scholarly journals Credit Card User Frequent Buying Predicton Analysis using Cluster Methods

2019 ◽  
Vol 8 (9) ◽  
pp. 2223-2225
Keyword(s):  
Data Mining ◽  
Data Clustering ◽  
Credit Card ◽  
Clustering Algorithm ◽  
User Requirements ◽  
Digital Form ◽  
Data Mining Techniques ◽  
Frequent Sets ◽  
The World ◽  
Cluster Methods

Today the world becomes more digital. The cashless transactions are increased in all sectors. The large amounts of data in digital form are generated every day. The companies need to analyze the existing transactions, to predict the user requirements in the future. The payment during the purchase can be done in different modes by the user. In this work, the credit card transactions are analyzed. There are many data mining techniques are used to predict the frequent sets of items during purchase. Data clustering in one of the familiar and widely used technique to identify a similar set of items in a group or dataset. In this work, the two familiar existing techniques k-means and k-mediods are compared with the same datasets. The results show the best clustering algorithm.

scholarly journals Clustering of Cardiovascular Disease Patients Using Data Mining Techniques with Principal Component Analysis and K-Medoids Clustering of Cardiovascular Disease Patients Using Data Mining Techniques with Principal Component Analysis and K-Medoids

2020 ◽  
Author(s):  
Edy Irwansyah ◽  
Ebiet Salim Pratama ◽  
Margaretha Ohyver
Keyword(s):  
Data Mining ◽  
Cardiovascular Disease ◽  
Principal Component Analysis ◽  
Data Reduction ◽  
Clustering Algorithm ◽  
Principal Component ◽  
Component Analysis ◽  
Data Mining Techniques ◽  
The World ◽  
Using Data

Cardiovascular disease is the number one cause of death in the world and Quoting from WHO, around 31% of deaths in the world are caused by cardiovascular diseases and more than 75% of deaths occur in developing countries. The results of patients with cardiovascular disease produce many medical records that can be used for further patient management. This study aims to develop a method of data mining by grouping patients with cardiovascular disease to determine the level of patient complications in the two clusters. The method applied is principal component analysis (PCA) which aims to reduce the dimensions of the large data available and the techniques of data mining in the form of cluster analysis which implements the K-Medoids algorithm. The results of data reduction with PCA resulted in five new components with a cumulative proportion variance of 0.8311. The five new components are implemented for cluster formation using the K-Medoids algorithm which results in the form of two clusters with a silhouette coefficient of 0.35. Combination of techniques of Data reduction by PCA and the application of the K-Medoids clustering algorithm are new ways for grouping data of patients with cardiovascular disease based on the level of patient complications in each cluster of data generated.

Literature Study –Data Mining Techniques on Detecting Fradulent Activities in Credit Card

2017 ◽  
Vol 6 (10) ◽  
pp. 60
Author(s):  
S. K. Saravanan ◽  
G. N. K. Suresh Babu
Keyword(s):  
Data Mining ◽  
Credit Card ◽  
Data Transfer ◽  
Study Data ◽  
Time Requirement ◽  
Literature Study ◽  
Data Mining Techniques ◽  
Online Transactions ◽  
Secured Data ◽  
Credit Card Usage

In contemporary days the more secured data transfer occurs almost through internet. At same duration the risk also augments in secure data transfer. Having the rise and also light progressiveness in e – commerce, the usage of credit card (CC) online transactions has been also dramatically augmenting. The CC (credit card) usage for a safety balance transfer has been a time requirement. Credit-card fraud finding is the most significant thing like fraudsters that are augmenting every day. The intention of this survey has been assaying regarding the issues associated with credit card deception behavior utilizing data-mining methodologies. Data mining has been a clear procedure which takes data like input and also proffers throughput in the models forms or patterns forms. This investigation is very beneficial for any credit card supplier for choosing a suitable solution for their issue and for the researchers for having a comprehensive assessment of the literature in this field.

