Association Rule Mining Method Research Articles

Detection of Schistosomiasis Factors Using Association Rule Mining

Bilharzia or schistosomiasis is one of the most fatal and factitious disease happens through pollute which become a significant reason of deaths in the world. Prediction and factors identification that become causes of disease in early stage, may escort to treatment before it becomes critical. Data mining techniques are used to assist medical professionals effectively in diseases’ classification. This research investigates the recovery and death factors which contributes to schistosomiasis disease preprocessed dataset, collected from Hubei, China. A computerized learning method, association rule mining (Apriori) is used to spot factors. Different tools were used for analysis and model evaluation with minimum support and minimum confidence indicated higher than 90% to generate rules. In addition, attributes indicating recovery and death of individuals were identified. Strong associations of disease factors; BMI, viability, nourishment, extent to ascites etc. determined and classified through Apriori algorithm. Further, results generated by association rule mining method may useful for professionals in treatment decision with better precision.

Quantitative analysis of the influence of learning resource scheduling in MOOC mode on traditional education and teaching

In view of the traditional method to evaluate the impact of MOOC model on traditional education and teaching, there is a problem of poor adaptive performance and low resource scheduling ability. A teaching ability evaluation combination model based on resource scheduling data envelopment analysis method is constructed. This method uses the traditional teaching mode to fuzzy clustering MOOC subject resources, analyses the correlation function of teaching ability, and uses the association rule mining method to mine the teaching ability characteristics. According to the teaching ability and the linear combination model of traditional education and teaching, the MOOC learning resources are completed. Data scheduling; construct a statistical model of data envelopment analysis to quantitatively assess the impact of MOOC model on teaching ability and traditional education and teaching. The experimental results show that the proposed method has good adaptive performance, high resource scheduling capability and high accuracy.

Quantitative Analysis of the Influence of Learning Resource Scheduling in MOOC Mode on Traditional Education and Teaching

In view of the traditional method to evaluate the impact of MOOC model on traditional education and teaching, there is a problem of poor adaptive performance and low resource scheduling ability. A teaching ability evaluation combination model based on resource scheduling data envelopment analysis method is constructed. This method uses the traditional teaching mode to fuzzy clustering MOOC subject resources, analyses the correlation function of teaching ability, and uses the association rule mining method to mine the teaching ability characteristics. According to the teaching ability and the linear combination model of traditional education and teaching, the MOOC learning resources are completed. Data scheduling; construct a statistical model of data envelopment analysis to quantitatively assess the impact of MOOC model on teaching ability and traditional education and teaching. The experimental results show that the proposed method has good adaptive performance, high resource scheduling capability and high accuracy.

Finding Relationship among Various Students’ Attributes with Sport-Interest using Association Rule Mining Method

Data mining methods are widely used in educational domain for the purpose of finding useful information from the large student’s database. This information is then used to understand the behaviors of students in respect of their academic and other curricular performance. One of such Data mining methods called Association rule mining is used in this research study to analyze the student’s database of Career College Bhopal using two mining tools called Weka and XLMiner. The database contain records of 212 students with main attributes like Student’s Gender, Category, Subject, name of district where he/she belong and their parent’s/guardian’s occupation/profession and sport-interest. Sport department of any educational institute also need to understand the behavior and psychology of students for their sport interest to make sport-policy for their institute. In this paper, author has found some unknown relationship among these attributes with respect to sport-interest which is a target attribute. This experimental study has generated many association rules that can be used to answer the questions like which student from particular course, district and category will participate in sports? Which sport usually prefer by male and female student most? Which student can be performed better in which sport? In this way sport policy maker can use these mined information about the sport interest of students to make better decisions in sport framework in an educational institute.

Binary Particle Swarm Optimization-Based Association Rule Mining for Discovering Relationships between Machine Capabilities and Product Features

An effective data mining method to automatically extract association rules between manufacturing capabilities and product features from the available historical data is essential for an efficient and cost-effective product development and production. This paper proposes a new binary particle swarm optimization- (BPSO-) based association rule mining (BPSO-ARM) method for discovering the hidden relationships between machine capabilities and product features. In particular, BPSO-ARM does not need to predefine thresholds of minimum support and confidence, which improves its applicability in real-world industrial cases. Moreover, a novel overlapping measure indication is further proposed to eliminate those lower quality rules to further improve the applicability of BPSO-ARM. The effectiveness of BPSO-ARM is demonstrated on a benchmark case and an industrial case about the automotive part manufacturing. The performance comparison indicates that BPSO-ARM outperforms other regular methods (e.g., Apriori) for ARM. The experimental results indicate that BPSO-ARM is capable of discovering important association rules between machine capabilities and product features. This will help support planners and engineers for the new product design and manufacturing.

