Data Mining Association Rules Research Articles

Understanding occupancy pattern and improving building energy efficiency through Wi-Fi based indoor positioning

Detailed visualisation and data analysis of occupancy patterns including spatial distribution and temporal variations are of great importance to delivering energy efficient and productive buildings. An experimental study comprising 24-h monitoring over 30 full days was conducted in a university library building. Occupancy profiles have been monitored and analysis has been carried out. Central to this monitoring study is the Wi-Fi based indoor positioning system based on the measured Wi-Fi devices' number and locations and data mining methods. Distinct from traditional occupancy and energy studies, more detailed information related to the indoor positions and number of occupants has offered a better understanding of building user behaviour. The implication of the occupancy patterns for energy (e.g. lighting and other building services) efficiency is assessed, assisted with data from lighting sensors where needed. It is found occupancy patterns change dramatically with time. Also, the energy waste patterns have been identified through the method of data association rule mining. If the identified energy waste is removed, the total energy consumption can be reduced by 26.1%. The indoor positioning information also has implications for optimizing space use, opening hours as well as staff deployment. The work could be extended to more rooms with diverse functions, other seasons and other types of non-domestic buildings for a more comprehensive understanding of building user behaviour and energy efficiency.

Data partitioning and association mining for identifying VRF energy consumption patterns under various part loads and refrigerant charge conditions

Variable refrigerant flow systems account for a considerable portion of energy consumption in buildings. In order to improve the energy efficiency and estimate the energy saving potentials of variable refrigerant flow systems this study proposes a data mining based method to identify and interpret the power consumption patterns and associations. Two descriptive data mining algorithms, clustering analysis and association rules mining, are used for data partitioning and association mining. The proposed method consists of four phases: data pre-processing, data partitioning, data association mining and knowledge interpretation. Experimental data collected from a tested variable refrigerant flow system in the standard psychrometer testing room are pre-processed and prepared to examine the proposed method. Three time independent influential factors: part load ratio, refrigerant charge level and cooling condition are analyzed. Results show that the method is able to help identify energy consumption patterns and extract energy consumption rules in variable refrigerant flow systems. Three distinct energy consumption patterns are identified: undercharge fault, low and high part load ratio conditions. For compressor operation frequency switch control and refrigerant undercharge patterns, the energy saving potentials could be estimated by making comparisons between energy patterns and rules in a top-down way.

Occurrence of emergencies and disaster analysis according to precipitation amount

An analysis of the occurrences of events related to precipitation, considering extensive and intensive risk, i.e., emergencies and disasters, based on twenty-nine years of data for five cities of Ecuador provided relevant information about the behavior over time of floods, river overflows and landslides. The records of events were examined in the immediate and in the short term, which corresponded to 5 and 30 days, respectively, using the data mining methods k-means and association rules, to identify the patterns that govern their behaviors with respect to the observed amount of precipitation. The results show an increase in the frequency of similar events, with the occurrences being separated by shorter periods in recent decades. The behavior of emergencies and disasters indicates that emergencies are expected for periods of 5 days, with low quantities of precipitation and for periods of 30 days with normal quantities of precipitation. Disasters are expected, for both periods of 5 and 30 days, in the higher quantiles of precipitation. Interrelations between floods, river overflows and landslides were identified in all cities, with at least one relationship between two of the hazards for each city. An apparent flood–river overflow–landslide cycle could explain the mechanics of their occurrence. The information provided by the results indicates the vulnerability of the cities over time, their low capacity to support normal quantities of precipitation and their high exposure to hydro-meteorological hazards. The products obtained could be used together with precipitation prediction to anticipate possible effects and to formulate adequate risk management policies.

