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A new and optimal architecture for applying association rule mining algorithms on grid
, Article 2007 IADIS European Conference on Data Mining, DM 2007, part of the 1st IADIS Multi Conference on Computer Science and Information Systems, MCCSIS 2007, 3 July 2007 through 8 July 2007 ; 2020 , Pages 230-232 ; Arasteh, B ; Karimi, M. B ; Hoseyni, M. J ; Bouyer, A ; Movaghar, A ; Sharif University of Technology
IADIS Press
2020
Abstract
Grid computing is the on-demand sharing of computing resources with in a tightly-coupled network to solve certain problems. One of the main topics is knowledge discovery and data mining. By using grid computing, we can solve this problem. To achieve these very ambitious goals, we present an architecture for applying Association rule mining algorithms on grid environment. Association rule mining seeks to discover associations among transactions encoded in a database on each machine and then send result to related coordinator. This new architecture is powerful and rapid. We tried to compare our method with a serial processing. Our experimental results show that by using the new architecture on...
Application of association rule mining in supplier selection criteria
, Article World Academy of Science, Engineering and Technology ; Volume 40 , 2009 , Pages 358-362 ; 2010376X (ISSN) ; Salmasi, N ; Modarres Yazdi, M ; Iranmanesh, H ; Sharif University of Technology
2009
Abstract
In this paper the application of rule mining in order to review the effective factors on supplier selection is reviewed in the following three sections 1) criteria selecting and information gathering 2) performing association rule mining 3) validation and constituting rule base. Afterwards a few of applications of rule base is explained. Then, a numerical example is presented and analyzed by Clementine software. Some of extracted rules as well as the results are presented at the end
Pattern extraction for high-risk accidents in the construction industry: a data-mining approach
, Article International Journal of Injury Control and Safety Promotion ; Volume 23, Issue 3 , 2016 , Pages 264-276 ; 17457300 (ISSN) ; Ardeshir, A ; Fazel Zarandi, M. H ; Soltanaghaei, E ; Sharif University of Technology
Taylor and Francis Ltd
Abstract
Accidents involving falls and falling objects (group I) are highly frequent accidents in the construction industry. While being hit by a vehicle, electric shock, collapse in the excavation and fire or explosion accidents (group II) are much less frequent, they make up a considerable proportion of severe accidents. In this study, multiple-correspondence analysis, decision tree, ensembles of decision tree and association rules methods are employed to analyse a database of construction accidents throughout Iran between 2007 and 2011. The findings indicate that in group I, there is a significant correspondence among these variables: time of accident, place of accident, body part affected, final...
Genetic-PSO fuzzy data mining with divide and conquer strategy
, Article Proceedings of the 2011 International Conference on Artificial Intelligence, ICAI 2011, 18 July 2011 through 21 July 2011 ; Volume 2 , July , 2011 , Pages 725-729 ; 9781601321855 (ISBN) ; Sharif University of Technology
2011
Abstract
Nowadays, discovery the association rules is an important and controversial area in data mining research studies. These rules, describe noticeable association relationships among different attributes. While most studies have focused on binary valued transaction data, in real world applications, there data usually consist of quantitative values. With that in mind, in this paper, we propose a fuzzy data mining algorithm for extracting membership functions from quantitative transactions. This is a hybrid genetic-pso algorithm for finding membership functions suitable for mining problems by a strong cooperation of GA and PSO. This algorithm integrates the two techniques entire run of simulation...
Data quality improvement using fuzzy association rules
, Article ICEIE 2010 - 2010 International Conference on Electronics and Information Engineering, Proceedings, 1 August 2010 through 3 August 2010 ; Volume 1 , August , 2010 , Pages V1468-V1472 ; 9781424476800 (ISBN) ; Pedram, M. M ; Alishahi, M ; Badie, K ; Sharif University of Technology
2010
Abstract
The activities and decisions of organizations and companies are based on data and the information obtained from data analysis. Data quality plays a crucial role in data analysis, because the incorrect data leads to wrong decisions. Nowadays, improving the data quality manually is very difficult and in many cases is impossible as data quality is one of the complicated and non-structured concepts and data refinement process can not be done without the help of professional domain experts, and detection and correction of errors require a thorough knowledge in the related domain of the data. Thus, the necessity of using (semi-)automatic methods is discussed to find data defects and errors and...
Analysis of high risk occupational accidents in construction industry using data-mining methods
, Article Iran Occupational Health ; Vol. 11, Issue 4 , 2014 , pp. 31-43 ; Ardeshir, A ; Aghaie, S. E ; Sharif University of Technology
Abstract
Background and aims: Among different types of occupational accidents in the construction industry, falls and falling objects accidents (group I) account for 44% of construction accidents. Hit by vehicle, electric shock, collapse in the excavation and fire or explosion accidents (group II), while are only 7% frequent, make up about 26% of all fatalities and total disabling accidents. The aim of this study is to investigate these two groups of accidents and to discuss the obtained results in order to identify the potential hazards of construction industry. Methods: Data mining methods are employed to analyze data in this research. Hence, 21864 data records which were provided by Social...