Teknik Data Mining Dalam Clustering Produksi Susu Segar Di Indonesia Dengan Algoritma K-Means

2019 ◽  
Vol 1 (1) ◽  
pp. 31-39
Author(s):  
Ilham Safitra Damanik ◽  
Sundari Retno Andani ◽  
Dedi Sehendro
Keyword(s):  
Data Mining ◽  
Milk Production ◽  
Clustering Algorithm ◽  
Clustering Method ◽  
Data Mining Techniques ◽  
Low Level ◽  
Fresh Milk ◽  
Nutritional Needs ◽  
High Level ◽  
Level Cluster

Milk is an important intake to meet nutritional needs. Both consumed by children, and adults. Indonesia has many producers of fresh milk, but it is not sufficient for national milk needs. Data mining is a science in the field of computers that is widely used in research. one of the data mining techniques is Clustering. Clustering is a method by grouping data. The Clustering method will be more optimal if you use a lot of data. Data to be used are provincial data in Indonesia from 2000 to 2017 obtained from the Central Statistics Agency. The results of this study are in Clusters based on 2 milk-producing groups, namely high-dairy producers and low-milk producing regions. From 27 data on fresh milk production in Indonesia, two high-level provinces can be obtained, namely: West Java and East Java. And 25 others were added in 7 provinces which did not follow the calculation of the K-Means Clustering Algorithm, including in the low level cluster.

scholarly journals Market Basket Analysis for Determination of Consumer Behavior at XYZ Stores Using R Programming

2021 ◽  
Vol 3 (2) ◽  
pp. 0210206
Author(s):  
Kelik Sussolaikah
Keyword(s):  
Data Mining ◽  
Consumer Behavior ◽  
Market Basket Analysis ◽  
Process Data ◽  
Market Basket ◽  
Data Mining Techniques ◽  
Programming Tool ◽  
The World ◽  
R Programming

Data mining is one of the fields of science in the world of informatics which has an important role, especially with regard to data. There are many algorithms and methods that can be used to process data. The paper this time the author tries to conduct research on consumer behavior by using one of the data mining techniques, namely market basket analysis. This research uses the R Programming tool, where it is hoped that the research can be carried out effectively and efficiently. Based on the research conducted, it is known that there has been a significant purchase of several items that have been described as a plot. The tendency of consumers to buy several items followed by other items can be a consideration for arranging the layout of goods on the sales shelf or arranging product stock in a supermarket.

Bibliomining for Library Decision-Making

2011 ◽  
pp. 341-345
Author(s):  
Scott Nicholson ◽  
Jeffrey Stanton
Keyword(s):  
Data Mining ◽  
Digital Libraries ◽  
Large Data ◽  
Data Sets ◽  
Data Mining Techniques ◽  
Governmental Organizations ◽  
Non Governmental Organizations ◽  
The People ◽  
The World ◽  
Use Of The Internet

Most people think of a library as the little brick building in the heart of their community or the big brick building in the center of a campus. These notions greatly oversimplify the world of libraries, however. Most large commercial organizations have dedicated in-house library operations, as do schools, non-governmental organizations, as well as local, state, and federal governments. With the increasing use of the Internet and the World Wide Web, digital libraries have burgeoned, and these serve a huge variety of different user audiences. With this expanded view of libraries, two key insights arise. First, libraries are typically embedded within larger institutions. Corporate libraries serve their corporations, academic libraries serve their universities, and public libraries serve taxpaying communities who elect overseeing representatives. Second, libraries play a pivotal role within their institutions as repositories and providers of information resources. In the provider role, libraries represent in microcosm the intellectual and learning activities of the people who comprise the institution. This fact provides the basis for the strategic importance of library data mining: By ascertaining what users are seeking, bibliomining can reveal insights that have meaning in the context of the library’s host institution. Use of data mining to examine library data might be aptly termed bibliomining. With widespread adoption of computerized catalogs and search facilities over the past quarter century, library and information scientists have often used bibliometric methods (e.g., the discovery of patterns in authorship and citation within a field) to explore patterns in bibliographic information. During the same period, various researchers have developed and tested data mining techniques—advanced statistical and visualization methods to locate non-trivial patterns in large data sets. Bibliomining refers to the use of these bibliometric and data mining techniques to explore the enormous quantities of data generated by the typical automated library.

