Association Rule Mining Algorithm Research Articles

Investigating Health Equity and Healthcare Needs among Immigrant Women Using the Association Rule Mining Method.

Equitable access to healthcare services is a major concern among immigrant women. Thus, this study investigated the relationship between socioeconomic characteristics and healthcare needs among immigrant women in Taiwan. The secondary data was obtained from “Survey of Foreign and Chinese Spouses’ Living Requirements, 2008”, which was administered to 5848 immigrant women by the Ministry of the Interior, Taiwan. Additionally, descriptive statistics and significance tests were used to analyze the data, after which the association rule mining algorithm was applied to determine the relationship between socioeconomic characteristics and healthcare needs. According to the findings, the top three healthcare needs were providing medical allowances (52.53%), child health checkups (16.74%), and parental knowledge and pre- and post-natal guidance (8.31%). Based on the association analysis, the main barrier to the women’s healthcare needs was “financial pressure”. This study also found that nationality, socioeconomic status, and duration of residence were associated with such needs, while health inequality among aged immigrant women was due to economic and physical factors. Finally, the association analysis found that the women’s healthcare problems included economic, socio-cultural, and gender weakness, while “economic inequality” and “women’s health” were interrelated.

Jaguar movement behavior: using trajectories and association rule mining algorithms to unveil behavioral states and social interactions.

Animal movement data are widely collected with devices such as sensors and collars, increasing the ability of researchers to monitor animal movement and providing information about animal behavioral patterns. Animal behavior is used as a basis for understanding the relationship between animals and the environment and for guiding decision-making by researchers and public agencies about environmental preservation and conservation actions. Animal movement and behavior are widely studied with a focus on identifying behavioral patterns, such as, animal group formation, the distance between animals and their home range. However, we observed a lack of research proposing a unified solution that aggregates resources for analyses of individual animal behavior and of social interactions between animals. The primary scientific contribution of this work is to present a framework that uses trajectory analysis and association rule mining [Jaiswal and Agarwal, 2012] to provide statistical measures of correlation and dependence to determine the relationship level between animals, their social interactions, and their interactions with other environmental factors based on their individual behavior and movement data. We demonstrate the usefulness of the framework by applying it to movement data from jaguars in the Pantanal, Brazil. This allowed us to describe jaguar behavior, social interactions among jaguars and their behavior in different landscapes, thus providing a highly detailed investigation of jaguar movement decisions at the fine scale.

Exploiting weighted association rule mining for indicating synergic formation tactics in soccer teams

SummaryManagers make decisions on team tactics, formations, and player selection based on their own experiences. The managers have limitations in understanding the team's situation and sometimes they can think wrong. The purpose of this study is to make decisions on player selection and tactical formation according to the level of the opponent based on the data, not on the intuition of the manager. In our previous study, the Boruta algorithm was used to extract important features from 69 features in soccer player data by position. The detailed roles of each position were defined by using K‐means algorithm. For example, the detailed roles of each position were defined as Mezzala, Shadow Striker, Deep‐lying playmaker, and so on. That is, forward positions are classified as Target Man (TM) and Shadow Striker (SS). TM is a high‐goal, high‐competitive forward, and SS is a high‐dribble, high‐pass forward. In this study, we analyze a clustering dataset and the game appearance dataset. The game appearance dataset are divided into CL (Champions league Level), EL (Europa league Level), ML (Middle Level), and RL (Relegation Level). Association rule mining algorithm analyzes the synergy between positions, and selects a position with high synergy. Weighted association rule mining algorithm establishes player selection and tactical formation with the weight, which is the player's rating data. Finally, using the obtained results, we visualize the synergy between positions, tactical formation, and player characteristics depending on the level of the opponent.

Parallel Design of Apriori Algorithm Based on the Method of “Determine Infrequent Items & Remove Infrequent Itemsets”

In the method of fault association rule diagnosis, Apriori algorithm has low efficiency for big data processing. In this paper, aiming at the defects of Apriori algorithm, MapReduce computing framework is used to optimize the Apriori association rule algorithm. This method improves the accuracy of association mining in fault diagnosis. In the process of optimization, this paper proposes the method of “Determine Infrequent Items & Remove Infrequent Itemsets”. Through experiments, this method effectively reduces the computational space needed by Apriori algorithm in association rule mining, and improves the computing speed.

