Extending inverse frequent itemsets mining to generate realistic datasets: complexity, accuracy and emerging applications

The development of novel platforms and techniques for emerging “Big Data” applications requires the availability of real-life datasets for data-driven experiments, which are however not accessible in most cases for various reasons, e.g., confidentiality, privacy or simply insufficient availability. An interesting solution to ensure high quality experimental findings is to synthesize datasets that reflect patterns of real ones using a two-step approach: first a real dataset X is analyzed to derive relevant patterns Z (latent variables) and, then, such patterns are used to reconstruct a new dataset \(X'\) that is like X but not exactly the same. The approach can be implemented using inverse mining techniques such as inverse frequent itemset mining (\(\texttt {IFM}\)), which consists of generating a transactional dataset satisfying given support constraints on the itemsets of an input set, that are typically the frequent ones. This paper introduces various extensions of \(\texttt {IFM}\) within a uniform framework with the aim to generate artificial datasets that reflect more elaborated patterns (in particular infrequency and duplicate constraints) of real ones. Furthermore, in order to further enlarge the application domain of \(\texttt {IFM}\), an additional extension is introduced that considers more structured schemes for the datasets to be generated, as required in emerging big data applications, e.g., social network analytics.

The funding was supported by MISE, Italian Ministry for Industry (Grant No. PON ID Service and Protect ID).

Authors and Affiliations

1. DIMES Department, University of Calabria, Rende, Italy

   Domenico Saccá & Antonino Rullo

2. CS Department, Boise State University, Boise, ID, USA

   Edoardo Serra

Corresponding author

Correspondence to Domenico Saccá.

Responsible editor: Toon Calders.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix: Proofs

Theorem 1

(1) Decision \({\texttt {IFM}} _{\texttt {G} }\) and \({\texttt {IFM}} _{\texttt {I} }\) are \({\texttt {NEXP}} \)-complete; (2) decision \({\texttt {IFM}} _{\texttt {D} }\) is in \({\texttt {PSPACE}} \) and \({\texttt {PP}} \)-hard; (3) decision \({\texttt {IFM}} \) is in \({\texttt {PSPACE}} \) and \({\texttt {NP}} \)-hard; (4) decision \({\texttt {IFM}} _{\texttt {S} }\) is \({\texttt {NP}} \)-complete.

Proof

The complexity results of \(\texttt {IFM}_\texttt {I}\) and \(\texttt {IFM}\) have been proved respectively in Guzzo et al. (2013); Saccà and Serra (2013) and in Mielikainen (2003).

Let us now prove the \({\texttt {PSPACE}}\)-membership of \(\texttt {IFM}_\texttt {D}\). A polynomial space nondeterministic algorithm can set at the beginning \(size=0\) and \(\forall I \in S\), \(\sigma _I=0\). Then, for each possible transaction \(I \in {\mathcal {U}}_{\mathcal {I}}\), it guesses a duplicate value \(\delta _I\) such that \(\delta _I \le {\texttt {size}}\) if there exists \(J\in S\) such that \(J \subseteq I\) or \(\delta _J \le \delta '\) otherwise (i.e., \(J \in S'\)). After each guessing, the algorithm updates \({\texttt {size}}\) and \(\sigma _J\) for each \(J \in S: \, J \subseteq I\). Once all guesses are completed, it remains to verify whether \(size = {\texttt {size}}\) and for each \(I \in S: \, I \subseteq J\), \(\sigma _{\min }^I \le \sigma _I \le \sigma _{\max }^I\). It follows that the problem is in \({\texttt {PSPACE}}\).

The \({\texttt {PP}}\)-hardness of \(\texttt {IFM}_\texttt {D}\) can be easily proved by reduction from \({\texttt {FREQSAT}}\)\(\small {\{\texttt {NDUP}, \texttt {NTRANS}\}}\) that has been shown to be \({\texttt {PP}}\)-hard in Calders (2007) (Theorem 13). The \({\texttt {NEXP}}\)-hardness of \(\texttt {IFM}_\texttt {G}\) immediately derives from the \({\texttt {NEXP}}\)-hardness of \(\texttt {IFM}_\texttt {I}\), which is a sub-problem of \(\texttt {IFM}_\texttt {G}\).

