables, relative risk is a measure of the relationship between two binary variables. It is defined as the ratio of the proportion of transactions containing \(Y\) in the two groups of transactions the do and do not contain \(X\). In epidemiology, this corresponds to the ratio of the risk of having disease \(Y\) in the exposed (\(X\)) and unexposed (\(\overline{X}\)) groups.

\[ \mathrm{RR}(X \Rightarrow Y) = \frac{n_{XY} / n_X}{n_{\overline{X}Y} / n_{\overline{X}}} = \frac{P(Y | X)}{P(Y | \overline{X})} = \frac{conf(X \Rightarrow Y)}{conf(\overline{X} \Rightarrow Y)} \]

Range: \([0, \infty]\) (\(RR = 1\) means \(X\) and \(Y\) are unrelated)

Rule Power Factor

Reference: Ochin and Kumar (2016)

Weights the confidence of a rule by its support. This measure favors rules with high confidence and high support at the same time.

Defined as \[ \mathrm{rpf}(X \Rightarrow Y) = supp(X \Rightarrow Y)\ conf(X \Rightarrow Y) = \frac{P(X \cap Y)^2}{P(X)} \]

Range: \([0, 1]\)

Right-Hand-Side Support

Alias: RHS support, consequent support

Support of the right-hand-side of the rule.

\[ \mathrm{RHSsupp}(X \Rightarrow Y) = supp(Y) = P(Y) \]

Range: \([0, 1]\)


Reference: Sebag and Schoenauer (1988)

Defined as \[ \mathrm{sebag}(X \Rightarrow Y) = \frac{conf(X \Rightarrow Y)}{conf(X \Rightarrow \overline{Y})} = \frac{P(Y | X)}{P(\overline{Y} | X)} = \frac{supp(X \cup Y)}{supp(X \cup \overline{Y})} = \frac{P(X \cap Y)}{P(X \cap \overline{Y})} \] i Range: \([0, 1]\)

Standardized Lift

Reference: McNicholas, Murphy, and O’Regan (2008)

Standardized lift uses the minimum and maximum lift that can reach for each rule to standardize lift between 0 and 1. The possible range of lift is given by the minimum

\[ \lambda = \frac{\mathrm{max}\{P(X) + P(Y) - 1, 1/n\}}{P(X)P(Y)}. \]

and the maximum

\[ \upsilon = \frac{1}{\mathrm{max}\{P(X), P(Y)\}} \]

The standardized lift is defined as

\[ \mathrm{stdLift}(X \Rightarrow Y) = \frac{\mathrm{lift}(X \Rightarrow Y) - \lambda}{ \upsilon - \lambda}. \]

The standardized lift measure can be corrected for minimum support and minimum confidence used in rule mining by replacing the minimum bound \(\lambda\) with

\[ \lambda^* = \mathrm{max}\left\{\lambda, \frac{4s}{(1+s)^2}, \frac{s}{P(X)P(Y)}, \frac{c}{P(Y)}\right\}. \]

Range: \([0, 1]\)

Varying Rates Liaison

Reference: Bernard and Charron (1996)

Defined as the lift of a rule minus 1 (0 represents independence).

\[ \mathrm{VRL}(X \Rightarrow Y) = lift(X \Rightarrow Y) -1 \]

Range: \([-1, \infty]\) (0 for independence)

Yule’s Q

Reference: Tan, Kumar, and Srivastava (2004)

Yule’s Q, also called Yule coefficient of association is a special case of the Goodman and Kruskal’s gamma. It is defined as \[ Q(X \Rightarrow Y) = \frac{\alpha-1}{\alpha+1} \]

where \(\alpha = OR(X \Rightarrow Y)\) is the odds ratio of the rule.

Range: \([-1, 1]\)

Yule’s Y

Reference: Tan, Kumar, and Srivastava (2004)

Yule’s Y is also know as the coefficient of colligation to measure the association between two binary variables. It is defined as \[ Y(X \Rightarrow Y) = \frac{\sqrt{\alpha}-1}{\sqrt{\alpha}+1} \]

where \(\alpha = OR(X \Rightarrow Y)\) is the odds ratio of the rule.

Range: \([-1, 1]\)


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