Optimization of Teaching Management System Based on Association Rules Algorithm

The teaching management department carries all the work related to teaching in the whole school. A scientific, efficient, and complete teaching management system cannot only help the teaching management department improve work efficiency and quality but also greatly reduce many problems caused by manual labour risk. This paper designs and implements a teaching management system based on an improved association rule algorithm. First, aiming at the low efficiency of the Apriori algorithm for mining association rules, an association rule model based on interest is proposed. Second, use the MapReduce calculation model to partition the transaction database, then use the improved Apriori optimization algorithm for mining, and finally merge the mining results to obtain frequent itemsets. Through experiments, the optimized algorithm has greatly improved selection mining and computing time than traditional algorithms.

Investigation of tugboat accidents severity: An application of association rule mining algorithms

This paper aims to investigate tugboat accidents using various association rule mining algorithms. A total of 477 tugboat accident records obtained from the Information Handling Services (IHS) Sea-Web database for the period of 2008–2017 were analysed. Apriori, Predictive Apriori and FP-Growth algorithms were employed to extract the association rules of the tugboat accidents dataset. The present study revealed that tugboats aged over 20 years are crucial indicators for serious accidents. Hull/machinery damage and collision type accidents, on the other hand, constitute more than half of the total tugboat accidents. Association rule mining also showed that four of the five rules for serious accidents are attributed to hull/machinery damage. The results of this study are thought to be beneficial for tugboat and ship operators, port management and public authorities regarding the awareness of the factors affecting tugboat accidents.

An improved approach for mining association rules in parallel using Spark Streaming

SummaryParallel computing is an effective method to solve computationally large and data‐intensive problems. The traditional data mining algorithm cannot mining association rules for large amounts of streaming data in a timely and effectively. In order to improve the speed and accuracy of association rules mining, distributed and parallel algorithms have become a research focus. This paper proposes a parallel FP‐growth approach using Spark Streaming, called SSPFP, which can parallel mining frequent itemsets and association rules in real‐time streaming data. In this paper, the proposed SSPFP algorithm is applied to mining the association rules between temperature and salinity in marine Argo data. The experimental results indicate that SSPFP algorithm is efficient for association rules mining.

A novel association rule mining method for the identification of rare functional dependencies in Complex Technical Infrastructures from alarm data

This work presents a data-driven method for identifying rare functional dependencies among components of different systems of Complex Technical Infrastructures (CTIs) from large-scale databases of alarm messages. It is based on the representation of the alarm data in a binary form, the use of a novel association rule mining algorithm properly tailored for discovering rare dependencies among components of different systems and on the identification of groups of functionally dependent components. The proposed method is applied to a synthetic alarm database generated by a simulated CTI model and to a real large-scale database of alarms collected in the CTI of CERN (European Organization for Nuclear Research). The obtained results show the effectiveness of the proposed method.

Research on parallelization of Apriori algorithm in association rule mining

Abstract Aiming at the performance bottleneck of traditional Apriori algorithm when the data set is slightly large, this paper adopts the idea of parallelization and improves the Apriori algorithm based on MapReduce model. Firstly, the local frequent itemsets on each sub node in the cluster are calculated, then all the local frequent itemsets are merged into the global candidate itemsets, and finally, the frequent itemsets that meet the conditions are filtered according to the minimum support threshold. The advantage of the improved algorithm is that it only needs to scan the transaction database twice and calculate the frequent item set in parallel, which improves the efficiency of the algorithm.

Clustering of Association Rules for Big Datasets using Hadoop MapReduce

Mining association rules is essential in the discovery of knowledge hidden in datasets. There are many efficient association rule mining algorithms. However, they may suffer from generating large number of rules when applied to big datasets. Large number of rules makes knowledge discovery a daunting task because too many rules are difficult to understand, interpret or visualize. To reduce the number of discovered rules, researchers proposed approaches, such as rules pruning, summarizing, or clustering. For the flourishing field of big data and Internet-of-Things (IoT), more effective solutions are crucial to cope with the rapid evolution of data. In this paper, we are proposing a novel parallel association rule clustering approach which is based on Hadoop MapReduce. We ran many experiments to study the performance of the proposed approach, and promising results have been demonstrated, e.g. the lowest scaleup was 77%.

Research on Information Security Risk Assessment Method Based on Fuzzy Rule Set

With the increasing complexity of the network structure and the increasing size of the network, various network security incidents pose an increasing threat to the security of computer systems and the network. Especially, in the network environment, the diversified intrusion methods and application environment make the security of the network more fragile. In order to improve information security, based on fuzzy rule sets, this paper proposes a fuzzy association rule mining algorithm based on fuzzy matrix and applies it to security event correlation. In addition, this paper combines the embedded system to construct an information security risk assessment system and sets the system performance based on the actual situation. Finally, this paper carries out experimental design to verify the performance of the system and analyzes the experimental results by mathematical statistics. From the experimental research, it can be seen that the system constructed in this paper has a certain effect.

