Association rule mining through adaptive parameter control in particle swarm optimization

Genetic Algorithm (GA) and Particle swarm optimization (PSO) are both population based search methods and move from set of points (population) to another set of points in a single iteration with likely improvement using set of control operators. GA has become popular because of its many versions, ease of implementation, ability to solve difficult problems and so on. PSO is relatively recent heuristic search mechanism inspired by bird flocking or fish schooling. Association Rule (AR) mining is one of the most studied tasks in data mining. The objective of this paper is to compare the effectiveness and computational capability of GA and PSO in mining association rules. Though both are heuristic based search methods, the control parameters involved in GA and PSO differ. The Genetic algorithm parameters are based on reproduction techniques evolved from biology and the control parameters of PSO are based on particle ‘best’ values in each generation. From the experimental study PSO is found to be as effective as GA with marginally better computational efficiency over GA.KeywordsGenetic AlgorithmParticle Swam optimizationAssociation rulesEffectivenessComputational efficiency

The Gene Ontology (GO) has become the internationally accepted standard for representing function, process, and location aspects of gene products. The wealth of GO annotation data provides a valuable source of implicit knowledge of relationships among these aspects. We describe a new method for association rule mining to discover implicit co-occurrence relationships across the GO sub-ontologies at multiple levels of abstraction. Prior work on association rule mining in the GO has concentrated on mining knowledge at a single level of abstraction and/or between terms from the same sub-ontology. We have developed a bottom-up generalization procedure called Cross-Ontology Data Mining-Level by Level (COLL) that takes into account the structure and semantics of the GO, generates generalized transactions from annotation data and mines interesting multi-level cross-ontology association rules. We applied our method on publicly available chicken and mouse GO annotation datasets and mined 5368 and 3959 multi-level cross ontology rules from the two datasets respectively. We show that our approach discovers more and higher quality association rules from the GO as evaluated by biologists in comparison to previously published methods. Biologically interesting rules discovered by our method reveal unknown and surprising knowledge about co-occurring GO terms.

An animal dynamic migration optimization method for directional association rule mining

Modeling microbiome systems as complex networks are known as the problem of network inference. Microbial association network inference is of great significance in applications on clinical diagnosis, disease treatment, pathological analysis, etc. However, most current network inference methods focus on mining strong pairwise associations between microorganisms, which is defective in reflecting the comprehensive interactive patterns participated by multiple microorganisms. It is also possible that the microorganisms involved in the generated network are not dominant in the microbiome due to the mere focus on the strength of pairwise associations. Some scholars tried to mine comprehensive microbial associations by association rule mining methods, but the adopted algorithms are relatively basic and have severe limitations such as low calculation efficiency, lacking the ability of mining negative correlations and high redundancy in results, making it difficult to mine high-quality microbial association rules and accurately infer microbial association networks. We proposed a microbial association network inference method 'MANIEA' based on the improved Eclat algorithm for mining positive and negative microbial association rules. We also proposed a new method for transforming association rules into microbial association networks, which can effectively demonstrate the co-occurrence and causal correlations in association rules. An experiment was conducted on three authentic microbial abundance datasets to compare the 'MANIEA' with currently popular network inference methods, which demonstrated that the proposed 'MANIEA' show advantages in aspects of correlation forms, computation efficiency, adjustability and network characteristics. The algorithms and data are available at: https://github.com/MaidiL/MANIEA.

Association rules mining methods have been recently applied to gene expression data analysis to reveal relationships between genes and different conditions and features. However, not much effort has focused on detecting the relation between gene expression maps and related gene functions. Here we describe such an approach to mine association rules among gene functions in clusters of similar gene expression maps on mouse brain. The experimental results show that the detected association rules make sense biologically. By inspecting the obtained clusters and the genes having the gene functions of frequent itemsets, interesting clues were discovered that provide valuable insight to biological scientists. Moreover, discovered association rules can be potentially used to predict gene functions based on similarity of gene expression maps.

Dunia Vape Trading Business is a trading business engaged in the sale of electronic cigarette spare parts. Every consumer who is going to buy a product, the consumer looks at the product shelves one by one to find the product they are looking for. The problem that occurs is that sometimes it is tiring for consumers to search on different shelves or it can also cause consumers not to buy the products they actually need. This is because the products sold are not arranged based on the purchasing patterns that consumers often make. Therefore, a method is needed that can make it easier for consumers to find the desired product and so that UD Dunia Vape can find out consumer purchasing patterns so that it can arrange and provide products according to the usual patterns. Therefore, researchers utilize computer technology to help consumers find the desired product and so that UD Dunia Vape can find out consumer purchasing patterns so that it can arrange and provide products according to the usual patterns. In computer science, one of the methods that can be applied is by using data mining. One of the methods used in determining patterns is the Association Rule Mining Method. Association rule mining is a data mining technique that aims to find association rule patterns between a combination of items. Implementation of the Association Rule Mining Method on sales of goods at the UD Dunia Vape Store is used to provide information on the results of building material sales patterns each month which can be used as parameters in managing UD Dunia Vape's inventory of materials, in addition to providing information on the most popular and most sold building materials at UD Dunia Vape. The results obtained Based on the results and discussion, it can be concluded that the analysis of consumer purchasing patterns using the Association Rule method on Dunia Vape sales transaction data can be applied based on the results of manual method application where the association rule results are the same as data mining application testing, namely if you buy I = LOST VAPE URSA CAP POD KIT JOY PURPLE then Buy N = PODS FRIENDLY ICE PEDIA BLUEBERRY 15MG 30ML (24) with a confidence value of 100% and if you buy N = PODS FRIENDLY ICE PEDIA BLUEBERRY 15MG 30ML (24) then Buy I = LOST VAPE URSA CAP POD KIT JOY PURPLE with a confidence value of 100%.

