Data Mining Association Rules Research Articles

A Summary of Research on Frequent Itemsets Mining Technology

In recent years, communication technology and Internet technology are developing rapidly. In this context, people pay more and more attention to the importance of data, especially the implied information with timeliness and value, and accessing to such information relies on data mining. Data mining technology has many important branches. In recent years, data mining has been widely concerned about the association rules data mining technology is one of them, and its biggest advantage is the ability to derive valuable, but less significant, patterns from a large amount of information. The key and core problem of association rule data mining is frequent itemsets mining. Based on the research status of frequent itemsets mining technology, the research results and basic ideas of frequent itemsets mining algorithms are discussed and analyzed.

Application of Improved Eclat Algorithm in Students’ Evaluation of Teaching

The evaluation system of students is to find a way to solve the status way according to the exact needs of students and the teaching requirements of teachers, so as to improve the teaching level of teachers and improve the quality of school education. This paper uses the real evaluation sample and uses the data mining association rule algorithm to comprehensively analyze the massive data of the evaluation data and the basic information of the teacher. The purpose is to obtain the association rules between the teacher’s comprehensive information and its evaluation results. Using the evaluation data to explore its core issues. In this paper, the Eclat algorithm of association rules improves the problem of insufficient memory and occupying a large amount of time when searching for frequent itemsets in the data. The breadth-first algorithm is added to save operation time and improve the efficiency of the algorithm. The effectiveness of the improved algorithm is verified by comparative experiments and applied to the evaluation system so as to provide suggestions for the professional development of teachers from an objective perspective, and to build a harmonious, "people-oriented" evaluation system for students.

Designing a more efficient, effective and safe Medical Emergency Team (MET) service using data analysis.

IntroductionHospitals have seen a rise in Medical Emergency Team (MET) reviews. We hypothesised that the commonest MET calls result in similar treatments. Our aim was to design a pre-emptive management algorithm that allowed direct institution of treatment to patients without having to wait for attendance of the MET team and to model its potential impact on MET call incidence and patient outcomes.MethodsData was extracted for all MET calls from the hospital database. Association rule data mining techniques were used to identify the most common combinations of MET call causes, outcomes and therapies.ResultsThere were 13,656 MET calls during the 34-month study period in 7936 patients. The most common MET call was for hypotension [31%, (2459/7936)]. These MET calls were strongly associated with the immediate administration of intra-venous fluid (70% [1714/2459] v 13% [739/5477] p<0.001), unless the patient was located on a respiratory ward (adjusted OR 0.41 [95%CI 0.25–0.67] p<0.001), had a cardiac cause for admission (adjusted OR 0.61 [95%CI 0.50–0.75] p<0.001) or was under the care of the heart failure team (adjusted OR 0.29 [95%CI 0.19–0.42] p<0.001).Modelling the effect of a pre-emptive management algorithm for immediate fluid administration without MET activation on data from a test period of 24 months following the study period, suggested it would lead to a 68.7% (2541/3697) reduction in MET calls for hypotension and a 19.6% (2541/12938) reduction in total METs without adverse effects on patients.ConclusionRoutinely collected data and analytic techniques can be used to develop a pre-emptive management algorithm to administer intravenous fluid therapy to a specific group of hypotensive patients without the need to initiate a MET call. This could both lead to earlier treatment for the patient and less total MET calls.

Understanding factors associated with misclassification of fatigue-related accidents in police record

IntroductionFatigue is one of the riskiest causes of traffic accidents threatening road safety. Due to lack of proper criteria, the identification of fatigue-related accidents by police officers largely depends on inferential evidence and their own experience. As a result, many fatigue-related accidents are misclassified and the harmfulness of fatigue on road safety is misestimated. MethodIn this paper, a joint model framework is introduced to analyze factors contributing to misclassification of a fatigue-related accident in police reports. Association rule data mining technique is employed to identify the potential interactions of factors, and logistic regression models are applied to analyze factors that hinder police officers' identification of fatigue-related accidents. Using the fatigue-related crash records from Guangdong Province during 2005–2014, factors contributing to the false positive and false negative detection of the fatigue-related accident have been identified and compared. ResultsSome variables and interactions were identified to have significant impacts on fatigue-related accident detection. ConclusionsBased on the results, it can be inferred that the stereotype of certain groups of drivers, crash types, and roadway conditions affects police officers' judgment on fatigue-related accidents. Practical applicationsThis finding can provide useful information for training police officers and build better criteria for fatigue identification.