Membership of \(\texttt {IFM}_\texttt {G}\) to \({\texttt {NEXP}}\) immediately follows from the fact that, given any instance x of \(\texttt {IFM}_\texttt {G}\), x is a yes-instance if and only if both \(x_I\) (obtained from x by removing the constraint 3) is a yes-instance of \(\texttt {IFM}_\texttt {I}\) and \(x_D\) (obtained from x by removing the constraint 2) is a yes-instance of \(\texttt {IFM}_\texttt {D}\).

We conclude by proving the \({\texttt {NP}}\)-completeness of \(\texttt {IFM}_S\) – this result was first presented without a proof in Guzzo et al. (2009). As for the membership to \({\texttt {NP}}\), we observe that the size of the output database \({\mathcal {D}}\) is certainly bounded by |S| and by the largest support required in the specification. Hence, in polynomial time a non-deterministic Turing machine may first guess such database (basically, the support for each itemset I in S) and then verify whether it satisfies the constraints \(\sigma _{min}^i\) and \(\sigma _{max}^i\), by simply computing the support \(\sigma ^{{\mathcal {D}}}(I_i)\) on \({\mathcal {D}}\).

To prove that \(\texttt {IFM}_{S}\) is \({\texttt {NP}}\)-hard, we exhibit a reduction from the graph 3-colorability problem of deciding whether, given a graph \(G=(V,E)\), there is a 3-coloring \(c:V\rightarrow \{r,g,b\}\) such that \(c(i)\ne c(j)\) for each pair of edges \((i,j)\in E\).

Based on the input graph \(G=(V,E)\), we construct an instance of the \(\texttt {IFM}_{S}({\mathcal {I}},\varSigma _S)\) problem such that: the set \({\mathcal {I}}\) of items is \(\{r,g,b,l_1,l_2,l_3\}\cup \{v_{x} | x \in V \}\cup \{e_{z,y}| (z,y) \in E\}\), where conceptually the item r, g, b are the colors in G, \(l_1, l_2, l_3\) are labels implementing an encoding of the three colors, \(v_{x}\) is an item for each node in G and \(e_{z,y}\) is an item for each edge in G. The encoding of colors by label is such that any two colors share exactly one label; in the proof we shall use the encoding \(r=\{l_1, l_2\}\), \(g=\{l_1, l_3\}\) and \(b=\{l_2, l_3\}\).

The set \(\varSigma _S\) contains two groups of constraints:

Group (I) these constraints are repeated for each node \(x \in V\) and enforces that x must be colored with exactly one color. There are 7 itemsets associated to x organized on 3 levels:

• 3 itemsets at the highest level 2: there is an itemset for each possible color c for x, containing the items corresponding to the node x, to all the arcs leaving x, to the color c and to the encoding of the color — the support for such itemsets can be either 0 or 1;

• 3 itemsets at level 1: there is an itemset for each of the 3 encoding labels, containing the item corresponding to the label and the item corresponding to the node x – the support must be exactly 1;

• 1 itemset at level 0 containg the item corresponding to the node x – its support must be exactly 2;

We explicit the constraints below:

• \((I_{x,r},0,1)\), \((I_{x,g},0,1)\), \((b_{x,r},0,1)\), where \(I_{x,r}=\)\(\{v_x, r,l_1, l_2\} \cup I\),

  \(I_{x,g}=\)\(\{v_x, g,l_1, l_3\}\)\(\cup I\), \(I_{x,b}=\)\(\{v_x, b,l_2, l_3\} \cup I\), and \(I = \{e_{x,y}|(x,y)\in E\}\);

• \((\{l_1,v_{x}\},1,1)\), \((\{l_2,v_{x}\},1,1)\) and \((\{l_3,v_{x}\},1,1)\);

• \((\{v_{x}\},2,2)\).

Because of the support constraints for the itemsets at level 1, \(\{l_1,v_{x}\}\) cannot occur as transaction in D as it inherits support from those itemsets; in addition, as the support of \(\{l_1,v_{x}\}\) is 2 and there are 3 itemsets at level 1 with obligatory support 1, exactly one of the itemsets at level 1 must occur as transaction, whereas the other two inherit support from a same itemset at level 2. It turns out that exactly one itemset at level 2 can occur as transaction whereas all others must have support 0 - the itemset selected as transaction will then fix the unique color for the node x.

