scholarly journals Data mining techniques applied to historical data of industrial processes as a tool to find time intervals suitable for system identification.

2020 ◽  
Author(s):  
Giulio Cesare Mastrocinque Santo
Keyword(s):  
Data Mining ◽  
System Identification ◽  
Historical Data ◽  
Industrial Processes ◽  
Time Intervals ◽  
Data Mining Techniques

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.

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.

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.

Analytical Customer Requirement Analysis Based on Data Mining

2008 ◽  
pp. 2798-2815
Author(s):  
Jianxin ("Roger") 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.

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.

KLASIFIKASI SMS SPAM MENGGUNAKAN SUPPORT VECTOR MACHINE

2019 ◽  
Vol 15 (2) ◽  
pp. 275-280
Author(s):  
Agus Setiyono ◽  
Hilman F Pardede
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Decision Tree ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Spam Detection ◽  
Support Vector Machine Algorithm ◽  
Data Mining Techniques ◽  
To Receive

It is now common for a cellphone to receive spam messages. Great number of received messages making it difficult for human to classify those messages to Spam or no Spam.  One way to overcome this problem is to use Data Mining for automatic classifications. In this paper, we investigate various data mining techniques, named Support Vector Machine, Multinomial Naïve Bayes and Decision Tree for automatic spam detection. Our experimental results show that Support Vector Machine algorithm is the best algorithm over three evaluated algorithms. Support Vector Machine achieves 98.33%, while Multinomial Naïve Bayes achieves 98.13% and Decision Tree is at 97.10 % accuracy.

How Sweet and Ripe are the Fruits? Data Mining Techniques for Classifying and Predicting ‘Quick-Wins’ Direct Capital Investment in Indonesia as One Approach to Business intelligence Orientation and Knowledge Management Scenarios of Indonesian Enterprises

2019 ◽  
Vol 1 (1) ◽  
pp. 121-131
Author(s):  
Ali Fauzi
Keyword(s):  
Data Mining ◽  
Business Intelligence ◽  
Direct Investment ◽  
Capital Investment ◽  
Export Market ◽  
Added Value ◽  
Knowledge Generation ◽  
Management Scenarios ◽  
Data Mining Techniques ◽  
Knowledge Based Economy

The existence of big data of Indonesian FDI (foreign direct investment)/ CDI (capital direct investment) has not been exploited somehow to give further ideas and decision making basis. Example of data exploitation by data mining techniques are for clustering/labeling using K-Mean and classification/prediction using Naïve Bayesian of such DCI categories. One of DCI form is the ‘Quick-Wins’, a.k.a. ‘Low-Hanging-Fruits’ Direct Capital Investment (DCI), or named shortly as QWDI. Despite its mentioned unfavorable factors, i.e. exploitation of natural resources, low added-value creation, low skill-low wages employment, environmental impacts, etc., QWDI , to have great contribution for quick and high job creation, export market penetration and advancement of technology potential. By using some basic data mining techniques as complements to usual statistical/query analysis, or analysis by similar studies or researches, this study has been intended to enable government planners, starting-up companies or financial institutions for further CDI development. The idea of business intelligence orientation and knowledge generation scenarios is also one of precious basis. At its turn, Information and Communication Technology (ICT)’s enablement will have strategic role for Indonesian enterprises growth and as a fundamental for ‘knowledge based economy’ in Indonesia.

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