and Miettinen et al (2008). However, in the current paper we chose to present an interestingness measure for noise-free tiles, as it is sufficient to **illustrate the**
general framework from Sec. 2 without risking to overload the reader by the technicalities related to noisy tiles.

## 7 Experiments

In this Section we assess the computational cost of fitting the MaxEnt model from Sec. 3 for given expected row and column sums. We also assess **the cost** and empirical properties of sampling random databases from the MaxEnt model, and we empirically compare this to swap randomizations. Additionally, we will show that the MaxEnt model can be fitted efficiently to very large networks as well. Finally, we will assess the compression ratio as a subjective interestingness measure for tiles by using it for three document databases.

All experiments were done on a 2GHz Pentium Centrino with 1GB Memory, and the code used for each of the experiments will be made freely available on www.tijldebie.net/software/maxent.

## 7.1 The Maxent Model For Binary Databases

We report empirical results on ten databases: seven databases commonly used for evaluation purposes (Retail (Brijs et al, 1999), Mushroom, Pumsb, Pumsb star, Connect (Asuncion and Newman, 2007), T10I4D100K, and T40I10D100K (Agrawal and Srikant, 1994)), as well as the following three textual datasets turned into databases by considering words as items and documents as transactions:
ICDM. **All ICDM paper abstracts until 2007. Each abstract is represented by**
a transaction and words are items, after stop word removal and stemming.

KDD. **All KDD paper abstracts between 2001 and 2008 (from all sessions)**
downloaded from the ACM website. Each abstract is represented by a transaction and words are items, after stop word removal and stemming.

Pubmed. **All Pubmed abstracts retrieved by querying with the search query**
"data mining", after stop word removal and stemming.

Some statistics are gathered in Table 2. The Table also mentions support thresholds used in some of the experiments reported below, as well as the numbers of closed itemsets satisfying these support thresholds.

For each of these databases we computed the MaxEnt model with expected row and column sums equal to the observed row and column sums in the data.

Fitting the MaxEnt model **The method we used to fit the model is a preconditioned gradient descent method with Jacobi preconditioner (e.g. Shewchuk,**
1994), implemented in C++. It is conceivable that more sophisticated **methods**
will lead to significant further speedups (e.g. methods discussed in Boyd and Vandeberghe