Data mining techniques on satellite images for discovery of risk areas

The high rates of cholera epidemic mortality in less developed countries is a challenge for health facilities to which it is necessary to equip itself with the epidemiological surveillance. To strengthen the capacity of epidemiological surveillance, this paper focuses on remote sensing satellite data processing using data mining methods to discover risk areas of the epidemic disease by connecting the environment, climate and health. These satellite data are combined with field data collected during the same set of periods in order to explain and deduct the causes of the epidemic evolution from one period to another in relation to the environment. The existing technical (algorithms) for processing satellite images are mature and efficient, so the challenge today is to provide the most suitable means allowing the best interpretation of obtained results. For that, we focus on supervised classification algorithm to process a set of satellite images from the same area but on different periods. A novel research methodology (describing pre-treatment, data mining, and post-treatment) is proposed to ensure suitable means for transforming data, generating information and extracting knowledge. This methodology consists of six phases: (1.A) Acquisition of information from the field about epidemic, (1.B) Satellite data acquisition, (2) Selection and transformation of data (Data derived from images), (3) Remote sensing measurements, (4) Discretization of data, (5) Data treatment, and (6) Interpretation of results. The main contributions of the paper are: to establish the nature of links between the environment and the epidemic, and to highlight those risky environments when the public awareness of the problem and the prevention policies are absolutely necessary for mitigation of the propagation and emergence of the epidemic. This will allow national governments, local authorities and the public health officials to effective management according to risk areas. The case study concerns the knowledge discovery in databases related to risk areas of the cholera epidemic in Mopti region, Mali (West Africa). The results generate from data mining association rules indicate that the level of the Niger River in the wintering periods and some societal factors have an impact on the variation of cholera epidemic rate in Mopti town. More the river level is high, at 66% the rate of contamination is high.

Prediction And Recommendations On The It Leaners' Learning Path As A Collective Intelligence Using A Data Mining Technique

With the recent advances in computer technology along with pervasive internet accesses, data analytics is getting more attention than ever before. In addition, research areas on data analysis are diverging and integrating lots of different fields such as a business and social sector. Especially, recent researches focus on the data analysis for a better intelligent decision making and prediction system. This paper analyzes data collected from current IT learners who have already studied various IT subjects to find the IT learners’ learning patterns. The most popular learning patterns are identified through an association rule data mining using an arules package running under R studio. Experimental results are used to recommend the IT learning path to rudimentary IT learners. It is expected that our research promotes IT learning field and results in a platform of IT learning helpful to IT learners.

Improvement and Simulation of Data Mining Algorithm Based on Association Rules

As a highly concerned research topic in the field of data mining, association rules have achieved good results at present. However, in practical application, the expanding of database scale coupled with the increasing of the amount of data leads to the decrease of efficiency in algorithm mining, which limits its own application. The paper modifies the data mining algorithm based on association rules and explores the simulation of this algorithm, intending to provide some reference for the improvement and simulation of association rules data mining algorithm.

Fault Analysis for Power Network Equipment Based On Association Rule Clustering Algorithm

In order to improve the fault positioning accuracy of distributed generation power distribution network, it puts forward fault positioning method of distribution network equipment based on blended data association rule data mining method. Based on fuzzy rough set theory, it studies classification rule mining method on hybrid data, through the introduction of the rule to obtain the a generalized threshold of the algorithm, and control the scale and complexity of the obtained rule set, so as to improve the classification efficiency of rough set method of knowledge discovery on the failure data and get the fault positioning feature of distribution network, finally adopts the support vector machine (SVM) to make fault classification, and tests the performance of the algorithm with simulation experiment. The simulation results show that, this paper can be quickly and accurately to locate fault power section, and the fault positioning accuracy is higher than other fault positioning methods of distribution network.

Association Rules Hiding for Privacy Preserving Data Mining: A Survey (Under Review Process)

(PPDM) privacy preserving data mining is recent advanced research in (DM) data mining field; Many efficient and practical techniques have been proposed for hiding sensitive patterns or information from been discovered by (DM) data mining algorithms. (ARM) Association rule mining is the most important tool in (DM) data mining, that is considered a powerful and interested tool for discovering relationships between items, which are hidden in large database and may provide business competitors with an advantage, thus the hiding of association rules is the most important point in (PPDM) privacy preserving data mining for protecting sensitive and crucial data against unauthorized access; Many Practical techniques and approaches have been proposed for hiding association rules for (PPDM) privacy preserving data mining; In this paper we have summarized the current existing techniques and algorithms for all approaches for (ARH) association rule hiding.

Research of Association Rules Algorithm in Data Mining

With the rapid development of information technology, the data is becoming more and more large, the data mining technology has been applied to many fields in the life. In view of the existing data mining algorithms complexity is too high, the execution time too long, the article applies association rules in data mining technology, analysis summarized the existing advantages and disadvantages of the classic data mining association rules algorithm, is proposed for mining maximum frequent itemsets MFP - Miner algorithm. At last, by experimental simulation result shows that the proposed algorithm performance superiority, its complexity and execution time significantly shortened, the practical guiding significance.