Fuzzy Clustering of Web User Profiles for Analyzing their Behavior and Interests

2012 ◽  
pp. 91-114
Author(s):  
Stanislav Kreuzer ◽  
Natascha Hoebel
Keyword(s):  
Data Mining ◽  
Consumer Behavior ◽  
Fuzzy Clustering ◽  
Data Clustering ◽  
Ordinal Data ◽  
Customer Relationships ◽  
User Profiles ◽  
Data Mining Techniques

One of the keys to building effective e-customer relationships is an understanding of consumer behavior online. However, analyzing the behavior of customers online is not necessarily an indicator of their interests. Therefore, building profiles of registered users of a website is of importance if it goes beyond collecting obvious information the user is willing to give at the time of the registration. These user profiles can contribute to the analysis of the users’ interests. Important tools for the analysis are data-mining techniques, for example, the clustering of collected user information. This chapter addresses the problem of how to define, calculate, and visualize fuzzy clusters of Web visitors with respect to their behavior and supposed interests. This chapter shows how to cluster Web users based on their profile and by their similar interests in several topics using the fuzzy and hybrid CORD (Clustering of Ordinal Data) clustering system, which is part of the Gugubarra Framework.

Modified Single Pass Clustering Algorithm Based on Median as a Threshold Similarity Value

2017 ◽  
pp. 24-48 ◽  
Author(s):  
Mamta Mittal ◽  
R. K. Sharma ◽  
V.P. Singh ◽  
Lalit Mohan Goyal
Keyword(s):  
Data Mining ◽  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Data Mining Techniques ◽  
Single Pass ◽  
Hidden Patterns ◽  
Data Objects

Clustering is one of the data mining techniques that investigates these data resources for hidden patterns. Many clustering algorithms are available in literature. This chapter emphasizes on partitioning based methods and is an attempt towards developing clustering algorithms that can efficiently detect clusters. In partitioning based methods, k-means and single pass clustering are popular clustering algorithms but they have several limitations. To overcome the limitations of these algorithms, a Modified Single Pass Clustering (MSPC) algorithm has been proposed in this work. It revolves around the proposition of a threshold similarity value. This is not a user defined parameter; instead, it is a function of data objects left to be clustered. In our experiments, this threshold similarity value is taken as median of the paired distance of all data objects left to be clustered. To assess the performance of MSPC algorithm, five experiments for k-means, SPC and MSPC algorithms have been carried out on artificial and real datasets.

A Diagnostic Model for Cardiovascular Diseases Using Feature Selection and Data Clustering

2020 ◽  
Vol 17 (6) ◽  
pp. 2859-2865
Author(s):  
Shima Farahbakhsh
Keyword(s):  
Data Mining ◽  
Feature Selection ◽  
Cardiovascular Diseases ◽  
Data Clustering ◽  
Information Gain ◽  
Pearson Correlation ◽  
Large Body ◽  
Diagnostic Model ◽  
Target Feature ◽  
Data Mining Techniques

Cardiovascular diseases are one of the most common diseases and currently, the number of people with cardiovascular diseases is increasing. However, if necessary treatment is not provided for the patient at the right time, it might lead to patient death. Therefore, accurate diagnosis of cardiac problems during the first examination along with suitable treatment can decrease the rate of mortality due to cardiovascular diseases. To this end, data mining techniques can be used. Data mining extracts the necessary data from a large body of information. This data is then is used for data classification and prediction through clustering, classification and/or identification of hidden patterns. Many studies so far have focused on using data mining techniques to diagnose cardiovascular diseases. The present study aims to provide a diagnostic model for cardiovascular diseases using an approach based on feature selection and data clustering as pre-processing steps. The proposed model involves 4 main phases: (1) Pre-processing the data to eliminate null and outlier values from data sets; (2) Choosing effective features by using three methods of Pearson correlation coefficient, Information Gain algorithm, and analysis of the main components which try to remove the features that do not have a special relationship with target feature and the behavior of this feature is independent of the target feature; at the end of this phase, 5 features of 13 initial features are removed. (3) Using the KMeans algorithm in data clustering and developing pre-processes before creating the final cluster and developing a model for predicting the type of cardiovascular diseases. The results obtained from the proposed solution show that am4 algorithms of ID3, Naïve Bayes, SVM, and IBK used, IBK algorithm was the most accurate algorithm with 0.97 accuracy.

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