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Showing 10 results for Data Mining

, , ,
Volume 20, Issue 1 (5-2009)
Abstract

Fuzzy Cognitive Maps (FCMs) have successfully been applied in numerous domains to show the relations between essential components in complex systems. In this paper, a novel learning method is proposed to construct FCMs based on historical data and by using meta-heuristic: Genetic Algorithm (GA), Simulated Annealing (SA), and Tabu Search (TS). Implementation of the proposed method has demonstrated via real data of a purchase system in order to simulate the system’s behavior.

Volume 21, Issue 3 (9-2010)
Abstract

  One of the most famous methods for recommendation is user-based Collaborative Filtering (CF). This system compares active user’s items rating with historical rating records of other users to find similar users and recommending items which seems interesting to these similar users and have not been rated by the active user. As a way of computing recommendations, the ultimate goal of the user-based collaborative filtering is recommending items with the high accuracy and coverage degree. Nevertheless, some famous limitations are obstacles to meet them. They are Scalability, Sparseness and new item problems. Scalability problem can be handled with the use of Data Mining techniques like clustering. However, use of this technique often leads to the lower recommendation accuracy. Nevertheless, two other problems still remain. Involving Semantic knowledge can increase the performance of recommendation in sparseness and New-Item Problem conditions as well. This paper presents a new approach to deal with the drawbacks of user-based CF systems for web pages recommendation by Combination of Semantic Knowledge with Web Usage Mining (WUM). Semantic knowledge of web pages are extracted and subsequently incorporated into the navigation patterns of each cluster which obtained from clustering the access sessions to get the Semantic Patterns of each cluster. The cluster with the most relevant semantic pattern is chosen with the comparison of semantic representation of the active user session with the semantic patterns and the proper web pages are recommended based on a switching recommendation engine. This engine recommends a list of appropriate recommendations. Results of the implementation of this hybrid web recommender system indicates that this combined approach yields better results in both accuracy and coverage metrics and also has a considerable capability to handle collaborative filtering recommender system for its typical shortcomings .


Saeed Ramezani , Azizollah Memariani,
Volume 22, Issue 2 (6-2011)
Abstract

 

  Condition Monitoring,

  Oil Analysis, Wear Behavior,

  Fuzzy Rule Based System

 

Maintenance , as a support function, plays an important role in manufacturing companies and operational organizations. In this paper, fuzzy rules used to interpret linguistic variables for determination of priorities. Using this approach, such verbal expressions, which cannot be explicitly analyzed or statistically expressed, are herein quantified and used in decision making.

In this research, it is intended to justify the importance of historic data in oil analysis for fault detection. Initial rules derived by decision trees and visualization then these fault diagnosis rules corrected by experts. With the access to decent information sources, the wear behaviors of diesel engines are studied. Also, the relation between the final status of engine and selected features in oil analysis is analyzed. The dissertation and analysis of determining effective features in condition monitoring of equipments and their contribution, is the issue that has been studied through a Data Mining model.
Seyed Omid Hasanpour Jesri, Abbas Ahmadi, Behrooz Karimi, Mohsen Akbarpour ,
Volume 23, Issue 4 (11-2012)
Abstract

One of the most important issues in urban planning is developing sustainable public transportation. The basic condition for this purpose is analyzing current condition especially based on data. Data mining is a set of new techniques that are beyond statistical data analyzing. Clustering techniques is a subset of it that one of it’s techniques used for analyzing passengers’ trip. The result of this research shows relations and similarities in different segments that its usage is from strategic to tactical and operational areas. The approach in transportation is completely novel in the part of trip patterns and a novel process is proposed that can be implemented in highway analysis. Also this method can be applied in traffic and vehicle treats that need automatic number plate recognition (ANPR) for data gathering. A real case study has been studied here by developed process.
Mehdi Alinaghian,
Volume 25, Issue 2 (5-2014)
Abstract

periodic vehicle routing problem focuses on establishing a plan of visits to clients over a given time horizon so as to satisfy some service level while optimizing the routes used in each time period. This paper presents a new effective heuristic algorithm based on data mining tools for periodic vehicle routing problem (PVRP). The related results of proposed algorithm are compared with the results obtained by best Heuristics and meta-heuristics algorithms in the literature. Computational results indicate that the algorithm performs competitive in the accuracy and its small amount of solving time point of views.
Amin Parvaneh, Mohammadjafar Tarokh, Hossein Abbasimehr,
Volume 25, Issue 3 (7-2014)
Abstract

