Search published articles
Showing 3 results for Fuzzy C-Means Algorithm
Volume 5, Issue 3 (12-2015)
Abstract
In recent existing environment one of the major challenges that planners and managers are grappling with is customer recognition, and distinguishing between different groups of customers in the field of banking services. It is obvious that using an appropriate model gives the bank the opportunity to fit valuable suggestions along with demands for targeted sectors and provides design and thus improves bank performance from different perspectives. The aim of this study is using and appropriate model for clustering customers based on indexes including novelty, number of transaction and financial factors. In this paper, for clustering data, the genetic algorithm combining with fuzzy C-means is used to overcome problems such as being sensitive to the initial value and getting trapped in the local optimum. The simple random sampling method is used to obtain the sample. The findings show that the first cluster of customers due to its high performance in "novelty", "number of transaction" and "financial factors" index are loyal customers and the second cluster of customers because of low performance in "novelty" index, mean performance in "number of transaction" index and high performance in "financial factors" are among those customers who are turning away from bank.
Volume 9, Issue 1 (10-2019)
Abstract
Customers’ purchase behavior is one of the main criteria and critical success factors of e-commerce and online businesses which is similar to traditional businesses with some differences. Therefore, this study tries to reach a model for analyzing the online re-purchasing intention in B2C transactions. This research has been done in the framework of interpretive philosophical paradigm, with inductive approach, in qualitative method and the theme analysis technique using interviewing tools. Accordingly, an interview was conducted with 36 people including 6 e-commerce experts, 13 brokers in Internet business and 17 re-purchasing customers. After coding the interviews, 120 codes were reached at the first stage and final codes had been retrieved which were classified in 21 basic themes. After final analysis, the basis themes were divided into four organizing theme: psychological theme, technological theme, institutional and customer-orientation theme. Among these four themes, the concept of technology in e-commerce has the greatest emphasis. Organizational issues, customer-orientation and psychological issues are at the next rankings, which can also be considered as important which can be considered by as e-commerce managers.
Dr. Zahra Alinezhad, Dr. Sayed Mohammad Bagher Najafi, Dr. Jamal Fathollahi, Dr. Nader Zali,
Volume 21, Issue 1 (3-2021)
Abstract
The knowledge-based economy is the newest pattern of production in the current era. So far, this pattern has resulted in unique achievements for a wide range of countries. This study aims to classify the provinces of Iran in terms of Knowledge-based economy. The classification of provinces based on their similarity in achieving the knowledge-based production pattern is the first step for correct and realistic planning. The same version cannot be used for different provinces. The regional knowledge-based economy index is defined in three dimensions: education, innovation, and information and communication technology, based on 15 sub-indices. The classification is based on the clustering technique, which is one of the branches of unsupervised learning. To do this, k-means and fuzzy c-means algorithms are used simultaneously to compare their results. The optimal number of clusters is calculated through the Silhouette coefficient. This coefficient also indicates the accuracy of the clustering results. Clustering based on the fuzzy c-means algorithm in 6-cluster case with a Silhouette coefficient of 0.77 is the most appropriate classification for research purposes. The results show that there is a clear discrepancy between different provinces in the context of knowledge-based economy. Tehran and Alborz are in separate clusters and are among the leading classes compared to others, while more than half of the provinces belong to backward cluster.
