In this paper we introduce a new parallel algorithm MLFPT (Multiple Local Frequent Pattern Tree) [11] for parallel mining of frequent patterns, based on FP-growth mining, that uses only two full I/O scans of the database, eliminating the need for generating the candidate items, and distributing the work fairly among processors. We have devised partitioning strategies at different stages of the ...

In this paper I introduce SaM, a split and merge algorithm for frequent item set mining. Its core advantages are its extremely simple data structure and processing scheme, which not only make it quite easy to implement, but also very convenient to execute on external storage, thus rendering it a highly useful method if the transaction database to mine cannot be loaded into main memory. Furtherm...

This article examines dynamical systems on a class of K3 surfaces in P×P with an infinite automorphism group. In particular, this article develops an algorithm to find Q-rational periodic points using information over Fp for various primes p. This algorithm is then optimized to examine the growth of the average number of cycles versus p and to determine the number of Fpm -rational points. The p...

In this paper a fuzzy logic (FL) based load frequency controller (LFC) called discrete FuzzyP+FuzzyI+FuzzyD (FP+FI+FD) is proposed to ensure the stability of a multi-source power system in restructured environment. The whale optimization algorithm (WOA) is used for optimum designing the proposed control strategy to reduce fuzzy system effort and achieve the best performance of LFC task. Further...

Correlated patterns are an important class of regularities that exist in a transactional database. CoMine uses pattern-growth technique to discover the complete set of correlated patterns that satisfy the user-defined minimum support and minimum all-confidence constraints. The technique involves compacting the database into FP-tree, and mining it recursively by building conditional pattern base...

Mining frequent patterns is to discover the groups of items appearing always together excess of a user specified threshold. Many approaches have been proposed for mining frequent patterns by applying the FP-tree structure to improve the efficiency of the FP-Growth algorithm which needs to recursively construct sub-trees. Although these approaches do not need to recursively construct many sub-tr...

Exceptional model mining has been proposed as a variant of subgroup discovery especially focusing on complex target concepts. Currently, efficient mining algorithms are limited to heuristic (non exhaustive) methods. In this paper, we propose a novel approach for fast exhaustive exceptional model mining: We introduce the concept of valuation bases as an intermediate condensed data representation...

Multi-core processors are proliferated across different domains in recent years. In this paper, we study the performance of frequent pattern mining on a modern multi-core machine. A detailed study shows that, even with the best implementation, current FP-tree based algorithms still under-utilize a multi-core system due to poor data locality and insufficient parallelism expression. We propose tw...

Let E/Fp be an elliptic curve, and G ∈ E/Fp. Define the Diffie–Hellman function as DHE,G(aG, bG) = abG. We show that if there is an efficient algorithm for predicting the LSB of the x or y coordinate of abG given 〈E, G, aG, bG〉 for a certain family of elliptic curves, then there is an algorithm for computing the Diffie–Hellman function on all curves in this family. This seems stronger than the ...