Multi-Angle Movie Reviews Analysis Based on Multi Model

In this study, movie reviews are used as data sets to extract related phrases, topics, and sentiment scores from the text. Based on users’ information, users’ behavior preferences and their influences are analysed, and text semantic information is mined from multiple perspectives. A variety of data processing and machine learning methods including text segmentation, Apriori association rule mining algorithm, sentiment analysis, linear fitting, TFIDF algorithm, PCA dimensionality reduction, and LDA topic model is used in the research. At the same time, due to the coarse granularity of the topic extraction in the LDA algorithm, it is not suitable for short text, this paper proposes a new topic model based on improved k-means and TextRank and gets good results on this dataset. This paper uses multiple data mining models to analyse film reviews and presents an empirical study of the efficacy of machine learning techniques in text semantic mining.

Weighted Frequent Itemset Mining Using Weighted Subtrees: WST-WFIM

There is a wide range of association rules mining algorithms concerning the importance of weighted data to extract frequent rules. In some algorithms, each item has only one weight in the data set, while, in others, each item in each segment of the data set has only one specific weight. In this work, a new efficient algorithm named weighted frequent itemset mining using weighted subtrees (WST-WFIM) has been proposed to discover the average weight of frequent rules. This algorithm uses specific trees and some new data structures on the frequent pattern growth (FP-Growth) algorithm to calculate the average weight of discovered rules. It works on the data set that each item in each transaction has a specific weight and keeps them in the dedicated tree. Moreover, we have proposed the shared transactions' concept and WST-WFIM by using it and calculate the average weight for discovered frequent rules. The algorithm capability was evaluated by standard sparse and dense weighted data sets. The results show that, while the algorithm works on weighted transactions, in sparse data sets, relative runtime increases by a decrease in minimum support (MinSup) parameter in comparison to the FP-Growth, and memory usage is approximately the same. The variations of runtime and memory usage with the MinSup parameter are opposite for dense data sets.

A novel interestingness measure based on fusion model for association rules mining

Aiming at the shortcomings of the traditional "support-confidence" association rules mining framework and the problems of mining negative association rules, the concept of interestingness measure is introduced. Analyzed the advantages and disadvantages of some commonly used interestingness measures at present, and combined the cosine measure on the basis of the interestingness measure model based on the difference idea, and proposed a new interestingness measure model. The interestingness measure can effectively express the relationship between the antecedent and the subsequent part of the rule. According to this model, an association rules mining algorithm based on the interestingness measure fusion model is proposed to improve the accuracy of mining. Experiments show that the algorithm has better performance and can effectively help mining positive and negative association rules.

Secured Data Storage and Retrieval using Elliptic Curve Cryptography in Cloud

Security of data stored in the cloud databases is a challenging and complex issue to be addressed due to the presence of malicious attacks, data breaches and unsecured access points. In the past, many researchers proposed security mechanisms including access control, intrusion detection and prevention models, Encryption based storage methods and key management schemes. However, the role based access control policies that were developed to provide security for the data stored in cloud databases based on the sensitivity of the information are compromised by the attackers through the misuse of privileges gained by them from multiple roles. Therefore, it is necessary to propose more efficient mechanisms for securing the sensitive information through attribute based encryption by analyzing the association between the various attributes. For handling the security issue related to the large volume of cloud data effectively, the association rule mining algorithm has been extended with temporal constraints in this work in order to find the association among the attributes so that it is possible to form groups among the attributes as public attributes with insensitive data, group attributes with medium sensitive data and owner with highly sensitive attributes and data for enhancing the strength of attribute based encryption scheme. Based on the associations among the attributes and temporal constraints, it is possible to encrypt the sensitive data with stronger keys and algorithms. Hence, a new key generation and encryption algorithm is proposed in this paper by combining the Greatest common divisor and the Least common multiple between the primary key value and the first numeric non key attribute that is medium sensitive attributes and data present in the cloud database for providing secured storage through effective attribute based encryption. Moreover, a new intelligent algorithm called Elliptic Curve Cryptography with Base100 Table algorithm is also proposed in this paper for performing encryption and decryption operations over the most sensitive data for the data owners. From the experiments conducted in this work, it is observed that the proposed model enhances the data security by more than 5% when it is compared with other existing secured storage models available for cloud