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;

In-Depth Analysis of Energy Efficiency Related Factors in Commercial Buildings Using Data Cube and Association Rule Mining

Significant amounts of energy are consumed in the commercial building sector, resulting in various adverse environmental issues. To reduce energy consumption and improve energy efficiency in commercial buildings, it is necessary to develop effective methods for analyzing building energy use. In this study, we propose a data cube model combined with association rule mining for more flexible and detailed analysis of building energy consumption profiles using the Commercial Buildings Energy Consumption Survey (CBECS) dataset, which has accumulated over 6700 existing commercial buildings across the U.S.A. Based on the data cube model, a multidimensional commercial sector building energy analysis was performed based upon on-line analytical processing (OLAP) operations to assess the energy efficiency according to building factors with various levels of abstraction. Furthermore, the proposed analysis system provided useful information that represented a set of energy efficient combinations by applying the association rule mining method. We validated the feasibility and applicability of the proposed analysis model by structuring a building energy analysis system and applying it to different building types, weather conditions, composite materials, and heating/cooling systems of the multitude of commercial buildings classified in the CBECS dataset.

A Survey for Association Rule Mining in Data Mining

Data mining, also identified as knowledge discovery in databases has well-known its place as an important and significant research area. The objective of data mining (DM) is to take out higher-level unknown detail from a great quantity of raw data. DM has been used in a variety of data domains. DM can be considered as an algorithmic method that takes data as input and yields patterns, such as classification rules, itemsets, association rules, or summaries, as output. The ’classical’ associations rule issue manages the age of association rules by support portraying a base level of confidence and support that the roduced rules should meet. The most standard and classical algorithm used for ARM is Apriori algorithm. It is used for delivering frequent itemsets for the database. The essential thought behind this algorithm is that numerous passes are made the database. The total usage of association rule strategies strengthens the knowledge management process and enables showcasing faculty to know their customers well to give better quality organizations. In this paper, the detailed description has been performed on the Genetic algorithm and FP-Growth with the applications of the Association Rule Mining.

Evaluation of rational nonsteroidal anti-inflammatory drugs and gastro-protective agents use; association rule data mining using outpatient prescription patterns

BackgroundNonsteroidal anti-inflammatory drugs (NSAIDs) and gastro-protective agents should be co-prescribed following a standard clinical practice guideline; however, adherence to this guideline in routine practice is unknown. This study applied an association rule model (ARM) to estimate rational NSAIDs and gastro-protective agents use in an outpatient prescriptions dataset.MethodsA database of hospital outpatients from October 1st, 2013 to September 30th, 2015 was searched for any of following drugs: oral antacids (A02A), peptic ulcer and gastro-oesophageal reflux disease drugs (GORD, A02B), and anti-inflammatory and anti-rheumatic products, non-steroids or NSAIDs (M01A). Data including patient demographics, diagnoses, and drug utilization were also retrieved. An association rule model was used to analyze co-prescription of the same drug class (i.e., prescriptions within A02A-A02B, M01A) and between drug classes (A02A-A02B & M01A) using the Apriori algorithm in R. The lift value, was calculated by a ratio of confidence to expected confidence, which gave information about the association between drugs in the prescription.ResultsWe identified a total of 404,273 patients with 2,575,331 outpatient visits in 2 fiscal years. Mean age was 48 years and 34% were male. Among A02A, A02B and M01A drug classes, 12 rules of associations were discovered with support and confidence thresholds of 1% and 50%. The highest lift was between Omeprazole and Ranitidine (340 visits); about one-third of these visits (118) were prescriptions to non-GORD patients, contrary to guidelines. Another finding was the concomitant use of COX-2 inhibitors (Etoricoxib or Celecoxib) and PPIs. 35.6% of these were for patients aged less than 60 years with no GI complication and no Aspirin, inconsistent with guidelines.ConclusionsAround one-third of occasions where these medications were co-prescribed were inconsistent with guidelines. With the rapid growth of health datasets, data mining methods may help assess quality of care and concordance with guidelines and best evidence.

