Depth-First Traversal over a Mirrored Space for Non-redundant Discriminative Itemsets

An Efficient Hybrid Algorithm for Mining Frequent Closures and Generators

The effective construction of many association rule bases requires the computation of both frequent closed and frequent generator itemsets (FCIs/FGs). However, these two tasks are rarely combined. Most of the existing solutions apply levelwise breadth-first traversal, though depth-first traversal, depending on data characteristics, is often superior. Hence, we address here a hybrid algorithm th...

Depth-First Non-Derivable Itemset Mining

Mining frequent itemsets is one of the main problems in data mining. Much effort went into developing efficient and scalable algorithms for this problem. When the support threshold is set too low, however, or the data is highly correlated, the number of frequent itemsets can become too large, independently of the algorithm used. Therefore, it is often more interesting to mine a reduced collecti...

An Exhaustive Covering Approach to Parameter-Free Mining of Non-redundant Discriminative Itemsets

Efficient Vertical Mining of Frequent Closed Itemsets and Generators

The effective construction of many association rule bases require the computation of both frequent closed and frequent generator itemsets (FCIs/FGs). However, these two tasks are rarely combined. Most of the existing solutions apply levelwise breadth-first traversal, though depth-first traversal is knowingly superior. Hence, we address here the depth-first FCI/FG-mining. The proposed algorithm,...

Efficient Vertical Mining of Minimal Rare Itemsets

Rare itemsets are important sort of patterns that have a wide range of practical applications, in particular, in analysis of biomedical data. Although mining rare patterns poses specific algorithmic problems, it is yet insufficiently studied. In a previous work, we proposed a levelwise approach for rare itemset mining that traverses the search space bottomup and proceeds in two steps: (1) movin...