Data mining based framework to identify rule based operation strategies for buildings with power metering system

Operation strategies influence the building energy efficiency. In order to enhance the building energy efficiency, it’s necessary to adopt proper operation strategies on building equipment. Thus, the identification of existing operation strategies is necessary for the improvement of operation strategies. A data mining (DM) based framework is proposed in this paper to automatically identify the building operation strategies. The framework includes classification and regression tree (CART), and weighted association rule mining (WARM) method, targeting at three types of rule based control strategies: on/off control, sequencing control (for equipment of the same type), and coordinated control (for equipment of different types). The performance of this framework is validated with power metering system data and manual identification results based on on-site survey of three buildings in Shanghai. The validation results suggest that the proposed framework is capable of identifying building operation strategies accurately and automatically. Implemented on the original software named BOSA (Building Operation Strategy Analysis), this framework is promising to be used in engineering field to enhance the efficiency of building operation strategy identification work.

C‐BEAM: A confidence‐based evaluation of MCQs for providing feedback to instructors

AbstractMultiple‐choice questions (MCQs) examination is the most preferred methodology for evaluating knowledge in a specific context for a large number of students. The problem with MCQs is that students apply probabilistic approach to guess answers for scoring marks. Depending on the accuracy of the prediction, it may happen that the examinees who have little or no knowledge score more than or equal to the examinees who have better knowledge. Unexpected failures of the candidates who have passed MCQ based prestigious competitive examinations have also been observed. To overcome these contradictions, this article proposes a novel Confidence‐Based Evaluation Approach for MCQs (C‐BEAM) that curb the use of probabilistic prediction. The proposed marking scheme has been compared with well‐known marking schemes like no‐negative marking, one‐fourth negative marking, and one‐third negative marking. These marking schemes have been applied for the evaluation of eight MCQ quizzes on three engineering subjects that were attempted by more than 500 students at MNNIT Allahabad, India. The analysis of results proves C‐BEAM marking scheme as more effective than other compared methods in minimizing the effect of guessed answers. In addition to the effectiveness, C‐BEAM forms the basis for the confidence‐based feedback of students. This article also applies association rule mining methods to the evaluation data generated from C‐BEAM method for discovering association rules. These rules are confidence‐based feedback of students that would help instructors to improve the teaching process.

LTARM: A novel temporal association rule mining method to understand toxicities in a routine cancer treatment

Cancer is a worldwide problem and one of the leading causes of death. Increasing prevalence of cancer, particularly in developing countries, demands better understandings of the effectiveness and adverse consequences of different cancer treatment regimes in real patient populations. Current understandings of cancer treatment toxicities are often derived from either “clean” patient cohorts or coarse population statistics. Thus, it is difficult to get up-to-date and local assessments of treatment toxicities for specific cancer centers. To address these problems, we propose a novel and efficient method for discovering toxicity progression patterns in the form of temporal association rules (TARs). A temporal association rule is defined as a rule where the diagnosis codes in the right hand side (e.g., a combination of toxicities/complications) are temporally occurred after the diagnosis codes in the left hand side (e.g., a particular type of cancer treatment). Our method develops a lattice structure to efficiently discover TARs. More specifically, the lattice structure is first constructed to store all frequent diagnosis codes in the dataset. It is then traversed using the paternity relations among nodes to generate TARs. Our extensive experiments show the effectiveness of the proposed method in discovering major toxicity patterns in comparison with the temporal comorbidity analysis. In addition, our method significantly outperforms existing methods for mining TARs in terms of runtime.

