SmartNotify

2018 ◽  
Vol 10 (1) ◽  
pp. 37-50 ◽  
Author(s):  
Longzhuang Li ◽  
Ajay K. Katangur ◽  
Naga Nandini Karuturi
Keyword(s):  
Data Mining ◽  
Detection Algorithm ◽  
Daily Basis ◽  
Apriori Algorithm ◽  
Android Application ◽  
Data Mining Techniques ◽  
Reminder System ◽  
Dbscan Algorithm ◽  
Points Of Interest

This article describes how it is impractical for a person to remember everything that they do on a day-to-day basis. To address this issue, an android location based reminder system (SmartNotify) that function on users' activities and points of interest are developed. SmartNotify automatically updates preferences of the user based on location behavior on a daily basis using validation from the stay detection algorithm. In addition, SmartNotify provides suggestions for the best locations that people visit frequently in the nearby area by making use of the DBSCAN algorithm and the Apriori algorithm. The utilization of data mining techniques in the android application makes the reminder application more efficient than the traditional way of notifying the user about their events.

scholarly journals Penerapan Algoritma Apriori pada Sistem Informasi Perpustakaan Sekolah SMK Negeri 1 Siantar

2012 ◽  
Vol 6 (1) ◽  
pp. 226
Author(s):  
Risti DwiSyari ◽  
M Safii ◽  
M Fauzan
Keyword(s):  
Data Mining ◽  
School Library ◽  
School Libraries ◽  
Vocational School ◽  
Apriori Algorithm ◽  
Data Mining Techniques ◽  
Great Leader ◽  
Library Staff ◽  
Strategic Layout

The SMK Negeri 1 Siantar School Library is one of the special libraries located at the SMK Negeri 1 Siantar School. Libraries provide various kinds of library materials such as books, lessons, lesson questions, and other vocational books. After the researcher made observations, the problem that often occurred was books that were borrowed and returned books that had a non-strategic layout, so that library visitors who did not know the placement found it difficult to find the books they wanted to borrow. This research uses data mining techniques, namely the Apriori Algorithm, the Apriori Method is a method for looking for patterns of relationships between one or more items in a dataset. The Apriori method can be used for data on borrowing books at the Siantar 1 State Vocational School School Library, where the composition of the library books (B1) X_Press UN 2019 B. Indonesia side by side with books (B4) School of Love is a Great Leader and Teacher, if the composition of the book is (B10) Moral Mulia side by side with book (B1) X_Press UN 2019 B. Indonesia, If the book arrangement (B7) X_Press Mathematics is side by side with the book (B5) Relationer, if the book arrangement (B7) X_Press Mathematics is side by side with the book (B9) Indonesian Wisdom Batak Toba, and if the arrangement of the book (B10) Morals Mulia is side by side with the book (B8) Hati Therapy, the data from these items each met the minimum confidance value of 0,5% or the same as the specified 50%. The result of this research is to help library staff arrange the book layout correctly. It is hoped that this research can provide input to the school

The Application of Apriori Algorithm in Analysis on Admitted Students of Colleges and Universities

2013 ◽  
Vol 321-324 ◽  
pp. 2578-2582
Author(s):  
Qian Zhang
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Colleges And Universities ◽  
Apriori Algorithm ◽  
Data Mining Techniques ◽  
Minimum Support ◽  
Sample Data ◽  
Mining Association Rules

This paper examined the application of Apriori algorithm in extracting association rules in data mining by sample data on student enrollments. It studied the data mining techniques for extraction of association rules, analyzed the correlation between specialties and characteristics of admitted students, and evaluated the algorithm for mining association rules, in which the minimum support was 30% and the minimum confidence was 40%.

scholarly journals Clustering e-Banking Customer using Data Mining and Marketing Segmentation

1970 ◽  
Vol 2 (1) ◽  
pp. 63-69 ◽  
Author(s):  
Waminee Niyagas ◽  
Anongnart Srivihok ◽  
Sukumal Kitisin
Keyword(s):  
Data Mining ◽  
Commercial Bank ◽  
Commercial Banks ◽  
Historical Data ◽  
Apriori Algorithm ◽  
Data Confidentiality ◽  
Banking Services ◽  
Data Mining Techniques ◽  
Marketing Segmentation ◽  
Using Data