Group (II) these constraints are repeated for each edge \((x,y) \in E\) and enforce that two end nodes of the edge have different color. There are 3 itemsets, one for each possible color; the constraints are: \((\{r,e_{z,y},0,1)\}\), \((\{g,e_{z,y},0,1)\}\), \((\{b,e_{z,y},0,1)\}\). These itemsets inherit support from two itemsets at level 2: one for x and the other for y. The constraints of Group (II) enforces that the two itemsets at level 2 cannot be of the same color, thus any two adjacent nodes cannot share the same color. It follows that the existence of a solution to \(\texttt {IFM}_{S}\) witnesses the fact that the graph G admits a 3-coloring; on the other hand, if \(\texttt {IFM}_{S}\) has no solution then the graph cannot be 3-colored. Hence, \(\texttt {IFM}_{S}\) is \({\texttt {NP}}\)-hard in general. \(\square \)

Proposition 1

Decision k-\({\texttt {IFM}} _{\texttt {G} }\) is in \({\texttt {PSPACE}} \) and \({\texttt {PP}} \)-hard and decision k-\({\texttt {IFM}} _{\texttt {I} }\) is in \({\texttt {PSPACE}} \) and \({\texttt {NP}} \)-hard.

Proof

The complexity results for k-\(\texttt {IFM}_\texttt {I}\) have been proved in (Guzzo et al. 2013; Saccà and Serra 2013). The \({\texttt {PP}}\)-hardness of k-\(\texttt {IFM}_\texttt {G}\) immediately derives from the \({\texttt {PP}}\)-hardness of \(\texttt {IFM}_\texttt {D}\), which is a sub-problem of k-\(\texttt {IFM}_\texttt {G}\). Finally \({\texttt {PSPACE}}\) membership of k-\(\texttt {IFM}_\texttt {G}\) follows from the fact that, given any instance x of k-\(\texttt {IFM}_\texttt {G}\), x is a yes-instance if and only if both \(x_I\) is a yes-instance of k-\(\texttt {IFM}_\texttt {I}\) and \(x_D\) is a yes-instance of \(\texttt {IFM}_\texttt {D}\).

Proposition 2

(1) Relaxed k-\({\texttt {IFM}} _{\texttt {I} }\) and relaxed \({\texttt {IFM}} \) are \({\texttt {NP}} \)-complete; (2) relaxed k-\({\texttt {IFM}} _{\texttt {G} }\) and relaxed \({\texttt {IFM}} _{\texttt {D} }\) are in \({\texttt {PSPACE}} \) and \({\texttt {PP}} \)-hard; (3) relaxed \({\texttt {IFM}} _{{S} }\) is in \({\texttt {P}}\).

Proof

The \({\texttt {NP}}\)-completeness of relaxed k-\(\texttt {IFM}_\texttt {I}\) has been proved in Guzzo et al. (2013); Saccà and Serra (2013). The \({\texttt {NP}}\)-membership of \(\texttt {IFM}\) derives from the fact that \(\texttt {IFM}\) is a sub-problem of k-\(\texttt {IFM}_\texttt {I}\); the \({\texttt {NP}}\)-hardness can be easily proved by reduction from \({\texttt {FREQSAT}}\) that has been shown to be \({\texttt {NP}}\)-complete in Calders (2004, 2007). The \({\texttt {PSPACE}}\)-memberships of relaxed k-\(\texttt {IFM}_\texttt {G}\) and relaxed \(\texttt {IFM}_\texttt {D}\) are obvious. The \({\texttt {PP}}\)-hardness can be easily proved by reduction from FREQSAT\( \small {\{\texttt {NDUP}\}}\) that has been shown to be \({\texttt {PP}}\)-hard in Calders (2007). It follows that k-\(\texttt {IFM}_\texttt {G}\) is \({\texttt {PP}}\)-hard as well. Finally, to prove that relaxed \(\texttt {IFM}_S\) is in \({\texttt {P}}\), we encode the problem as a system of linear equations over |S| rational variables \(\delta ^I\), \(\forall I \in S\), and \(2\, |S|\) inequalities implementing the minimal and maximum support constraint for each itemset in S. The result then follows, since it is well-known that deciding whether a system of linear equations admits a solution is feasible in polynomial time.

