Analysis of Association Rule in Fire Accidents - Evidence from Taipei City

2014 ◽  
Vol 1030-1032 ◽  
pp. 2407-2410
Author(s):  
Yu Ying Huang ◽  
Yeng Horng Perng
Keyword(s):  
Association Rule ◽  
Fire Hazard ◽  
Data Mining Technique ◽  
Mining Technique ◽  
Taipei City ◽  
Potential Factors ◽  
Fire Accidents ◽  
In Fire ◽  
Financial Losses ◽  
Fire Accident

Fire accidents may induce serious human casualties, building damages, and financial losses. The association rule of data mining technique was applied in this research to identify the characteristics, potential factors, and hidden relationships, that were associated with each other in the building fires of Taipei during 2007-2010. The results of this paper are insightful and can be used to assist fire officials and citizens in reducing the chance of a fire accident, as well as, in mitigating the damages of a fire hazard.

Research on Teaching Quality Evaluation Using Data Mining Technique

2014 ◽  
Vol 926-930 ◽  
pp. 4582-4585
Author(s):  
Ai Feng Li ◽  
Ying Hu ◽  
Wen Jing Zhao
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Association Rule ◽  
Quality Evaluation ◽  
Teaching Quality ◽  
Data Mining Technique ◽  
Higher Education System ◽  
Potential Factors ◽  
Rule Method ◽  
Using Data

—In this paper, we employ data mining (DM) technique to analyze various potential factors which impact the in-class teaching quality evaluation. Based on an effective dataset, we first exploit association rule method to mine the relationship between the teacher’s attributions, such as title, degree, age, seniority, and load, and the in-class teaching quality evaluation results. Then, we construct the decision tree of course’s attributions to reveal how the course’s attributions, such as property, credit, week hour, and number of students, impact the in-class teaching quality evaluation results. Our mined rules can provide effective guidance to talent development, teaching management, and input of talent in higher education system. Index Terms—data mining, decision tree, association rule, teaching quality evaluation

scholarly journals Application of Data Mining Technique in Stock Market : An Analysis

2014 ◽  
pp. 294-295
Author(s):  
SACHIN KAMBEY ◽  
R. S. THAKUR ◽  
SHAILESH JALORI
Keyword(s):  
Data Mining ◽  
Stock Market ◽  
Association Rule ◽  
Movement Direction ◽  
Data Mining Technique ◽  
Business World ◽  
Mining Technique ◽  
Hidden Patterns ◽  
Technology Tool ◽  
And Behavior

Stock market prediction with data mining technique is one of the most important issues to be investigated and it is one of the fascinating issues of stock market research over the past decade. Many attempts have been made to predict stock market data using statistical and traditional methods, but these methods are no longer adequate for analyzing this huge amount of data. Data mining is one of most important powerful information technology tool in today’s competitive business world, it is able to uncover hidden patterns and predict future trends and behavior in stock market. This paper also highlights the application of association rule in stock market and their future movement direction.

From data to knowledge mining

2009 ◽  
Vol 23 (4) ◽  
pp. 427-441 ◽  
Author(s):  
Ana Cristina Bicharra Garcia ◽  
Inhauma Ferraz ◽  
Adriana S. Vivacqua
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
Evaluation Criteria ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Data Mining Technique ◽  
Mining Technique ◽  
Data Points

AbstractMost past approaches to data mining have been based on association rules. However, the simple application of association rules usually only changes the user's problem from dealing with millions of data points to dealing with thousands of rules. Although this may somewhat reduce the scale of the problem, it is not a completely satisfactory solution. This paper presents a new data mining technique, called knowledge cohesion (KC), which takes into account a domain ontology and the user's interest in exploring certain data sets to extract knowledge, in the form of semantic nets, from large data sets. The KC method has been successfully applied to mine causal relations from oil platform accident reports. In a comparison with association rule techniques for the same domain, KC has shown a significant improvement in the extraction of relevant knowledge, using processing complexity and knowledge manageability as the evaluation criteria.

Formalizing and Exploring the Transferability of Inclusive Design Rules

2013 ◽  
Vol 135 (9) ◽  
Author(s):  
Shraddha Sangelkar ◽  
Daniel A. McAdams
Keyword(s):  
Association Rule ◽  
Rule Learning ◽  
Inclusive Design ◽  
Large Set ◽  
Data Mining Technique ◽  
Product Families ◽  
Physical Similarity ◽  
Mining Technique ◽  
Finite Set ◽  
Product Domains

Inclusive products intend to equally serve people with and without a disability. This paper focuses on creating guidelines that are applicable during the early stages of designing inclusive products. Actionfunction diagrams are used to formally compare existing inclusive products to their typical counterparts to study the design similarities and differences in the context of accessibility. A data mining technique, association rule learning, generates rules through comparison of inclusive and typical product data. In prior work, generation of function-based association rules for inclusive design has been performed on a smaller scale using this method; this research seeks to extend and formalize the same method, by studying a larger set of inclusive products. Trends in the generation of rules are analyzed indicating that a finite set of rules should be applicable to an arbitrarily large set of products. Further, the rules are analyzed in detail to evaluate their potential for transferability and reuse from one product to another. Of particular interest is the transferability of the rules across apparently disparate product domains such as garden tools and residential furniture. The conceptual and physical similarity of the rules is discussed in the context of creating inclusive product families based on a platform of inclusive elements.

scholarly journals Association Rule Data Mining Applications for Atlantic Tropical Cyclone Intensity Changes