In Thailand e-banking has been offered by various financial institutes including Thai commercial banks and government banks. However, e-banking in Thailand is not widely used and accepted as in other countries. Accordingly, the study of e-banking is scantly due to the limitation of data confidentiality. This study uses data mining techniques to analyse historical data of e-banking usages from a commercial bank in Thailand. These techniques including SOMS, K-Mean algorithm and marketing techniques-RFM analysis are used to segment customers into groups according to their personal profiles and e-banking usages. Then Apriori algorithm is applied to detect the relationships within features of e-banking services. Typically, results of this study are presented and can be used to generate new service packages which are customised to each segment of e-banking users.

scholarly journals Implementasi Algoritma Apriori Pada Penyusunan Menu Makanan Rumah Makan Prasmanan

2020 ◽  
Vol 6 (2) ◽  
Author(s):  
Asep Budiman Kusdinar ◽  
Daris Riyadi ◽  
Asriyanik Asriyanik
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
High Frequency ◽  
A Priori ◽  
Apriori Algorithm ◽  
Frequency Pattern ◽  
Data Mining Techniques ◽  
Rule Method

A buffet restaurant is a restaurant that provides buffet food that is served directly at the dining table so that customers can order more food according to their needs. This study uses the association rule method which is one of the methods of data mining and a priori algorithms. Data mining is the process of discovering patterns or rules in data, in which the process must be automatic or semi-automatic. Association rules are one of the techniques of data mining that is used to look for relationships between items in a dataset. While  the apriori algorithm is a very well-known algorithm for finding high-frequency patterns, this a priori algorithm is a type of association rule in data mining. High- frequency patterns are patterns of items in the database that have frequencies or support. This high-frequency pattern is used to develop rules and also some other data mining techniques. The composition of the food menu in the Asgar restaurant is now arranged randomly without being prepared on the food menu between one another. The result of this research is  to support the composition of the food menu at the Asgar restaurant so that it is easier to take food menu with one another.  

scholarly journals IMPLEMENTASI METODE MARKET BASKET ANALYSIS (MBA) MENGGUNAKAN ALGORITMA APRIORI DALAM TRANSAKSI PENJUALAN (STUDI KASUS: KAFE RUANG TEMU)

2020 ◽  
Vol 10 (2) ◽  
pp. 138
Author(s):  
Muhammad SyahruRomadhon ◽  
Achmad Kodar
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
Apriori Algorithm ◽  
Market Basket Analysis ◽  
Tourist Attractions ◽  
Market Basket ◽  
Transaction Data ◽  
Data Mining Techniques ◽  
Minimum Support

Jakarta is one of the culinary attractions, many tourist attractions every year become creative in business. One of them is a cafe. Cafe Ruang Temu has sales transaction data but is not used to see associations between one product and another. In this case there needs to be a system for finding menu combinations by processing sales transactions. One of the data mining techniques is association rule or Market Basket Analysis (MBA) with apriori algorithm. Apriori algorithm aims to produce association rules to form menu combinations. The sales dataset for January 2019 to July 2019 is determined by the minimum support and minimum confidence values that have been set.  

Data Mining Techniques for Outlier Detection

Data Mining ◽  
2013 ◽  
pp. 159-178
Author(s):  
N Suri ◽  
M Murty ◽  
G Athithan
Keyword(s):  
Data Mining ◽  
Outlier Detection ◽  
Detection Algorithm ◽  
Abnormal Behavior ◽  
Data Sets ◽  
Data Set ◽  
Research Issues ◽  
Data Mining Techniques ◽  
Performance Requirements ◽  
Typical Data

Among the growing number of data mining techniques in various application areas, outlier detection has gained importance in recent times. Detecting the objects in a data set with unusual properties is important as such outlier objects often contain useful information on abnormal behavior of the system described by the data set. Outlier detection has been popularly used for detection of anomalies in computer networks, fraud detection and such applications. Though a number of research efforts address the problem of detecting outliers in data sets, there are still many challenges faced by the research community in terms of identifying a suitable technique for addressing specific applications of interest. These challenges are primarily due to the large volume of high dimensional data associated with most data mining applications and also due to the performance requirements. This chapter highlights some of the important research issues that determine the nature of the outlier detection algorithm required for a typical data mining application. The research issues discussed include the method of outlier detection, size and dimensionality of the data set, and nature of the target application. Thus this chapter attempts to cover the challenges and possible research directions along with a survey of various data mining techniques dealing with the outlier detection problem.