PRM170 - Application Of Association Rules In Clinical Data Mining: A Case Study For Identifying Adverse Drug Reactions

PRM170 - Application Of Association Rules In Clinical Data Mining: A Case Study For Identifying Adverse Drug Reactions

Edifice an Educational Framework using Educational Data Mining and Visual Analytics

Educational Data Mining and Visual analytics are two emerging trends in the industry that plays a major role in bringing out changes in the educational institutions. This paper discusses about building an educational framework that suits the higher education in India using the above mentioned technologies. Educational data mining comprises of various technologies and tasks which can applied on educational data to bring out useful information. In this research work, a data ware house is built to store the student data, two data mining tasks classification and association rule mining are applied over the student data set to analyse their performance in the examination. Decision tree algorithm is used to predict the course and program outcome. Association mining is used to analyze the outcome and understand technical capability of the students. The algorithms were found very accurate in predicting and analyzing the performance. Visual analytics is used in the framework to depict the analysis of the student’s performance.

Another Tale of Two Cities: Understanding Human Activity Space Using Actively Tracked Cellphone Location Data

Activity space is an important concept in geography. Recent advancements of location-aware technologies have generated many useful spatiotemporal data sets for studying human activity space for large populations. In this article, we use two actively tracked cellphone location data sets that cover a weekday to characterize people's use of space in Shanghai and Shenzhen, China. We introduce three mobility indicators (daily activity range, number of activity anchor points, and frequency of movements) to represent the major determinants of individual activity space. By applying association rules in data mining, we analyze how these indicators of an individual's activity space can be combined with each other to gain insights of mobility patterns in these two cities. We further examine spatiotemporal variations of aggregate mobility patterns in these two cities. Our results reveal some distinctive characteristics of human activity space in these two cities: (1) A high percentage of people in Shenzhen have a relatively short daily activity range, whereas people in Shanghai exhibit a variety of daily activity ranges; (2) people with more than one activity anchor point tend to travel further but less frequently in Shanghai than in Shenzhen; (3) Shenzhen shows a significant north–south contrast of activity space that reflects its urban structure; and (4) travel distance in both cities is shorter around noon than in regular work hours, and a large percentage of movements around noon are associated with individual home locations. This study indicates the benefits of analyzing actively tracked cellphone location data for gaining insights of human activity space in different cities.

Apriori算法在入侵检测系统中的改进

随着互联网的发展，网络安全形势日趋严峻，网络安全问题频发，这些安全问题出现在各行各业，造成的危害越发严重。而对于每天面临大量数据量的公司而言，如何在众多数据中检测出入侵病毒是个很大的挑战。用于数据挖掘的关联规则在分布式计算中发挥重要的作用，它们同样也适用于对病毒的入侵检测，本文将对数据挖掘中关联规则产生的Apriori算法在入侵检测系统下进行改进，使得它适用于对病毒的入侵检测。 With the development of the Internet, the network security situation is becoming increasingly grim. Network security problems occur frequently. These security problems appear in all walks of life. The harm caused is more serious. And for companies who face a lot of data every day, how to detect a large number of data in the virus is a big challenge. The association rules in data mining play an important role in distributed computing, which is also suitable for the invasion of virus detection. This Apriori algorithm to association rules in data mining in intrusion detection system is improved, which makes it applicable to the invasion of virus detection.

Case Study: Text-Mining Customers View Point and Perceived Value About Brand

paper describes how text mining techniques can be applied in the analysis of consumer voice to gain useful and actionable business insights for marketers. The technique is illustrated via its application to understand Brands perceived value of certain automobile brands. This case study shows the use of text mining techniques to understand brands perception vis-a-vis competition from their opinion, sentiment and reactions. As the amount of online text increases, the demand for text classification to aid the analysis and management of text is increasing. Data acquisition in this case is not costly, information is rich in nature, classification of text can provide this information at low cost, but the classifiers themselves must be built with expensive human effort, or trained from texts which have themselves been manually classified. In this paper, we mention about a procedure of classifying text using the concept of association rule of data mining and correspondence analysis for Brand perception.