2011 ◽  
Vol 26 (3) ◽  
pp. 337-353 ◽  
Author(s):  
Ruixin Yang ◽  
Jiang Tang ◽  
Donglian Sun
Keyword(s):  
Data Mining ◽  
Tropical Cyclone ◽  
Association Rule ◽  
Tropical Storms ◽  
Rapid Intensification ◽  
Data Mining Technique ◽  
Mining Technique ◽  
Intensity Prediction ◽  
Intensity Changes ◽  
Tropical Depressions

Abstract This study applies a data mining technique called association rule mining to the analysis of intensity changes of North Atlantic tropical cyclones (TCs). The “best track” data from the National Hurricane Center and the Statistical Hurricane Intensity Prediction Scheme databases were stratified into tropical depressions, tropical storms, and category 1–5 hurricanes based on the Saffir–Simpson hurricane scale. After stratification, the seven resulting groups of TCs plus two additional aggregation groups were further separated into intensifying, weakening, and stable TCs. The analysis of the stratified data for preprocessing revealed that faster northward storm motion (the meridional component of storm motion) favors tropical storm intensification but does not favor the intensification of hurricanes. Intensifying tropical storms are more strongly associated with a higher convergence in the upper atmosphere (200-hPa relative eddy momentum flux convergence) than weakening tropical storms, while intensifying hurricanes are more strongly associated with lower convergence values. The mined association rules showed that cofactors usually display higher-intensity prediction power in the stratified TC groups. The data mining results also identified a predictor set with fewer factors but improved probabilities of rapid intensification. This study found that the data mining technique not only sheds light on the roles of multiple-associated physical processes in tropical cyclone development—especially in rapid intensification processes—but also will help improve TC intensity forecasting. This paper provides an outline on how to use this data mining technique and how to overcome low occurrences of mined conditions in order to improve TC intensity forecasting capabilities.

scholarly journals Prediction of By-Diseases in Diabetic Patients Using Associative Classification with Improved Classifier Accuracy for Decision Support System

2019 ◽  
Vol 8 (6) ◽  
pp. 2625-2628
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Diabetic Patients ◽  
Classification Algorithms ◽  
Data Sets ◽  
Classification Models ◽  
Associative Classification ◽  
Rule Mining ◽  
Data Mining Technique ◽  
Mining Technique

Associative Classification in data mining technique formulates more and more simple methods and processes to find and predict the health problems like diabetes, tumors, heart problems, thyroid, cancer, malaria etc. The methods of classification combined with association rule mining gradually helps to predict large amount of data and also builds the accurate classification models for the future analysis. The data in medical area is sometimes vast and containss the information that relates to different diseases. It becomes difficult to estimate and analyze the disease problems that change from period to period based on severity. In this research paper, the use and need of associative classification for the medical data sets and the application of associative classification on the data in order to predict the by-diseases has been put front. The association rules in this context developed in training phase of data have predicted the chance of occurrence of other diseases in persons suffering with diabetes mellitus using Predictive Apriori. The associative classification algorithms like CAR is deployed in the context of accuracy measures.

scholarly journals Penerapan Data Mining Association Rule Menggunakan Algoritma FP-Growth Untuk Persediaan Sparepart Pada Bengkel

2021 ◽  
Vol 5 (2) ◽  
pp. 112-121
Author(s):  
Guntoro Guntoro ◽  
Charles Parmonangan Hutabarat
Keyword(s):  
Data Mining ◽  
Data Processing ◽  
Association Rule ◽  
Data Gathering ◽  
Engine Oil ◽  
Data Mining Technique ◽  
Mining Technique ◽  
Spark Plug ◽  
Gathering Data ◽  
Mining Association Rule

Many individuals are interested in starting a workshop. By responding to each customer's desires, the workshop company may continue to develop, and so the data mining technique can address this challenge. The FP-Growth algorithm is one of the methods that may be used to determine the stock availability of automotive spare components such as engine oil, spark plugs, oil filters, ac filters, batteries, tires, and so on. This research is divided into four stages: problem identification, data gathering, data processing, and data testing. Based on the results of the testing, AK (Battery), OM (Engine Oil), and BS (Spark plug) received support values of 33% and 80%, respectively. Furthermore, the BN (Ban) and KR (Kampas Bram) values were found with 33% support and 80% confidence. Furthermore, we obtain AK (Battery) and OM (Engine Oil) with 33% support and 80% confidence, and BN (Tires) and OM (Engine Oil) with 33% support and 80% confidence. OM (Engineering Oil), AK (Battery), and BS (Battery Storage) are the abbreviations for the terms OM (Engineering Oil), AK (Battery), and BS (Battery (Spark plug)).

Numerical Study on the Fire Development Process of Typical Power Plant Multi-Floor Compartment

2014 ◽  
Vol 666 ◽  
pp. 354-358
Author(s):  
Xin Ma ◽  
Wei He ◽  
Chao Ding ◽  
Yong Jiang
Keyword(s):  
Power Plant ◽  
Numerical Study ◽  
Radiant Heat ◽  
Power Stations ◽  
Single Room ◽  
Modeling Software ◽  
Burning Rates ◽  
Fire Accidents ◽  
In Fire ◽  
Fire Accident

About frequently happened fire accidents in Power stations, an actual compartment fire accident in one room on 3nd floor was studied numerically with software FDS 5.3.0/SMV 5.3.10 in this article. According to the results of simulation, we investigated the different radiant heat flux parameters, burning losses under different heat release rates and rising rates. The developing process of fuel combustion was analyzed. Also, we compared the simulation results with the actual smoke trace and burning rates in fire accidents. It shows the numerical modeling software FDS is a reliable tool to analyze the power plant fire accident caused by casually placed inflammable objects in single room. It provides an applied valuable good way for the fire accident research in power plant.

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