scholarly journals K- Means Clustering on Based Classification Method of Sales Agent

2021 ◽  
pp. 1-6
Author(s):  
Yeng Primawati ◽  
Ihsan Verdian ◽  
Gunadi Widi Nurcahyo
Keyword(s):  
Data Mining ◽  
Business Strategy ◽  
Historical Data ◽  
Classification Method ◽  
Clustering Method ◽  
Transaction Data ◽  
Data Mining Techniques ◽  
Segmentation Approach ◽  
Sales Agent ◽  
Sales Agents

Agent is one of very important assets for distributors. A better knowledge of the agents and their behavior is required, particularly to support decisions related to the company's business strategy and to manage a better relationship with distributors. Such knowledge can be obtained by classifying agents based on their behavior through historical data, such as the sale and purchase transaction data. One approach that can be done is a segmentation approach can be done by dividing the agents into several segments. In this paper, Data Mining techniques i.e. K-means clustering method is exploredto classify sales agents. By implementing k-means, the knowledge about the best agents can be acquired along with the agents that have least contribution to the distributor.

scholarly journals Pola Beli Konsumen Menggunakan Algoritma Fp-Growth Untuk Rekomendasi Promosi Pada Aneka Jaya Motor

2021 ◽  
Vol 6 (1) ◽  
pp. 48-55
Author(s):  
Junta Zeniarja
Keyword(s):  
Data Mining ◽  
Business Strategy ◽  
Recommendation System ◽  
Transaction Data ◽  
Data Mining Techniques ◽  
Minimum Support ◽  
Spark Plug ◽  
Using Data ◽  
Product Promotion

A piece of appropriate information can create and establish a business strategy in increasing sales through technology that can affect the trade-in buying and selling goods with the data information generated can be calculated in detail and accurately. At Aneka Jaya Motor Semarang, this was triggered by the demand for competition. One solution is a product promotion target. For determining which items are feasible for promotion, the application of a promotional decision recommendation system is made using data mining techniques associated with FP-Growth algorithms, its function is to find items that are often purchased simultaneously by consumers. Data used in the form of transaction data with the total amount used 501 data. The results obtained by appearing 1 rule is if consumers buy spark plug parts then buy oil parts with minimum support of 10% and minimum confidence of 35%. The lift ratio obtained is 1 so that valid rules are generated.

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 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 Rekomendasi Paket Menu Angkringan Waru Tanjung Bias Dengan Algoritma Frequent Pattern Growth Berbasis Web

2021 ◽  
Vol 3 (2) ◽  
pp. 92-98
Author(s):  
Lalu Aldila Maulana Fajar ◽  
Ria Rismayati
Keyword(s):  
Data Mining ◽  
Millennial Generation ◽  
Frequent Pattern ◽  
Growth Data ◽  
Transaction Data ◽  
Data Mining Techniques ◽  
Pattern Growth ◽  
The Many ◽  
Package Recommendations ◽  
Data Mining Application

Culinary business using carts selling various kinds of heavy food, light and drinks, is favored by many people to just fill their stomachs, gather with friends and even family. Culinary businesses or culinary destinations like this are known as Angkringan which are increasingly mushrooming in the millennial generation. Angkringan Waru, located in Tanjung Bias, is a gathering destination for all people to enjoy a relaxed atmosphere on the beach. Angkringan Waru provides 85 types of menus for its customers, the many menus often confuse customers in choosing snacks while enjoying the beachside atmosphere. Starting from these problems, data mining techniques are used with the Frequent Pattern Growth (Fp-Growth) algorithm to recommend items in producing a menu package consisting of 1 snack item and 1 drink item. The dataset used is transaction data from Angkringan Waru as many as 870 transactions, the resulting output is a menu package recommendation rule and implemented in a web for Angkringan Waru. The Fp-Growth Data Mining Application by providing a minimum support value of 20% and Confident 50% with a lift ratio > 1 produces 57 rules or menu package recommendations that will be offered to Angkringan Waru customers. The results of the application in the form of 57 menu package recommendations are then used as recommendations for Angkringan Waru customers, where these menus are the favorite menus of customers at Angkringan Waru.

Segmentation Approach for Athleisure and Performance Sport Retailers Based on Data Mining Techniques

2021 ◽  
Vol 13 (3) ◽  
pp. 71-85
Author(s):  
Sunčica Rogić ◽  
Ljiljana Kašćelan
Keyword(s):  
Data Mining ◽  
Direct Marketing ◽  
Business Environment ◽  
Customer Segmentation ◽  
Data Sets ◽  
Data Mining Techniques ◽  
The Future ◽  
Segmentation Approach ◽  
Promotional Materials ◽  
And Performance

This paper seeks to compare certain customer segments from two sport footwear, apparel, and equipment retailers and to examine an objective market segmentation method, based on the recency, frequency, monetary (RFM) and the decision tree (DT) models. The case study is based on two data sets, aiming to compare the different customer segments, both from sport retail industry, and represents an application of data mining techniques in a business environment. The customer segmentation enables the customer selection for the future direct marketing campaigns based on the previous purchasing behavior. Analyzing the customers' purchasing history can help the company determine the value of each customer and therefore target or not target such customers in the future with promotional materials, based on both the customers' interests and their value. Thus, based on the results, personalized offers can be created for each of the defined customer groups, which may increase the efficiency of the overall campaign, reduce costs, and increase profitability.