Shared Variable Extraction and Hardware Implementation for Nonlinear Boolean Functions Based on Swarm Intelligence

To solve the problem of complex relationships among variables and the difficulty of extracting shared variables from nonlinear Boolean functions (NLBFs), an association logic model of variables is established using the classical Apriori rule mining algorithm and the association analysis launched during shared variable extraction (SVE). This work transforms the SVE problem into a traveling salesman problem (TSP) and proposes an SVE based on particle swarm optimization (SVE-PSO) method that combines the association rule mining method with swarm intelligence to improve the efficiency of SVE. Then, according to the shared variables extracted from various NLBFs, the distribution of the shared variables is created, and two corresponding hardware circuits, Element A and Element B, based on cascade lookup table (LUT) structures are proposed to process the various NLBFs. Experimental results show that the performance of SVE via SVE-PSO method is significantly more efficient than the classical association rule mining algorithms. The ratio of the rules is 80.41%, but the operation time is only 21.47% when compared to the Apriori method, which uses 200 iterations. In addition, the area utilizations of Element A and Element B expended by the NLBFs via different parallelisms are measured and compared with other methods. The results show that the integrative performances of Element A and Element B are significantly better than those of other methods. The proposed SVE-PSO method and two cascade LUT-structure circuits can be widely used in coarse-grained reconfigurable cryptogrammic processors, or in application-specific instruction-set cryptogrammic processors, to advance the performance of NLBF processing and mapping.

Method of Spare Parts Prediction Models Evaluation Based on Grey Comprehensive Correlation Degree and Association Rules Mining: A Case Study in Aviation

Probability of spare parts sufficiency is crucial in the process of the normal operation of businesses, especially for the airline company. However, higher support sufficiency could inevitably lead to the increase of inventory cost of spare parts and restrict a company’s efficiency. Therefore, it is important for businesses to reduce material cost on the premise of normal operation in order to accurately predict spare parts requirements based on reasonable models. The purpose of this paper is to solve problems with the evaluation of spare parts prediction models and to improve efficiency of company. Firstly, this paper summarizes a series of prediction models of spare parts requirements and applies the grey comprehensive correlation degree to rank the models. Secondly, the method of association rules mining is used to discover the association relationships between the types of spare parts and the prediction models. Finally, a case study in aviation is given to demonstrate the feasibility of the methodology, and optimal prediction models are recommended for aircraft spare parts. In accordance with the association relationships, the applicable prediction model can be provided in terms of different types of spare parts. This model will greatly enhance the work efficiency of spare parts prediction and improve the prediction tasks for the aircraft companies.

Kavosh: an effective Map-Reduce-based association rule mining method

The immense amount of data generated on a daily basis by various devices and systems necessitates a change in data analysis methods. As an important part of analytics, data mining methods require a paradigm shift to solve problems because the old methods cannot manage massive data. Association rule mining is a data mining algorithm used to solve various domain problems. Because of the immense volume of data, one-node solutions are no longer useful, and it is necessary to solve problems by using a distributed and shared-nothing architecture such as Map-Reduce. However, when association rule mining is transferred to these architectures, new problems appear. The main problems are lack of data locality and iteration support and process skewness. In this paper, a method is proposed that solves these problems. Kavosh converts data into a unified format that helps nodes perform their tasks independently without the need to exchange data with other nodes. In addition, the proposed method compresses input data to facilitate data management. Another advantage is the lack of process skewness because it is possible to allocate a predefined amount of data to each node. Kavosh omits iterations required for finding frequent itemsets by changing the Map-Reduce architecture. The proposed method is implemented using Hadoop, and the results are compared with open-source products in terms of three aspects: execution time, load balancing and data compression. The results show that Kavosh outperforms other methods in these aspects.

Method of Association Rules Mining and Its Application in Analysis of Seawater Samples

This paper aims to set up new rules for processing seawater quality monitoring data collected by photoelectric sensor network, and mine out the useful information contained in the data. For this purpose, the immune algorithm was introduced to the classical genetic algorithm, the fitness function was designed, and the crossover and mutation probabilities were adjusted, thus creating the adaptive immune genetic algorithm (IIGA). The new algorithm was described in details and applied in an actual case. Through the comparison between the IIGA, IGA and apriori algorithms, the author concluded that the IIGA not only shortened the mining time, but also ensured the operation accuracy. The research findings are of great importance to the association rules mining in various fields.

Correlation analysis between cold and heat property of traditional Chinese medicines and light conditions of their original plants in habitat