Batch Picking Method and Its Application in Library Returning Service

Arranging books on the shelves is a key part of the library returning service. The batch picking method for the operations of logistics distribution centers is used in arranging books in the stacks based on considering the characteristics of library returning service. The returned books in a fixed time interval are collected as an order. The batch picking method is proposed based on association rule data mining and 0-1 integer programming. The associations between orders are mined by the apriori algorithm. Orders are grouped into batches by establishing and solving 0-1 integer programming to maximize the support value between orders. This model is applied in an academic library and application results show the effectiveness of the proposed method.

Analysis of Blast Furnace Data Based on Association Rules

In this paper, taking the blast furnace temperature data as the data source, using association rules in data mining. Firstly, the practical significance of temperature data analysis of blast furnace in a steel plant is introduced. Secondly, the theory of data mining and association rules is given, detailed description of the principle of Apriori algorithm. This paper selects the temperature data of B blast furnace in a steel plant in September 2015 as the research object. Association Rule Mining for temperature data of blast furnace in a steel plant, and the association results are analyzed.

Concept of Association Rule of Data Mining Assists Mitigating the Increasing Obesity

Association rule of data mining is known to encompass a wide set of intelligent techniques that intent to unveil and analyze correlations and associations between items in a set. Market basket analysis is one such, possibly the most popular technique in business domain that is used to analyze combinations of items that often are listed together in various transactions. In this paper, the author strives to expand applicability of the same concept to human health under purview of health informatics. The present growing rate of obesity has raised alarming concept to the communities globally. It entails several chronic diseases that may be fatal eventually. This work aims to aid in the ongoing efforts to alleviate the obesity, primarily caused by lack of physical exercise. Concept of association rule of data mining may help regulating mild exercise by associating it with a daily activity, sleeping at night. Mild but regular short exercise just before sleep may help ameliorating individual's health.

Survey on Privacy Preserving Association Rule Data Mining

The progress in the development of data mining techniques achieved in the recent years is gigantic. The collative data mining techniques makes the privacy preserving an important issue. The ultimate aim of the privacy preserving data mining is to extract relevant information from large amount of data base while protecting the sensitive information. The togetherness in the information retrieval with privacy and data quality is crucial. A detailed survey of the present methodologies for the association rule data mining and a review of the state of art method for privacy preserving association rule mining is presented in this paper. An analysis is provided based on the association rule mining algorithm techniques, objective measures, performance metrics and results achieved. The metrics and the short comings of the various existing technologies are also analysed. Finally, the authors present various research issues which can be useful for the researchers to accomplish further research on the privacy preserving association rule data mining.

Analysing the influence of African dust storms on the prevalence of coral disease in the Caribbean Sea using remote sensing and association rule data mining

ABSTRACTThe application of an association rule data mining algorithm is described to combine remote sensing and in-situ geophysical data to show a relationship between African dust storms, Caribbean climate, and Caribbean coral disease. An analysis is performed to quantify the relative statistical significance of each Caribbean climate parameter on the prevalence of coral disease. Results show that African dust storms contribute to an increased prevalence of coral disease in the Caribbean Sea, and that the correlation between them is influenced by other climate parameters, especially sea surface temperature.

基于SOM网络及关联规则的湖北省城市空气质量研究

随着经济发展及人口增多，我们居住的城市陆续发生了各种环境问题。利用自组织竞争(SOM)神经网络研究湖北省城市环境空气质量，以PM2.5、PM10、SO2、CO、NO2、O3六个主要空气污染物作为指标，建立SOM网络模型，结果显示在空间分布上湖北省城市空气质量呈现从外围到中心降低的特征。采用数据挖掘中的关联规则，利用经典的Apriori算法挖掘主要空气污染物之间的关联关系，得到PM2.5、NO2、O3之间的强关联规则。 With the development of economy and the increase of population, all kinds of environmental problems have taken place in our city. Firstly, taking PM2.5, PM10, SO2, CO, NO2, and O3 the six major air pollutants as indicators, self-organizing neural (SOM) network is used to study the urban air quality in Hubei province. The SOM neural network clustering model is established, and the results show that the air quality in Hubei province is reduced from the periphery to the center. Secondly, this paper uses the association rules in data mining and the classical Apriori algorithm to mine the correlation between PM2.5, NO2, and O3 the three major air pollutants, the strong association rules are found finally.