A Novel Mapreduce Lift Association Rule Mining Algorithm (Mrlar) for Big Data

Big Data mining is an analytic process used to discover the hidden knowledge and patterns from a massive, complex, and multi-dimensional dataset. Single-processor's memory and CPU resources are very limited, which makes the algorithm performance ineffective. Recently, there has been renewed interest in using association rule mining (ARM) in Big Data to uncover relationships between what seems to be unrelated. However, the traditional discovery ARM techniques are unable to handle this huge amount of data. Therefore, there is a vital need to scalable and parallel strategies for ARM based on Big Data approaches. This paper develops a novel MapReduce framework for an association rule algorithm based on Lift interestingness measurement (MRLAR) which can handle massive datasets with a large number of nodes. The experimental result shows the effi-ciency of the proposed algorithm to measure the correlations between itemsets through integrating the uses of MapReduce and LIM instead of depending on confidence.

Applying data mining techniques for spatial distribution analysis of plant species co-occurrences

The continuous growth of biodiversity databases has led to a search for techniques that can assist researchers. This paper presents a method for the analysis of occurrences of pairs and groups of species that aims to identify patterns in co-occurrences through the application of association rules of data mining. We propose, implement and evaluate a tool to help ecologists formulate and validate hypotheses regarding co-occurrence between two or more species. To validate our approach, we analyzed the occurrence of species with a dataset from the 50-ha Forest Dynamics Project on Barro Colorado Island (BCI). Three case studies were developed based on this tropical forest to evaluate patterns of positive and negative correlation. Our tool can be used to point co-occurrence in a multi-scale form and for multi-species, simultaneously, accelerating the identification process for the Spatial Point Pattern Analysis. This paper demonstrates that data mining, which has been used successfully in applications such as business and consumer profile analysis, can be a useful resource in ecology.

Improving Intrusion Detection System Based on Snort Rules for Network Probe Attacks Detection with Association Rules Technique of Data Mining

The intrusion detection system (IDS) is an important network security tool for securing computer and network systems. It is able to detect and monitor network traffic data. Snort IDS is an open-source network security tool. It can search and match rules with network traffic data in order to detect attacks, and generate an alert. However, the Snort IDS Â can detect only known attacks. Therefore, we have proposed a procedure for improving Snort IDS rules, based on the association rules data mining technique for detection of network probe attacks. Â We employed the MIT-DARPA 1999 data set for the experimental evaluation. Since behavior pattern traffic data are both normal and abnormal, the abnormal behavior data is detected by way of the Snort IDS. The experimental results showed that the proposed Snort IDS rules, based on data mining detection of network probe attacks, proved more efficient than the original Snort IDS rules, as well as icmp.rules and icmp-info.rules of Snort IDS. Â The suitable parameters for the proposed Snort IDS rules are defined as follows: Min_sup set to 10%, and Min_conf set to 100%, and through the application of eight variable attributes. As more suitable parameters are applied, higher accuracy is achieved.

Clinic + - A Clinical Decision Support SystemUsing Association Rule Mining

A Clinical Decision Support System(CDSS) assist practitioners and healthcare providers in decision making through timely access to electronically stored medical knowledge. A CDSS interacts with practitioners and electronic medical record systems to receive the patient data as input and provides reminders, alerts, or recommendations for patient diagnosis, treatment and long-term care planning. A review on this clinical decision support system , have observed an ICU clinical decision support system based on associate rule mining (ARM), and a publicly available research database MIMIC-II (Multi-parameter Intelligent Monitoring in Intensive Care II) .System is focused on developing clinical decision support system considering both clinical and physiological data using association rule data mining algorithm and clustering algorithm.

Improvements in Data Mining Association Rules Algorithm

Improvements in Data Mining Association Rules Algorithm

Hydraulic metal structure health diagnosis based on data mining technology

In conjunction with association rules for data mining, the connections between testing indices and strong and weak association rules were determined, and new derivative rules were obtained by further reasoning. Association rules were used to analyze correlation and check consistency between indices. This study shows that the judgment obtained by weak association rules or non-association rules is more accurate and more credible than that obtained by strong association rules. When the testing grades of two indices in the weak association rules are inconsistent, the testing grades of indices are more likely to be erroneous, and the mistakes are often caused by human factors. Clustering data mining technology was used to analyze the reliability of a diagnosis, or to perform health diagnosis directly. Analysis showed that the clustering results are related to the indices selected, and that if the indices selected are more significant, the characteristics of clustering results are also more significant, and the analysis or diagnosis is more credible. The indices and diagnosis analysis function produced by this study provide a necessary theoretical foundation and new ideas for the development of hydraulic metal structure health diagnosis technology.