A New Dynamic Intelligent Model to Determine Reliability and Trust of Online Banking by Using Fuzzy C-Mean

2016 ◽  
Vol 4 (3) ◽  
pp. 605
Author(s):  
Ali Mohammad Rezaiee ◽  
Abbas Karimi
Keyword(s):  
Data Mining ◽  
User Behavior ◽  
Target Population ◽  
Internet Banking ◽  
Validity And Reliability ◽  
Use Of Data ◽  
Internet Users ◽  
Exploratory Data ◽  
Exploratory Data Mining ◽  
Descriptive Survey

<p>The main purpose of the present study presents a new model of smart dynamically determine the validity and reliability survey of Internet users bank is using Fuzzy C-Mean model. In other words, the aim of this study is to provide a smart system to determine the behavior of Internet users bank is confidence, so that we can fit the points by the customer, providing banking services to defined limits. In terms of method, a descriptive and exploratory data mining is in use. The method of research was descriptive survey and the use of data mining, exploration. The aim of this study is applied. The survey of methods for qualitative and quantitative data. Since the data of the Agricultural Bank documents (bills of transfer, transfer funds transfers, the number of IT users, foundations, etc.) were collected and interviews with experts in the field of electronic banking Agricultural Bank, Agricultural Bank branch target population for were randomly selected. The results showed that the diagnostic accuracy provided structure to determine the acceptable level of confidence in Internet banking is user behavior.</p>

Impediments to Exploratory Data Mining Success

2008 ◽  
pp. 2566-2582
Author(s):  
Jeff Zeanah
Keyword(s):  
Data Mining ◽  
Data Quality ◽  
Exploratory Data ◽  
Exploratory Data Mining

This chapter discusses impediments to exploratory data mining success. These impediments were identified based on anecdotal observations from multiple projects either reviewed or undertaken by the author and are classified into four main areas: data quality; lack of secondary or supporting data; insufficient analysis manpower; lack of openness to new results. Each is explained, and recommendations are made to prevent the impediment from interfering with the organization’s data mining efforts. The intent of the chapter is to provide an organization with a structure to anticipate these problems and to prevent the occurrence of these problems.

Web Usage Mining Issues in Big Data

2021 ◽  
pp. 102-112
Author(s):  
Sunny Sharma ◽  
Manisha Malhotra
Keyword(s):  
Data Mining ◽  
Big Data ◽  
User Behavior ◽  
Web Usage Mining ◽  
Web Personalization ◽  
Data Mining Techniques ◽  
Meaningful Information ◽  
Web Usage ◽  
Use Of Data ◽  
The Web

Web usage mining is the use of data mining techniques to analyze user behavior in order to better serve the needs of the user. This process of personalization uses a set of techniques and methods for discovering the linking structure of information on the web. The goal of web personalization is to improve the user experience by mining the meaningful information and presented the retrieved information in a way the user intends. The arrival of big data instigated novel issues to the personalization community. This chapter provides an overview of personalization, big data, and identifies challenges related to web personalization with respect to big data. It also presents some approaches and models to fill the gap between big data and web personalization. Further, this research brings additional opportunities to web personalization from the perspective of big data.

scholarly journals Exploratory Data Mining Analysis Identifying Subgroups of Patients With Depression Who Are at High Risk for Suicide

2009 ◽  
Vol 70 (11) ◽  
pp. 1495-1500 ◽  
Author(s):  
Mark A. Ilgen ◽  
Karen Downing ◽  
Kara Zivin ◽  
Katherine J. Hoggatt ◽  
H. Myra Kim ◽  
...  
Keyword(s):  
Data Mining ◽  
High Risk ◽  
Exploratory Data ◽  
Exploratory Data Mining ◽  
Data Mining Analysis

scholarly journals Analyzing Effect of Alternative Banking Channels on Financial Performance of Burundi Commercial Banks: Evidence from Kenya Commercial Bank in Burundi