Smart frequent itemsets mining algorithm based on FP-tree and DIFFset data structures

Association rule data mining is an important technique for finding important relationships in large datasets. Several frequent itemsets mining techniques have been proposed using a prefix-tree structure, FP-tree, a compressed data structure for database representation. The DIFFset data structure has also been shown to significantly reduce the run time and memory utilization of some data mining algorithms. Experimental results have demonstrated the efficiency of the two data structures in frequent itemsets mining. This work proposes FDM, a new algorithm based on FP-tree and DIFFset data structures for efficiently discovering frequent patterns in data. FDM can adapt its characteristics to efficiently mine long and short patterns from both dense and sparse datasets. Several optimization techniques are also outlined to increase the efficiency of FDM. An evaluation of FDM against three frequent itemset data mining algorithms, dEclat, FP-growth, and FDM* (FDM without optimization), was performed using datasets having both long and short frequent patterns. The experimental results show significant improvement in performance compared to the FP-growth, dEclat, and FDM* algorithms.

Data Mining Application for Finding Patterns: Survey of Large Data Research Tools

Data Mining is now a common method for mining data from databases and finding out patterns from the data. Today many organizations are using data mining techniques. In this paper concepts and techniques such as Neural Network, Decision Tree, Clustering, Association Rule, Clustering and many more techniques of Data Mining is reviewed. This paper focuses how different techniques of Data Mining are used in different applications for finding out patterns from the data taken from the data base.

Efficient attribute selection strategies for association rule mining in high dimensional data

This paper presents a new computational approach to discover interesting relations between variables, called association rules, in large and high dimensional datasets. State-of-the-art techniques are computationally expensive due to reasons like high dimensions, generation of huge number of candidate sets and multiple database scans. In general, most of the enormous discovered patterns are obvious, redundant or uninteresting to the user. So the context of this paper is to improve apriori algorithm to find association rules pertaining to only important attributes from high dimensional data. We employ an information theoretic method together with the concept of QR decomposition to represent the data in its proper substructure form without losing its semantics, by identifying significant attributes. Experiment on real datasets and comparison with the existing technique reveals that the proposed strategy is computationally always faster and statistically always comparable with the apriori algorithms in terms of rules generated and time complexity.

Efficient attribute selection strategies for association rule mining in high dimensional data

Efficient attribute selection strategies for association rule mining in high dimensional data

An application of data mining in district heating substations for improving energy performance

Automatic meter reading system is capable of collecting and storing a huge number of district heating (DH) data. However, the data obtained are rarely fully utilized. Data mining is a promising technology to discover potential interesting knowledge from vast data. This paper applies data mining methods to analyse the massive data for improving energy performance of DH substation. The technical approach contains three steps: data selection, cluster analysis and association rule mining (ARM). Two-heating-season data of a substation are used for case study. Cluster analysis identifies six distinct heating patterns based on the primary heat of the substation. ARM reveals that secondary pressure difference and secondary flow rate have a strong correlation. Using the discovered rules, a fault occurring in remote flow meter installed at secondary network is detected accurately. The application demonstrates that data mining techniques can effectively extrapolate potential useful knowledge to better understand substation operation strategies and improve substation energy performance.

Prediction of Tea Production in Kenya Using Clustering and Association Rule Mining Techniques

As at present, the agricultural sector is the backbone of the Kenyan economy. Though there has been a significant focus on other emerging industries, the agriculture sector remains to be a crucial player in the Kenyan economy, and which vastly contributes in the provision of job opportunities for millions of Kenyan citizens as well as strengthening the Gross Domestic Product (GDP). Therefore, efforts towards strengthening this sector are highly and warranted. Mining the past agricultural data to establish any new knowledge is, hence of great essence. Knowledge discovery is a crucial component of the modern day decision making. In the agricultural sector, the knowledge gained from the past data can be used for various beneficial purposes, including planning, budgeting a forecasting the possible future production trends. This paper attempts to predict tea production in Kenya through step-wise use of the clustering and association rule data mining techniques. A conclusion is presented, based on the presented arguments.