Pattern recognition and data mining techniques to identify factors in wafer processing and control determining overlay error

2015 ◽  
Author(s):  
Auguste Lam ◽  
Alexander Ypma ◽  
Maxime Gatefait ◽  
David Deckers ◽  
Arne Koopman ◽  
...  
Keyword(s):  
Data Mining ◽  
Pattern Recognition ◽  
Data Mining Techniques ◽  
Wafer Processing ◽  
And Control

scholarly journals Using data mining techniques to fight and control epidemics: A scoping review

2021 ◽  
Author(s):  
Reza Safdari ◽  
Sorayya Rezayi ◽  
Soheila Saeedi ◽  
Mozhgan Tanhapour ◽  
Marsa Gholamzadeh
Keyword(s):  
Data Mining ◽  
Scoping Review ◽  
Data Mining Techniques ◽  
Using Data ◽  
And Control

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.

Web based Privacy Disclosure Threats and Control Techniques

2016 ◽  
pp. 334-341
Author(s):  
Tithi Hunka ◽  
Sital Dash ◽  
Prasant Kumar Pattnaik
Keyword(s):  
Data Mining ◽  
Privacy Preserving ◽  
Extensive Study ◽  
Security And Privacy ◽  
Sensitive Data ◽  
Privacy Preserving Data Mining ◽  
Web Based ◽  
Data Mining Techniques ◽  
The Public ◽  
And Control

Due to advancement of internet technologies, web based applications are gaining popularity day by day. Many organizations maintain large volumes of web site based data about individuals that may carry information that cannot be revealed to the public or researchers. While web-based applications are becoming increasingly pervasive by nature, they also present new security and privacy challenges. However, privacy threats effects negatively on sensitive data and possibly leads to the leakage of confidential information. More ever, privacy preserving data mining techniques allow us to protect the sensitive data before it gets published to the public by changing the original micro-data format and contents. This chapter is intended to undertake an extensive study on some ramified disclosure threats to the privacy and PPDM (privacy preserving data mining) techniques as a unified solution to protect against threats.

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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