Fast Vertical Mining Using Boolean Algebra

Fast Vertical Mining Using Boolean Algebra

The vertical association rules mining algorithm is an efficient mining method, which makes use of support sets of frequent itemsets to calculate the support of candidate itemsets. It overcomes the disadvantage of scanning database many times like Apriori algorithm. In vertical mining, frequent itemsets can be represented as a set of bit vectors in memory, which enables for fast computation. The...

Fast Vertical Mining of Sequential Patterns Using Co-occurrence Information

Sequential pattern mining algorithms using a vertical representation are the most efficient for mining sequential patterns in dense or long sequences, and have excellent overall performance. The vertical representation allows generating patterns and calculating their supports without performing costly database scans. However, a crucial performance bottleneck of vertical algorithms is that they ...

Ternary Boolean Algebra

Visual Data Mining Using Monotone Boolean Functions

This paper describes a new technique for extracting patterns and relations visually from multidimensional binary data using monotone Boolean functions. Visual Data Mining (VDM) has shown benefits in many areas when used with numerical data, but that technique is less beneficial for binary data. This problem is especially challenging in medical applications tracked with binary symptoms. The prop...

Boolean Algebra Approximations

Knight and Stob proved that every low4 Boolean algebra is 0-isomorphic to a computable one. Furthermore, for n = 1, 2, 3, 4, every lown Boolean algebra is 0-isomorphic to a computable one. We show that this is not true for n = 5: there is a low5 Boolean algebra that is not 0-isomorphic to any computable Boolean algebra. It is worth remarking that, because of the machinery developed, the proof u...