Association Rules Mining in Distributed and Dynamic Databases
, M.Sc. Thesis Sharif University of Technology ; Habibi, Jafar (Supervisor) ; Mirian Hosseinabadi, Hassan (Supervisor)
Abstract
Classical methods of data mining assume that data is centric, is in memory, and is static, although in reality, most of the systems have a lot of data in distributed and dynamic environments or databases. So, classical algorithms of data mining in such environments lose memory and computation resources. In this case, transferring the whole data to a central server and applying the process centrally is inefficient and is subject to privacy issues. Distributed data mining techniques try to address these problems. Mining association rules is one of the important data mining strategies which mines frequent itemsets, correlation, or random structures among itemsets in transactional databases....
Using Data Mining in Production Information Systems
, M.Sc. Thesis Sharif University of Technology ; Hooshmand, Mahmoud (Supervisor)
Abstract
Nowadays, because of high volume and growth of data in industrial organizations and productive factories, registration and storing of data have forgotten manual and tradition styles for which using automation and mechanized machinery and systems has been a necessary task. In order to reach to this revolution, need to some tools, facilities and methods which can fulfill this requirement is felt strongly. Therefore, high volume of data is considered as an advantage because based on precise analysis it is possible to make logical management decisions with less risk. During last years, statistical and numerical methods and simulation were used to discover knowledge and information when one of...
Data Mining Application in Customer Relationship Management: Case study in Saipa Yadak Co.
,
M.Sc. Thesis
Sharif University of Technology
;
Salmasi, Naser
(Supervisor)
Abstract
One of the most applicable fields in data mining is customer relationship management (CRM). CRM process includes four aspects: Customer identification, Customer attraction, Customer retention, and Customer development. Data mining can be a supportive tool for decision making in each of these CRM aspects. Huge volume of data and information corresponding to CRM that exists in companies' databases, has made sufficient potential for data mining process and discovering hidden knowledge. Importance of concepts like customer needs identification, customer retention, and increasing customer value for companies has made the need to use of data mining techniques more valuable. Saipa Yadak Co., as a...
Identifying the Main Factors Affecting Road Accidents in Iran Through Data Mining, Determining the Optimal Solution in Mitigation and Forecasting its Effectiveness Through Arima Models
, M.Sc. Thesis Sharif University of Technology ; Akhavan Niaki, Taghi (Supervisor)
Abstract
Road accidents are unfortunate events that cause more thanl16000 deaths each year in Iran. Intercity accidents require a comprehensive plan to reduce casualties because the number of roads users are increasing and the accidents account for nearlyl65% of fatalities. In this study, we first tried to identify the status of Iran through a study of traffic accidents in the world, and then the research and activities carried out in Iran were analyzed to find new and effective solutions. Using the daily fatalities data froml2008 tol2014, and using the new methodology presented in this research based on the Discrete Fourier Transformation (DFT), the Box-Jenkins models and the Secant method, the...
A new DEA model for ranking association rules considering the risk, resilience and decongestion factors
, Article European Journal of Industrial Engineering ; Volume 15, Issue 4 , May , 2021 , Pages 463-486 ; 17515254 (ISSN) ; Babaei, A ; Sharif University of Technology
Inderscience Publishers
2021
Abstract
In this paper, a novel data envelopment analysis (DEA) model is proposed for ranking the association rules. In this regard, a mixed-integer linear programming (MILP) model is proposed to determine the most efficient association rules where, an N-person bargaining game is used to create an interactive competition between the existing N-weights to get a better ranking. In addition, the proposed model is fuzzified by setting the ambiguous threshold of the indicators’ weight in each rule to improve the overall ranking of the rules. Finally, the risk, resilience and decongestion factors are also considered to increase the responsiveness of the models to different real-world conditions. The...
Privacy Preserving Data Mining
, M.Sc. Thesis Sharif University of Technology ; khazaei, Shahram (Supervisor)
Abstract
Increasing use of new data technologies have made data collection possible in large scales. Practicallity of the data relies upon the extraction of meaningful knowledge.Data mining is a solution to this problem. One of the new areas in data mining is consideration of the concern of privacy alongside the usefulness of the mining results.Main goal of privacy preserving data mining is to develop data mining models which only extract the useful knowledge. In recent years, many researches have been done in this area. Since the literature and notation of these published works vary, a survey would help to better understand these concepts. This thesis tries to explain, analyse,unify and categorize...