Prediction of fire risk based on cloud computing

The development of cloud computing and big data analysis has given rise to various disaster prediction methods. To reduce the probability of fire accidents, it is critical to predict the fire risk by mining the massive historical data on fire. Considering the advantages of MapReduce, a cloud computing method, in parallel processing of data, this paper puts forward a novel prediction method for fire risk that mines the association rules in the time domain. Firstly, the risk of disaster-causing factors and the ability of disaster-reducing factors were evaluated. Based on the evaluation results, an evaluation index system was constructed for fire risk, and the indices were quantified through proper weighting. Facing the historical fire data, the authors designed the spatiotemporal density-based spatial clustering of applications with noise (spatiotemporal DBSCAN), and quantitatively evaluated fire risk by the association rule mining algorithm based on time domain partition (TDP). The effectiveness of our method in fire risk prediction was verified through experiments. The research results provide reference for the risk prediction of other disasters.

Towards Association Rule-based Item Selection Strategy in Computerized Adaptive Testing

One of the most important stages of Computerized Adaptive Testing is the selection of items, in which various methods are used, which have certain weaknesses at the time of implementation. Therefore, in this paper, it is proposed the integration of Association Rule Mining as an item selection criterion in a CAT system. We present the analysis of association rule mining algorithms such as Apriori, FP-Growth, PredictiveApriori and Tertius into two data set with the purpose of knowing the advantages and disadvantages of each algorithm and choose the most suitable. We compare the algorithms considering number of rules discovered, average support and confidence, and velocity. According to the experiments, Apriori found rules with greater confidence, support, in less time.

Evaluation model and management strategy for reducing pollution caused by ship collision in coastal waters

This study addresses the Spatio-temporal associations of internal and external factors (e.g., ship type, flag registry, port inspections and season, etc.) with ship collisions in coastal waters. To prevent collision and subsequent pollution, effective identification of various potential risk factors related to collisions is important. Based on 10-year collision reports, a Bayesian Spatio-temporal (BS) model is developed to assess collision risk by taking into account space and time mutual influences. In this research, the research area including parts of North China, Korean Peninsula, and Japan, is categorized into three different risk levels of potential collision. An association rule mining algorithm is then developed to analyze the spatial-temporal mutual relationships of collision elements in neighbor regions. Corresponding association rules among different collision risks are revealed for different regions. The results show that small ship size, general cargo type, and spring or summer season have a strong association with the collisions occurred in high-risk regions, while large ship size in accordance with summer or winter season is remarkable in moderate-risk regions. It is also found that large ship size and tanker ship type are significant factors in low-risk areas. Finally, management strategies are proposed to help preventing ship collisions and reducing potential pollutions.

Semen quality and waiting time to pregnancy explored using association mining.

Assessment of semen quality is a key pillar in the evaluation of men from infertile couples. Usually, semen parameters are interpreted individually because the interactions between parameters are difficult to account for. To determine how combinations of classical semen parameters and female partner age were associated with waiting time to pregnancy (TTP). Semen results of 500 fertile men, information of TTP, and partner age were used for regressions and to detect breaking points. For a modified Association Rule Mining algorithm, semen parameters were categorized as High, Medium, and Low. Men ≤32.1years and women ≤32.9years had shorter TTP than older. Decreasing TTP was associated with increasing level of individual semen parameters up to threshold values: sperm concentration 46mill/mL, total sperm count 179 mill, progressive motility 63%, and normal morphology 11.5%. Using association mining, approximately 100 combinations of semen parameters and partner age were associated with TTP. TTP≤1month often co-occurred with high percentages of progressive motility (≥62%) and morphologically normal spermatozoa (≥10.5%). Furthermore, TTP≤1 did not tend to appear with lower percentages of these two semen parameters or high partner age (≥32years). However, high percentages of motile or normal spermatozoa could not compensate for sperm concentration ≤42 mill/mL or total sperm count ≤158 mill. The prolonging effect of high partner age could not be compensated for by the man's semen quality. Using association mining, we observed that TTP was best predicted when combinations of semen parameters were accounted for. Sperm counts, motility, and morphology were all important, and no single semen parameter was inferior. Additionally, female age above 32years had a negative impact on TTP that could not be compensated for by high semen parameters of the man.

Association rules extraction for the identification of functional dependencies in complex technical infrastructures

This work proposes a method for identifying functional dependencies among components of complex technical infrastructures using databases of alarm messages. The developed method is based on the representation of the alarm database by a binary matrix, the use of the Apriori algorithm for mining association rules and a new algorithm for identifying groups of functionally dependent components. The effectiveness of the proposed method is shown by means of its application to an artificial case study and a real large-scale database of alarms generated by different supervision systems of the complex technical infrastructure of CERN (European Organization for Nuclear Research).