A method for mining association rules that reflect the behaviors of past users is proposed for an adaptive search engine. The logs of the users' retrieving behaviors are described with the resource description framework model, from which association rules that reflect successful retrieving behaviors are extracted. The extracted rules are used to improve the performance of a metadata-based search engine. The document repository with adaptive hybrid search engine is also developed based on the proposed method. The repository consists of a document registration module, hybrid search engine, and reasoning base. The document registration module is designed to reduce the cost of adding metadata to documents, and the hybrid search engine combines full-text search with metadata-based search engine to improve the recall of retrieval result. The reasoning base is implemented based on the association rule mining method, which contributes to improve both precision and recall of the hybrid search engine. Experiments are performed with a virtual user model, of which results show that appropriate rules can be extracted with the proposed method. The proposed technologies will contribute to realize the concept of humatronics in terms of establishing symmetric relation between humans and systems, as well as sharing information, knowledge, and experiences via computer networks

Online stores assist customers in buying the desired products online. Great competition in the e-commerce sector necessitates technology development. Many e-commerce systems not only present products but also offer similar products to increase online customer interest. Due to high product variety, analyzing products sold together similar to a recommendation system is a must. This study methodologically improves the traditional association rule mining (ARM) method by adding fuzzy set theory. Besides, it extends the ARM by considering not only items sold but also sales amounts. Fuzzy association rule mining (FARM) with the Apriori algorithm can catch the customers’ choice from historical transaction data. It discovers fuzzy association rules from an e-commerce company to display similar products to customers according to their needs in amount. The experimental result shows that the proposed FARM approach produces much information about e-commerce sales for decision-makers. Furthermore, the FARM method eliminates some traditional rules considering their sales amount and can produce some rules different from ARM.

Artificial intelligence methods such as fuzzy logic and particle swarm optimization (PSO) have been applied to maximum power point tracking (MPPT) for solar panels. The P-V curve of a solar panel exhibits multiple peaks under partial shading condition (PSC) when all modules of a solar panel do not receive the same solar irradiation. Although conventional PSO has been shown to perform well under uniform insolation, it is often unable to find the global maximum power point under PSC. Fuzzy adaptive PSO controllers have been proposed for MPPT. However, the controller became computation-intensive in order to adjust the PSO parameters for each particle. In this paper, fuzzy adaptive PSO-based and conventional PSO-based MPPT are compared and evaluated in the aspect of design and performance. A simple fuzzy adaptive PSO controller for MPPT was designed to reach the global optimal point under PSC and uniform irradiation. The controller combines the advantages of both PSO and fuzzy control. The fuzzy controller dynamically adjusts the PSO parameter to improve the convergence speed and global search capability. Since tuning of the PSO parameter is designed to be common for all particles, it reduced the computation complexity. The fuzzy controller’s rule base is designed to obtain a fast transient response and stable steady state response. Design of the fuzzy adaptive PSO-based MPPT is verified with simulation results using a boost converter. The results are evaluated in comparison to the results using a conventional PSO controller under PSC. Simulation shows the fuzzy adaptive PSO-based MPPT is able to improve the global search process and increase the convergency speed. The comparison indicates the settling time using the fuzzy adaptive PSO-based MPPT is 14% faster under PSC on average and 30% faster under uniform irradiation than the settling time using the conventional PSO. Both the fuzzy adaptive and conventional PSO controllers have similar output power tracking accuracy.

The proposed research deals with selection of particle swarm optimization (PSO) algorithm parameters for missile gliding trajectory optimization relying on Taguchi design of experiments, analysis of variance (ANOVA) and artificial neural networks (ANN). Population size, inertial weight and acceleration coefficients of PSO were chosen for the present study. The experiments have been designed as per Taguchi's design of experiments using L25 orthogonal array for selection of better PSO parameters. Missile gliding trajectory is optimized by discretizing angle of attack as control parameter, consequent conversion of optimal control problem to nonlinear programming problem (NLP) and finally solving the problem using PSO with optimized parameters to obtain optimum angle of attack and realization of maximum gliding range. Simulation results portrayed that the gliding range is maximized and missile glide distance is enhanced compared to earlier experiments. The efficiency of proposed approach was verified via different test scenarios.

