MR-Radix: a multi-relational data mining algorithm

Abstract

Background: Once multi-relational approach has emerged as an alternative for analyzing structured data such as relational databases, since they allow applying data mining in multiple tables directly, thus avoiding expensive joining operations and semantic losses, this work proposes an algorithm with multi-relational approach. Methods: Aiming to compare traditional approach performance and multi-relational for mining association rules, this paper discusses an empirical study between PatriciaMine-an traditional algorithm-and its corresponding multi-relational proposed, MR-Radix. Results: This work showed advantages of the multi-relational approach in performance over several tables, which avoids the high cost for joining operations from multiple tables and semantic losses. The performance provided by the algorithm MR-Radix shows faster than PatriciaMine, despite handling complex multirelational patterns. The utilized memory indicates a more conservative growth curve for MR-Radix than PatriciaMine, which shows the increase in demand of frequent items in MR-Radix does not result in a significant growth of utilized memory like in PatriciaMine. Conclusion: The comparative study between PatriciaMine and MR-Radix confirmed efficacy of the multi-relational approach in data mining process both in terms of execution time and in relation to memory usage. Besides that, the multi-relational proposed algorithm, unlike other algorithms of this approach, is efficient for use in large relational databases.

A Game Theoretical Approach for Solving Winner Determination Problems

“…In the future, we will first study the data mining approaches, such as the mining association rules [20] and feature extraction [21], to explore the relationship between the auction items and the bidding behaviors. Then, we will evaluate the instances that are generated via simulating the real-world bidding behaviors.…”

A User-Driven Association Rule Mining Based on Templates for Multi-Relational Data

“…The proposed algorithm called TBMR-Radix is an improvement of the MR-Radix algorithm proposed by Valêncio et al (2012).…”

“…The described algorithm is based on the MR-Radix algorithm, proposed by Valêncio et al (2012). The MR-Radix has good results in terms of processing time and memory consumption when compared with other wellknown solutions found on the literature, such as FP-Growth, FP-Growth*, Apriori, OportuneProject, CT-Pro e FP-Growth-Tiny, which justify their choice as based algorithm for de development of this work.…”

“…Among the different methods available to fulfill this task, it is possible to highlight the algorithms that perform the extraction of association rules (Han et al, 2011), such as the traditional Apriori algorithm (Agrawal et al, 1993;Liu, 2010), FP-Growth (Wu et al, 2007;Zhang et al, 2008), PatriciaMine (Pietracaprina and Zandolin, 2003) and MR-Radix (Valêncio et al, 2012).…”

Proposed efficient approach for classification for multi-relational data mining using Bayesian Belief Network

“…Valencio et.al proposed the MR-Radix algorithm. These MRDM algorithm has a data structure called the Radix tree [7] and solve the problem of scalability. At the time of comparison, MR-Radix provides better efficacy then traditional data mining algorithm.…”