The current study was conducted to explore the relationship between property of traditional Chinese medicines (TCM) and the light conditions of their original plant in habitats and advance a "light-cold and heat property" hypothesis under the guidance of theory of TCM. In the present study, we selected 127 common Chinese herbs. Association rules mining method was used to find out the association relationship between the cold-heat properties of different TCM and the habitats of their original plant, while using statistical methods to analyze the correlation between light condition and cold-heat property of TCM based on the description of the habitats in the Flora of China and investigation. The results showed that the habitats associated to TCM with cold property are in the order of grassland>curb>hillside>wasteland>gully>farmland>riverside. The habitats that associated to TCM with heat property are: undergrowth>shrub>gully>jungle. For 68 TCMs with cold property, 81.2% of their original plants are light-demanding, 8.7% are shade-demanding, and 8.7% of them showed no obvious light preference. For 59 TCMs with heat property, 62.7% of their original plants are shade-demanding, 20.3% are shade-demanding, and 17.0% of them showed no obvious light preference. Meanwhile, Chi-square test showed that there was a significant difference between the cold-heat property and light preference (P<0.01). In summary, there is a significant correlation between the light preference of TCM original plants and their cold-heat properties. In order to ensure the pharmacological effect of TCM, the relationship between the light conditions and the properties of TCM should be considered in the cultivation of medicinal plants for TCM.

A Method of Mining Association Rules for Geographical Points of Interest

Association rule (AR) mining represents a challenge in the field of data mining. Mining ARs using traditional algorithms generates a large number of candidate rules, and even if we use binding measures such as support, reliability, and lift, there are still several rules to keep, and domain experts are needed to extract the rules of interest from the remaining rules. The focus of this paper is on whether we can directly provide rule rankings and calculate the proportional relationship between the items in the rules. To address these two questions, this paper proposes a modified FP-Growth algorithm called FP-GCID (novel FP-Growth algorithm based on Cluster IDs) to generate ARs; in addition, a new method called Mean-Product of Probabilities (MPP) is proposed to rank rules and compute the proportion of items for one rule. The experiment is divided into three phases: the DBSCAN (Density-Based Scanning Algorithm with Noise) algorithm is used to cluster the geographic interest points and map the obtained clusters into corresponding transaction data; FP-GCID is used to generate ARs, which contain cluster information; and MPP is used to choose the best rule based on the rankings. Finally, a visualization of the rules is used to validate whether the two previously stated requirements were fulfilled.

Dynamic optimisation based fuzzy association rule mining method

Techniques of performance analysis, comprising of various metrics such as accuracy, efficiency and consuming time, have been conducted to evaluate the measures of properties and interestingness for the association rule mining method. Therefore, these metrics combined with different parameters (partitioning points, fuzzy sets) should be analysed thoroughly and balanced simultaneously to enhance the entire performance (effectiveness, accuracy and efficiency) for an algorithm. As a result, Most of the current algorithms face the pressure from the tradeoff of these metrics and parameters, which becomes even rougher when we employ it in different resources of data (discrete data, categorical data and continuous data). Specifically, serial data (i.e., sequences or transactions of floating point numbers), such as analysis of sensor streaming data, financial streaming data, medical streaming data and sentimental streaming data, are different from discrete variables, such as boolean data (e.g., sentiment: negative and positive represented as ‘0’ and ‘1’ separately) and categorical data (e.g., ‘young age’, ‘middle age’, ‘old age’). The main difference is that serial data face sharp boundary’s problem. That is, it is hard to decide the boundary values (i.e., the single points to partition data into different value groups), which is few to be solved in association rule mining methods. This paper aims to resolve the problem of sharp boundaries and balance multiple performances of our algorithm simultaneously by developing a novel dynamic optimisation (parameters and metrics) based fuzzy association rule mining (DOFARM) method. The proposed method can be applied in a wide range of classifying problems, such as the classification of sentiment strength (negative and positive). In our DOFARM method, instead of single partitioning points, we use a range of values to smoothly separate two consecutive partitions and develop a corresponding membership function to generate fuzzy sets for original data sets of physical and emotional diseases. Mainly, we design a dual compromise scheme: the first tradeoff balances better performance of out-putting association rules and more widely applicable fuzzy membership function while the second tradeoff reduces the time parameter as well as enhances the entire performance of our DOFARM method. The feasibility and accuracy of DOFARM we proposed have been certified theoretically and experimentally. Besides, we demonstrate the accuracy, effectiveness and efficiency for our DOFARM method by experiments according to both synthesis and real datasets.

Analytical Study of Association Rule Mining Methods in Data Mining

In data processing, the foremost common and effective technique is to spot frequent pattern victimization association rule mining. There are such a large amount of algorithms that provides simple and effective method of association rule mining, however still some analysis is required which might improve potency of association rule mining. As we have a tendency to operate with immense historical information (homogeneous or heterogeneous), it is important to spot frequent patterns quickly and accurately. Here during this analytical paper, we have been tried to incorporate survey of analysis systematically towards association rule mining from last many years to till date from totally different researchers. It’s true that one paper isn't enough for complete analysis of all smart researches, however it'll facilitate in future to urge right direction towards association rule mining analysis for fascinating, effective and correct analysis.