A Radar Intelligence Simulation Method Based on Data Mining Techniques

2012 ◽  
Vol 263-266 ◽  
pp. 277-282 ◽  
Author(s):  
Xiao Chao Wu ◽  
Ying Cheng ◽  
Liao Liao Yan ◽  
Fang Xia Xue
Keyword(s):  
Data Mining ◽  
Historical Data ◽  
Simulation Method ◽  
Radar Data ◽  
Second Order ◽  
Test Bed ◽  
Discrete Curvature ◽  
Data Mining Techniques ◽  
Intelligent Information ◽  
Using Data

A new method to generate radar air intelligent information by using data mining techniques based on historical radar data is proposed. This method has two stages: One is “filtering separation - piecewise fitting - feature clustering". In this stage, the radar historical data is divided into the actual true track and noise. Through computing the second-order discrete curvature, the actual true track is decomposed into several segments, such as straight line and arc, which are fitted with multinomial subsequently. On this basis, after analyzing the characteristic vector of radar historical data, the clustering database is established; the other is “feature association-track recombination”. The track in pre-deigned air scenario is segmented by the second-order discrete curvature. After the correlative feature information of the segmented scenario is searched, matched and associated with the information in clustering database, a new track will be restructured by using this output results. This method is very available for its effective application in simulation test-bed of C3I system.

scholarly journals THE ENHANCEMENT OF ODL STUDENT RECRUITING CAMPAIGN WITH DATA WAREHOUSE DEVELOPMENT AND DATA MINING TECHNIQUES

2010 ◽  
Vol 5 (1) ◽  
pp. 41-47
Author(s):  
Waranya Poonnawat ◽  
Sumruay Komlayut ◽  
Nuttaporn Henchareonlert
Keyword(s):  
Data Mining ◽  
Marital Status ◽  
Data Warehouse ◽  
Low Income ◽  
Historical Data ◽  
Demographic Data ◽  
Annual Reports ◽  
User Requirements ◽  
Data Mining Techniques ◽  
Student Data

The purpose of this research was to develop an OLAP cube data warehouse, and, using data mining techniques, to support the university's public relations, admissions, and planning divisions in the efficient recruiting of students by surveying, through interviews; the opinions of management and operational personnel, and through documents; the attributes in application forms and annual reports. User requirements, source data and systems were all examined. The data warehouse and front-end applications developed are described below. 1. Student Data Warehouse—this repository was designed to store students' historical data and to facilitate analysis and reporting following the user requirements. Students' historical data including demographic data from 2001-2005 were extracted, loaded and transformed from source systems, then they were cleaned before uploading to the data warehouse using star schema. 2. OLAP Cub—this 122 multidimensional structure enables users to analyze the students' demographic data in many dimensions such as “Number of Registered Students in each year by Semester, Major, School, Gender, Occupation, Region, etc.” Predefined reports were created and published to an intranet and users were able to create ad-hoc reports through web browsers as well as XLAddin. 3. Data Mining—this technique finds hidden knowledge and patterns in ODL student data supporting decision making, using three algorithms: Naïve Bayes, Clustering and Association Rules. Occupation of students is the strongest factor influencing students' choices of Schools. Students' demographic data can be clustered into groups with similar or dissimilar characteristics such as “Single, Unemployed, Low Income (<3,000 Baht)” or “Married, Male, Studying Law, High Income”, and can generate rules from frequently occurring cases such as “Occupation=Teacher-Lecturer (private sector), Marital Status=Single > School=School of Educational Studies” or “Occupation=Police, Marital Status=Single -> School=School of Law”. The results from the study indicated that users were satisfied using information and applications from the data warehouse, OLAP cube and data mining techniques which enable the university to reduce costs and to reach the desired enrolment target effectively.

scholarly journals Implementasi Algoritma Apriori Terhadap Data Penjualan Oli Mobil Berbasis Sistem Pencarian Aturan Asosiasi

2019 ◽  
Vol 3 (2) ◽  
pp. 316
Author(s):  
Jorza Rulianto ◽  
Wida Prima Mustika
Keyword(s):  
Data Mining ◽  
Pattern Recognition ◽  
Software Testing ◽  
A Priori ◽  
Marketing Strategies ◽  
Motor Oil ◽  
Transaction Data ◽  
Data Mining Techniques ◽  
Pattern Recognition Techniques ◽  
Mathematical Techniques

Data mining techniques are used to design effective sales or marketing strategies by utilizing sales transaction data that is already available in the company. The problem in the company is that there are many data transactions that occur unknown, causing an accumulation of data unknown sales most in each month & year, unknown brands of car oil are often sold or demanded by customers. So this association search uses a priori algorithm as a place to store data using pattern recognition techniques such as static and mathematical techniques from a set of relationships (associations) between items obtained, it is expected that can help developers in designing marketing strategies for goods in the company. Software testing results that have been made have found the most sold oil brand products if you buy Shell Hx7, it will buy Toyota Motor Oil with 50% support and 66.7% confidence. If you buy Toyota Motor Oil, you will buy Shell Hx 7 with 50% support and 85.7% confidence.

Analytical Customer Requirement Analysis Based on Data Mining

2011 ◽  
pp. 227-247
Author(s):  
Jianxin Jiao ◽  
Yiyang Zhang ◽  
Martin Helander
Keyword(s):  
Data Mining ◽  
Product Development ◽  
Historical Data ◽  
Requirement Analysis ◽  
Manufacturing Companies ◽  
Customer Requirement ◽  
Data Mining Techniques ◽  
Analysis Process ◽  
Principal Factors

This chapter applies data-mining techniques to help manufacturing companies analyze their customers’ requirements. Customer requirement analysis has been well recognized as one of the principal factors in product development for achieving success in the marketplace. Due to the difficulties inherent in the customer requirement analysis process, reusing knowledge from historical data suggests itself as a natural technique to facilitate the handling of requirement information and the tradeoffs among many customers, marketing and engineering concerns. This chapter proposes to apply data-mining techniques to infer the latent information from historical data and thereby improve the customer requirement analysis process.

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