Particle swarm optimization (PSO) can be interpreted physically as a particular discretization of a stochastic damped mass-spring system. Knowledge of this analogy has been crucial to derive the PSO continuous model and to introduce different PSO family members including the generalized PSO (GPSO) algorithm, which is the generalization of PSO for any time discretization step. In this paper, we present the stochastic analysis of the linear continuous and generalized PSO models for the case of a stochastic center of attraction. Analysis of the GPSO second order trajectories is performed and clarifies the roles of the PSO parameters and that of the cost function through the algorithm execution: while the PSO parameters mainly control the eigenvalues of the dynamical systems involved, the mean trajectory of the center of attraction and its covariance functions with the trajectories and their derivatives (or the trajectories in the near past) act as forcing terms to update first and second order trajectories. The similarity between the oscillation center dynamics observed for different kinds of benchmark functions might explain the PSO success for a broad range of optimization problems. Finally, a comparison between real simulations and the linear continuous PSO and GPSO models is shown. As expected, the GPSO tends to the continuous PSO when time step approaches zero. Both models account fairly well for the dynamics (first and second order moments) observed in real runs. This analysis constitutes so far the most realistic attempt to better understand and approach the real PSO dynamics from a stochastic point of view.

Computational approach to predict peptide binding to major histocompatibility complex (MHC) is crucial for vaccine design since these peptides can act as a T-Cell epitope to trigger immune response. There are two main branches for peptide prediction methods; structural and data mining approaches. These methods can be successfully used for prediction of T-Cell epitopes in cancer, allergy and infectious diseases. In this paper, association rule mining methods are implemented to generate rules of peptide selection by MHCs. To capture the binding characteristics, modified rule mining and data transformation methods are implemented in this paper. Peptides are known to bind to the same MHC show sequence variability, to capture this characteristic, we used a reduced amino acid alphabet by clustering amino acids according to their physico-chemical properties. Using the classification of amino acids and the OR-operator to combine the rules to reflect that different amino acid types and positions along the peptide may be responsible for binding are the innovations of the method presented. We can predict MHC Class-I binding with 75-97% coverage and 76-100% accuracy.

Today, people benefit from utilizing data mining technologies, such as association rule mining methods, to find valuable knowledge residing in a large amount of data. However, they also face the risk of exposing sensitive or confidential information, when data is shared among different organizations. Thus, a question arise: how can we prevent that sensitive knowledge is discovered, while ensuring that ordinary non-sensitive knowledge can be mined to the maximum extent possible. In this paper, we address the problem of privacy preserving in association rule mining from the perspective of multi-objective optimization. A new hiding method based evolutionary multi-objective optimization (EMO) is proposed and the side effects generated by the hiding process are formulated as optimization goals. EMO is used to find candidate transactions to modify so that side effects are minimized. Comparative experiments with exact methods on real datasets demonstrated that the proposed method can hide sensitive rules with fewer side effects.

Sub-pixel displacement measurement in Digital Image Correlation (DIC) is an optimization problem in search of a point of the deformed image where has the maximum correlation coefficient value to the point of the reference image. Particle Swarm Optimization (PSO) is a novel swarm intelligence algorithm which has great prospect in multidimensional nonlinear optimization because of its advantages of high convergence rate, global search effectiveness, robustness and having few parameters. In this paper, PSO is first applied into DIC to get sub-pixel displacement fields with our aim of proving the feasibility of this method. Two displacements and four deformation gradients for a subset of DIC can all be taken into PSO, while we only take the first two displacements into calculation in consideration of rigid body translation. With set iterative time 20, an accuracy of 0.005 pixels has been achieved between two computer-simulated images by using basic PSO algorithm. In order to limit positions of particles within small areas and thus reduce iterative calculation, an improved integer pixel displacement search method called Grid Method is put forward for accurate integer pixel displacement measurement. Besides, through empirical study, Inverse Function Decreasing Inertia Weight has been proposed and other parameters of PSO have been discussed concerning DIC correlation coefficient function while we attempt to minimize the iterative calculation. We compare the proposed parameters with commonly used PSO parameters and find that PSO with the proposed parameters has much higher convergence rate. So Inverse Function Decreasing Inertia Weight and the set parameters are recommended for use in the PSO method of DIC.

All methods of association rule mining require the frequent sets of items, that occur together sufficiently often to be the basis of potentially interesting rules, to be first computed. The cost of this increases in proportion to the databaseL size, and also with its density. Densely-populated databases can give rise to very large numbers of candidates that must be counted. Both these factors cause performance problems, especially when the data structures involved become too large for primary memory. In this paper we describe a method of partitioning that organises the data into tree structures that can be processed independently. We present experimental results that show the method scales well for increasing dimensions of data, and performs significantly better than alternatives, especially when dealing with dense data and low support thresholds.