2020 ◽  
pp. 1-11
Author(s):  
Nkeshimana Carlos ◽  
Martin Onsiro Ronald
Keyword(s):  
Financial Performance ◽  
Commercial Bank ◽  
Commercial Banks ◽  
Strong Relationship ◽  
Target Population ◽  
Internet Banking ◽  
Sample Population ◽  
Mobile Banking ◽  
Significance Level ◽  
Descriptive Survey

The study sought to assess the effect of channels of alternative banking on financial performance of Kenya Commercial Banks in Burundi. The specific objectives were: to examine the effect of mobile banking on financial performance of Kenya Commercial Bank, Burundi; to assess the effect of internet banking on financial performance of Kenya Commercial Bank, Burundi; to examine the effect of auto teller machines on financial performance of Kenya Commercial Bank, Burundi; and to assess the effect of agency banking on financial performance of Kenya Commercial Bank, Burundi. The study employed descriptive survey research design as well as correlation research designs. Based on information obtained from KCB, the target population for the study was 37 employees and 114 customers. The researcher used Slovin’s formula to define the sample population n = 60 (14 employees and 46 customers). A questionnaire was used for data collection. The data was qualitatively and quantitatively analyzed. The results of the study showed that there was a strong relationship between the different banking distribution channels and the financial performance of KCB Bank. It also found that 14.1% of the total variance in financial performance of KCB Bank could be attributed to alternative banking channels. The remaining 85.9% of the variance in financial performance could be attributed to other determinants of financial performance that were not the focus of this study. ANOVA statistics revealed that the regression model was ideal since it had a significance level of 0.0%. The study also found that mobile banking, Automated Teller Machine, agencies and Internet banking affected the performance of commercial banks in a positive and statistically significant way. The study recommends that Burundian commercial bank sought to invest heavily in alternative banking as this will lead to an improvement in banks' financial performance. The study also recommended that KCB should examine the competitive environment and determine the means to achieve the goal of interoperability, and continue to make electronic banking products available, offering various types of bank cards adapted to the needs of each client.

Impediments to Exploratory Data Mining Success

2011 ◽  
pp. 280-299
Author(s):  
Jeff Zeanah
Keyword(s):  
Data Mining ◽  
Data Quality ◽  
Exploratory Data ◽  
Exploratory Data Mining

This chapter discusses impediments to exploratory data mining success. These impediments were identified based on anecdotal observations from multiple projects either reviewed or undertaken by the author and are classified into four main areas: data quality; lack of secondary or supporting data; insufficient analysis manpower; lack of openness to new results. Each is explained, and recommendations are made to prevent the impediment from interfering with the organization’s data mining efforts. The intent of the chapter is to provide an organization with a structure to anticipate these problems and to prevent the occurrence of these problems.

Sequences, Nets, and Filters of Fuzzy Soft Multi Sets in Fuzzy Soft Multi Topological Spaces

2016 ◽  
pp. 172-190
Author(s):  
Anjan Mukherjee ◽  
Ajoy Kanti Das
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Cluster Analysis ◽  
Topological Spaces ◽  
Main Task ◽  
Filter Convergence ◽  
Common Technique ◽  
Exploratory Data ◽  
Exploratory Data Mining ◽  
Convergence Sequence

In this chapter, the authors introduce a new sequence of fuzzy soft multi sets in fuzzy soft multi topological spaces and their basic properties are studied. The concepts of subsequence, convergence sequence and cluster fuzzy soft multi sets of fuzzy soft multi sets are proposed. Actually Cluster analysis or clustering is the task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar to each other than to those in other groups (clusters). It is a main task of exploratory data mining and a common technique for statistical data analysis used in many fields including machine learning, pattern recognition, image analysis, information retrieval and bioinformatics. Here the authors define the notions of net and filter and establish the correspondence between net convergence and filter convergence in fuzzy soft multi topological spaces.

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