Proposition 3

Each column \(j \in U\) is an infeasible solution for the ILP price formulation.

Proof

By constraints (14), a generic column \(j\in U\) represented by the itemset \(I_j\) can be uniquely identified by the itemsets in \(S\cup B_{S'}\) that are included in \(I_j\). Then, in order to prove that the itemset \(I^*\) returned by PLI formulation is different from each other itemset representing a column j in U, the following condition must hold: either the itemset in \(S\cup B_{S'}\) contained in \(I^*\) is a subset of some itemset in \(S\cup B_{S'}\) contained in \(I_j\) or there exists an itemset in \(S\cup B_{S'}\) that is contained in \(I^*\) but not in \(I_j\). The previous condition can be formulate by the following disjunction of integer linear inequalities:

or

Now we prove that the above disjunction is equivalent to the inequality (15).

Suppose that the integer linear inequality (32) is not satisfied. Then

and the integer linear inequality (15) becomes

that is equivalent to integer linear inequality (33). Instead if the integer linear inequality (33) is unsatisfied then

and the integer linear inequality (15) is equivalent to (32). It follows that each column \(j \in U\) is an infeasible solution for the PLI price formulation.

Proposition 4

The decision version of ms-\({\texttt {IFM}} \) is in \({\texttt {PSPACE}} \) and \({\texttt {NP}} \)-hard and the decision version of relaxed ms-\({\texttt {IFM}} \) is \({\texttt {NP}} \)-complete.

Proof

The NP-hardness of decision \({ms\texttt {-}{} \texttt {IFM}}\) derives from the fact that an instance of it without SV attributes and infrequency constraints and with exactly one MV attribute is an instance of the classical \(\texttt {IFM}\) decision problem, that is known to be NP-hard (see Mielikainen 2003). To prove membership to \({\texttt {PSPACE}}\), we use the following non-deterministic algorithm to solve any instance of the problem. We use \(m+{\widehat{m}}+1\) variables \(\ddot{s}\), \(\ddot{b}^f_1, \dots , \ddot{b}^f_m\), \(\ddot{b}^i_1, \dots , \ddot{b}^i_m\) and set all of them to 0. Then we perform the following steps. For each possible transaction t: (1) guess a value \(n_t\) between 0 and \({\texttt {size}}\), (2) if \(n_t>0\) then: (3) set \(\ddot{s} = \ddot{s}+n_t\), (4) for all \(I_i \in S\) s.t. \(I_i\) subsumes t then set \(\ddot{b}^f_i\) := \(\ddot{b}^f_i+n_t\) and (5) for all \(I_i \in S'\) s.t. \(I_i\) subsumes t then set \(\ddot{b}^i_i\) := \(\ddot{b}^i_i+n_t\). At the end of all iterations we check that: (1) \(\ddot{s} = {\texttt {size}}\), (2) for each \(I_i \in S\), \(\ddot{b}^f_i\) is included into the support constraint range for the frequent itemset \(I_i\) and (3) for each \(I_i \in S'\), \(\ddot{b}^i_i\) is less than or equal to the support constraint upper bound for the infrequent itemset \(I_i\). It is easy to see that the algorithm is correct and runs in a nondeterministic polynomial space. So the problem is in \(\mathrm {NPSPACE}\); hence, by Savitch’s theorem, it is in \({\texttt {PSPACE}}\) as well.

The NP-hardness of relaxed \({ms\texttt {-}{} \texttt {IFM}}\) derives from the fact that an instance of it without SV attributes and infrequency constraints and with exactly one MV attribute is an instance of decision \({\texttt {FREQSAT}}\), that is known to be NP-hard (see Calders 2004, 2007). To prove membership to \({\texttt {NP}}\), observe that the problem can be formulated as linear program LP with \(m+{\widehat{m}}+1\) constraints. It is well known that LP has a solution if and only if there exists a basic feasible solution x (i.e., with at most \(m+{\widehat{m}}+1\) values in x different from 0). Therefore we can guess a \((m+{\widehat{m}}+1)\)-vector S of variable indices and non-deterministically assign values \(v \le {\texttt {size}}\) to them. At this point we are able to verify that this basic solution is admissible (i.e. satisfy the linear constraints).

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