Data mining is a powerful tool for firms to extract knowledge from their customers’ transaction data. One of the useful applications of data mining is segmentation. Segmentation is an effective tool for managers to make right marketing strategies for right customer segments. In this study we have segmented retailers of a hygienic manufacture. Nowadays all manufactures do understand that for staying in the competitive market, they should set up an effective relationship with their retailers. We have proposed a LRFMP (relationship Length, Recency, Frequency, Monetary, and Potential) model for retailer segmentation. Ten retailer clusters have been obtained by applying K-means algorithm with K-optimum according Davies-Bouldin index on LRFMP variables. We have analyzed obtained clusters by weighted sum of LRFMP values, which the weight of each variable calculated by Analytic Hierarchy Process (AHP) technique. In addition we have analyzed each cluster in order to formulate segment-specific marketing actions for retailers. The results of this research can help marketing managers to gain deep insights about retailers.
Mahdieh Akhbari,
Volume 29, Issue 2 (6-2018)
Abstract

The aim of this study is to present a new method to predict project time and cost under uncertainty. Assuming that what happens in projects implementation which is expressed in the form of Earned Value Management (EVM) indicators is primarily related to the nature of randomness or unreliability, in this study, by using Monte Carlo simulation, and assuming a specific distribution for the time and cost of project activities, a significant number of predicting scenarios will be simulated. According to the data, an artificial neural network is used as efficient data mining methods to estimate the project time and cost at completion.
Naghmeh Khosrowabadi, Rouzbeh Ghousi, Ahmad Makui,
Volume 30, Issue 2 (6-2019)
Abstract

With regard to the industry's development, occupational safety is a key factor in protecting the worker's health, achieving organizational goals and increasing productivity. Therefore, research is needed to investigate the factors affecting occupational safety. This research, based on the information gathered from the paint halts of one of the industrial units of Tehran, uses data mining technique to identify the important factors.Initially with Literature review to 2018, an insight into existing approaches and new ideas earned. Then, with a significant 5600 units of data, the results of the charts, association rules and K-means algorithm were used to extract the latent knowledge with the least error without human intervention from the six-step methodology of Crisp for data mining.The results of charts, association rules, and K-means algorithm for clustering are in a line and have been successful in determining effective factors such as important age groups and education, identifying important events, identifying the halls and finally, the root causes of major events that were the research questions.The results reveal the importance of very young and young age with often diploma education and low experience, in major accidents involving bruising, injury, and torsion, often due to self-unsafe act and unsafe conditions as slipping or collision with things. In addition, the important body members, hands and feet in the color retouching and surface color cabins are more at risk. These results help improve safety strategies. Finally, suggestions for future research were presented.
Maryam Shekary Ashkezary, Amir Albadavi, Mina Shekari Ashkezari,
Volume 30, Issue 4 (12-2019)
Abstract

One of the key issues in the studies on customer relationship management (CRM) and modalities of marketing budget allocation is to calculate the customer’s lifetime value and applying it to macro-management decisions. A major challenge in this sector pertains to making calculations so as to incorporate the possibility of changes in the behavior of customers with the turn of time in the model.
In this article, we first classify the customers of ISACO using clustering techniques and use multilayer neural network to calculate the monetary value of each group of customers during the specific period of time. Then, we use the Markov chain approach to develop a model for calculating the lifetime value of ISACO’s customers by taking into consideration the possibility of changes in their behavior in future time periods.
In this study, a new approach has been used to estimate the parameters of the model proposed for calculating the future lifetime value of ISACO’s customers. This method takes into consideration the possibility of changes in the customer behavior throughout their interaction with the company.
The results obtained here may be used in the allocation of marketing budget and adoption of macro-management decisions to envisage various projects for customers with different lifetime value.
Mehrdad Kargari, Susan Sahranavard,
Volume 31, Issue 1 (3-2020)
Abstract

Background: The continuous growth of healthcare and medicine costs as a strategic commodity requires tools to identify high cost populations and cost control. After the implementation of the healthcare Reform plan in Iran, a huge share of hospital funding has been spent on undesirable costs due to changes in the use of medicines and instruments.
Objective: The aim of this study was to compare the cost of medicines in both the pre and post period of health plan implementation to detect abnormalities and low frequency patterns in the medical prescriptive that account more than 30% of hospital budget funds.
Method: Therefore a data mining model has been used. First, by forming incidence matrices on the cross-features; categorized prescriptions information. Then using normalized risk function to identify abnormal and high cost cases based on the distance between the input data and the mean of the data. The data used are 15078 records, including information from patients' prescriptions from Shari'ati HIS in Tehran-Iran from 2012 to 2016.
Results: According to the obtained results, the proposed model has a positive Likehood ratio (LR+) of 6.35.
 

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