Incremental data mining has been discussed widely in recent years, as it has many practical applications, and various incremental mining algorithms have been proposed. Hong et al. proposed an efficient incremental mining algorithm for handling newly inserted transactions by using the concept of pre-large itemsets. The algorithm aimed to reduce the need to rescan the original database and also c...

Association rules mining from transaction-oriented databases is an important issue in data mining. Frequent pattern is crucial for association rules generation, time series analysis, classification, etc. There are two categories of algorithms that had been proposed, candidate set generate-and-test approach (Apriori-like) and Pattern growth approach. Many methods had been proposed to solve the a...

The paper is concerned with distributed learning and optimization in large-scale settings. The wellknown Fictitious Play (FP) algorithm has been shown to achieve Nash equilibrium learning in certain classes of multi-agent games. However, FP can be computationally difficult to implement when the number of players is large. Sampled FP is a variant of FP that mitigates the computational difficulti...

We describe a frequent itemset mining algorithm and implementation based on the well-known algorithm FPgrowth. The theoretical difference is the main data structure (tree), which is more compact and which we do not need to rebuild for each conditional step. We thoroughly deal with implementation issues, data structures, memory layout, I/O and library functions we use to achieve comparable perfo...

ABSRACT Mining Association Rules from huge databases is one of the important issue that need to be addressed. This paper presents a new sampling based association rule mining algorithm that uses a progressive sampling approach based on negative border and Frequent pattern growth (FP Growth) algorithm for finding the candidate item sets which ultimately shortens the execution time in generating ...

The pattern growth approach of association rule mining is very efficient as avoiding the candidate generation step which was utilized in Apriori algorithm. Here, revisited of the pattern growth approaches are done to improve the performance using different criteria like item search order, conditional database representation and construction approach and tree traversal ways. The header table con...

Traditional data mining approaches look for patterns in a single table, while multirelational data mining aims for identifying patterns that involve multiple tables. In recent years, the most common mining techniques have been extended to the multirelational context, but there are few dedicated to deal with data stored following the multi-dimensional model, in particular the star schema. These ...