Evolving temporal association rules with genetic algorithms

A novel framework for mining temporal association rules by discovering itemsets with a genetic algorithm is introduced. Metaheuristics have been applied to association rule mining, we show the efficacy of extending this to another variant - temporal association rule mining. Our framework is an enhancement to existing temporal association rule mining methods as it employs a genetic algorithm to simultaneously search the rule space and temporal space. A methodology for validating the ability of the proposed framework isolates target temporal itemsets in synthetic datasets. The Iterative Rule Learning method successfully discovers these targets in datasets with varying levels of difficulty

The Rule Extraction of Numerical Association Rule Mining Using Hybrid Evolutionary Algorithm

The topic of Particle Swarm Optimization (PSO) has recently gained popularity. Researchers has used it to solve difficulties related to job scheduling, evaluation of stock markets and association rule mining optimization. However, the PSO method often encounters the problem of getting trapped in the local optimum. Some researchers proposed a solution to over come that problem using combination of PSO and Cauchy distribution because this performance proved to reach the optimal rules. In this paper, we focus to adopt the combination for solving association rule mining (ARM) optimization problem in numerical dataset. Therefore, the aim of this research is to extract the rule of numerical ARM optimization problem for certain multi-objective functions such as support, confidence, and amplitude. This method is called PARCD. It means that PSO for numerical association rule mining problem with Cauchy Distribu- tion. PARCD performed better results than other methods such as MOPAR, MODENAR, GAR, MOGAR and RPSOA

Encapsulation of Soft Computing Approaches within Itemset Mining a A Survey

Data Mining discovers patterns and trends by extracting knowledge from large databases. Soft Computing techniques such as fuzzy logic, neural networks, genetic algorithms, rough sets, etc. aims to reveal the tolerance for imprecision and uncertainty for achieving tractability, robustness and low-cost solutions. Fuzzy Logic and Rough sets are suitable for handling different types of uncertainty. Neural networks provide good learning and generalization. Genetic algorithms provide efficient search algorithms for selecting a model, from mixed media data. Data mining refers to information extraction while soft computing is used for information processing. For effective knowledge discovery from large databases, both Soft Computing and Data Mining can be merged. Association rule mining (ARM) and Itemset mining focus on finding most frequent item sets and corresponding association rules, extracting rare itemsets including temporal and fuzzy concepts in discovered patterns. This survey paper explores the usage of soft computing approaches in itemset utility mining

Penguins Search Optimisation Algorithm for Association Rules Mining

Association Rules Mining (ARM) is one of the most popular and well-known approaches for the decision-making process. All existing ARM algorithms are time consuming and generate a very large number of association rules with high overlapping. To deal with this issue, we propose a new ARM approach based on penguins search optimisation algorithm (Pe-ARM for short). Moreover, an efficient measure is incorporated into the main process to evaluate the amount of overlapping among the generated rules. The proposed approach also ensures a good diversification over the whole solutions space. To demonstrate the effectiveness of the proposed approach, several experiments have been carried out on different datasets and specifically on the biological ones. The results reveal that the proposed approach outperforms the well-known ARM algorithms in both execution time and solution quality

Improved optimization of numerical association rule mining using hybrid particle swarm optimization and cauchy distribution

Particle Swarm Optimization (PSO) has been applied to solve optimization problems in various fields, such as Association Rule Mining (ARM) of numerical problems. However, PSO often becomes trapped in local optima. Consequently, the results do not represent the overall optimum solutions. To address this limitation, this study aims to combine PSO with the Cauchy distribution (PARCD), which is expected to increase the global optimal value of the expanded search space. Furthermore, this study uses multiple objective functions, i.e., support, confidence, comprehensibility, interestingness and amplitude. In addition, the proposed method was evaluated using benchmark datasets, such as the Quake, Basket ball, Body fat, Pollution, and Bolt datasets. Evaluation results were compared to the results obtained by previous studies. The results indicate that the overall values of the objective functions obtained using the proposed PARCD approach are satisfactory

Improving a multi-objective evolutionary algorithm to discover quantitative association rules

This work aims at correcting flaws existing in multi-objective evolutionary schemes to discover quantitative association rules, specifically those based on the wellknown non-dominated sorting genetic algorithm-II (NSGA-II). In particular, a methodology is proposed to find the most suitable configurations based on the set of objectives to optimize and distance measures to rank the non-dominated solutions. First, several quality measures are analyzed to select the best set of them to be optimized. Furthermore, different strate-gies are applied to replace the crowding distance used by NSGA-II to sort the solutions for each Pareto-front since such distance is not suitable for handling many-objective problems. The proposed enhancements have been integrated into the multi-objective algorithm called MOQAR. Several experiments have been carried out to assess the algorithm’s performance by using different configuration settings, and the best ones have been compared to other existing algorithms. The results obtained show a remarkable performance of MOQAR in terms of quality measures.Ministerio de Ciencia y Tecnología TIN2011-28956-C02Ministerio de Ciencia y Tecnología TIN2014- 55894-C2-RJunta de Andalucia P12-TIC-1728Universidad Pablo de Olavide APPB81309

Discovering gene association networks by multi-objective evolutionary quantitative association rules

In the last decade, the interest in microarray technology has exponentially increased due to its ability to monitor the expression of thousands of genes simultaneously. The reconstruction of gene association networks from gene expression profiles is a relevant task and several statistical techniques have been proposed to build them. The problem lies in the process to discover which genes are more relevant and to identify the direct regulatory relationships among them. We developed a multi-objective evolutionary algorithm for mining quantitative association rules to deal with this problem. We applied our methodology named GarNet to a well-known microarray data of yeast cell cycle. The performance analysis of GarNet was organized in three steps similarly to the study performed by Gallo et al. GarNet outperformed the benchmark methods in most cases in terms of quality metrics of the networks, such as accuracy and precision, which were measured using YeastNet database as true network. Furthermore, the results were consistent with previous biological knowledge.Ministerio de Ciencia y Tecnología TIN2011-28956-C02-02Junta de Andalucía P11-TIC-752