A Novel Association Rule Mining Method of Big Data for Power Transformers State Parameters Based on Probabilistic Graph Model

The correlative change analysis of state parameters can provide powerful technical supports for safe, reliable, and high-efficient operation of the power transformers. However, the analysis methods are primarily based on a single or a few state parameters, and hence the potential failures can hardly be found and predicted. In this paper, a data-driven method of association rule mining for transformer state parameters has been proposed by combining the Apriori algorithm and probabilistic graphical model. In this method the disadvantage that whenever the frequent items are searched the whole data items have to be scanned cyclically has been overcame. This method is used in mining association rules of the numerical solutions of differential equations. The result indicates that association rules among the numerical solutions can be accurately mined. Finally, practical measured data of five 500 kV transformers is analyzed by the proposed method. The association rules of various state parameters have been excavated, and then the mined association rules are used in modifying the prediction results of single state parameters. The results indicate that the application of the mined association rules improves the accuracy of prediction. Therefore, the effectiveness and feasibility of the proposed method in association rule mining has been proved.

DISCOVERY AND ANALYSIS OF JOB ELIGIBILITY AS ASSOCIATION RULES BY APRIORI ALGORITHM

Association rule mining is a data mining technique in which pattern of occurrences of one set of items with another set of items in databases of transactions are discovered as rules of implication with certain measures of interestingness. Support or the frequency of occurrences of sets of items and confidence are the most widely used measures of interestingness of association rules of the form X→Y where X and Y are disjoint sets of items. Though the problem of association rule mining emerged from analysis of market basket data in supermarket there are numerous areas of applications of association rule mining technique. In this paper, association rule mining method is applied to discover and analyze eligibility criteria for jobs from a large set of data for choosing career and professional goals effectively. For this the data are collected by conducting a wide survey and is prepared and modeled suitably. Then the a priori algorithm is implemented for discovering the frequent itemsets and the association rules. The discovered rules are then classified based on the kind of jobs and also based on the kinds of qualifications. The discovered results are analyzed and interpreted and the computational performances are also analyzed.

A Weighted Association Rule Mining Method for Predicting HCV-Human Protein Interactions

Background: Hepatitis C Virus causes the most severe form of chronic liver disease and nearly 200 million people worldwide are estimated to be infected with this virus. Much about the HCV pathogenesis process is still unknown. The study of interactions between HCV and human proteins will lead to deeper understanding of HCV mechanism. Objective: The objective of this paper is to predict potentially new HCV-Human protein interactions using a weighted association rule mining technique. Methods: A new computational method was developed for mining associations within a bipartite graph that was constructed from the HCV-human protein interactions dataset. A new mathematical model was applied to weigh the discovered association rules based on Gene Ontology annotations of viral and human proteins. HCVpro database was used to generate a human-viral bipartite graph that was then analyzed computationally to extract biclusters within the graph. Association rules were extracted from the bipartite graph and weighted using a mathematical model that incorporated information about proteins available from Gene Ontology knowledge base. Results: Forty two new interactions between HCV and human proteins were predicted. Some of these predicted interactions were validated through literature survey and enrichment studies such as Gene ontology-based analysis, pathway- based analysis and disease association based analysis. Conclusion: The methodology developed in this paper can also be used for various other kind of data analysis and hence it carries a wide scope. This will be useful to conduct similar kind of experiments for other disease databases. Keywords: Weighted association rule mining, gene ontology, HCV-human PPI network, bi-clustering, computational PPI prediction, enrichment studies.

Application of rule association with algorithm apriori of disaster residental fires

&#x0D; &#x0D; &#x0D; &#x0D; Data mining is a technique of decision making by means of extracting information based on historical data of existing data in a large database. One of technique in data mining, is association rule algorithm where the method is searching for a set of items that frequently appear together. This study will use data association rule mining method for data processing Fire Disaster settlements in Indonesia because we want to know what information is often occur together in the event of fire disaster settlement. From the analysis associative relationship, event's pattern that occur from residential fires in Indonesia which the data is from the beginning of January 2015 to June 2015, support the highest value that the event of catastrophic fires in settlements in the afternoon resulted in broken homes with a value of 0.8148148 support and confident value of 1.0292398.&#x0D; &#x0D; &#x0D; &#x0D;