scholarly journals Applying Data Mining Techniques to Improve Information Security in the Cloud: A Single Cache System Approach

2016 ◽  
Vol 2016 ◽  
pp. 1-5 ◽  
Author(s):  
Amany AlShawi
Keyword(s):  
Data Mining ◽  
Cloud Computing ◽  
Information Security ◽  
System Approach ◽  
Specific Reference ◽  
Apriori Algorithm ◽  
Data Mining Techniques ◽  
Data Objects ◽  
Cache System ◽  
Day By Day

Presently, the popularity of cloud computing is gradually increasing day by day. The purpose of this research was to enhance the security of the cloud using techniques such as data mining with specific reference to the single cache system. From the findings of the research, it was observed that the security in the cloud could be enhanced with the single cache system. For future purposes, an Apriori algorithm can be applied to the single cache system. This can be applied by all cloud providers, vendors, data distributors, and others. Further, data objects entered into the single cache system can be extended into 12 components. Database and SPSS modelers can be used to implement the same.

scholarly journals The Use of Apriori Algorithm in the Formation of Association Rule at Lotteria Cibubur

SinkrOn ◽  
2020 ◽  
Vol 4 (2) ◽  
pp. 76
Author(s):  
Ovi Liansyah ◽  
Henny Destiana
Keyword(s):  
Data Mining ◽  
Data Storage ◽  
Association Rules ◽  
Association Rule ◽  
Apriori Algorithm ◽  
Transaction Data ◽  
Data Mining Techniques ◽  
Sales Data ◽  
Sales Strategy ◽  
Data Mining Application

Lotteria as one of the franchises that produce sales data every day, has not been able to maximize the utilization of that data. The sale data storage is still not optimal. By utilizing sales transaction data that have been stored in the database, the management can find out the menus purchased simultaneously, using the association rule. Namely, data mining techniques to find the association rules of a combination of items. The process of searching for associations uses the help of apriori algorithms to produce patterns of the combination of items and rules as important knowledge and information from sales transaction data. By using the minimum support parameters, the minimum and the month period of the sales transaction to find the association rules, the data mining application generates association rules between items in April 2019, where consumers who buy hot / ice coffee will then buy float together with support of 16% and 100% confidence. Knowing which menu products or items are the most sold, thus lotteria Cibubur can develop a sales strategy to sell other types of menu products by examining the advantages of the most sold menu with other menus and can increase the stock of menu ingredients.

Data Mining Techniques for Outlier Detection

2011 ◽  
pp. 19-38 ◽  
Author(s):  
N Suri ◽  
M Murty ◽  
G Athithan
Keyword(s):  
Data Mining ◽  
Outlier Detection ◽  
Detection Algorithm ◽  
Abnormal Behavior ◽  
Data Sets ◽  
Data Set ◽  
Research Issues ◽  
Data Mining Techniques ◽  
Performance Requirements ◽  
Typical Data

Among the growing number of data mining techniques in various application areas, outlier detection has gained importance in recent times. Detecting the objects in a data set with unusual properties is important as such outlier objects often contain useful information on abnormal behavior of the system described by the data set. Outlier detection has been popularly used for detection of anomalies in computer networks, fraud detection and such applications. Though a number of research efforts address the problem of detecting outliers in data sets, there are still many challenges faced by the research community in terms of identifying a suitable technique for addressing specific applications of interest. These challenges are primarily due to the large volume of high dimensional data associated with most data mining applications and also due to the performance requirements. This chapter highlights some of the important research issues that determine the nature of the outlier detection algorithm required for a typical data mining application. The research issues discussed include the method of outlier detection, size and dimensionality of the data set, and nature of the target application. Thus this chapter attempts to cover the challenges and possible research directions along with a survey of various data mining techniques dealing with the outlier detection problem.

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