goal of the experiments was to explore the nature and extent of specious rules and how their pruning affects results. For this purpose we tested classical benchmark data sets with varying dimensions and densities from the UCI Machine Learning Repository [@MLrep] and the Frequent Itemset Mining Dataset Repository [@FIMI]. The test data is described in Table \[testsets\]. All sets are real world data except T10I4D100K and T40I10D100K which are synthetic data sets simulating market basket data.

  Data          Abbr          $n$    $k$   $tlen$
  ------------- -------- -------- ------ --------
  Plants        Plants      22632     70     12.5
  Chess         Chess        3196     75     37.0
  Mushroom      Mush         8124    119     23.0
  Accidents     Acc        340183    468     33.8
  T10I4D100K    T1         100000    870     10.1
  T40I10D100K   T4         100000    942     39.6
  PumsbStar     PStar       49046   1934     50.5
  Pumsb         Pumsb       49046   2113     74.0

  : Description of data sets: Abbr=abbreviation, $n$=number of rows, $k$=number of attributes, $tlen$=average transaction length.[]{data-label="testsets"}

In the experiments, we searched for the top-100 and top-1000 positive or negative dependency rules with mutual information and analyzed the detected specious rules. The search was done with the Kingfisher program [@kingfkais], which was extended with a special module for speciousness testing. The basic version of Kingfisher already prunes out redundant rules (a special case of specious rules) 