scholarly journals Data-Mining of Factors Affecting Circuit Connection Reliability on Laser-Drilled Micro Blind via Holes in Multi-Layer PWBs

2006 ◽  
Vol 49 (4) ◽  
pp. 522-528 ◽  
Author(s):  
Keiji OGAWA ◽  
Toshiki HIROGAKI ◽  
Eiichi AOYAMA ◽  
Shinji MAEDA ◽  
Hisahiro INOUE ◽  
...  
Keyword(s):  
Data Mining ◽  
Factors Affecting ◽  
Via Holes

scholarly journals Applying Improved Apriori Algorithm in Figuring out the Relation between Weather Factors and Rainfall

2020 ◽  
Vol 1 (1) ◽  
pp. 23-26
Author(s):  
Siti Zulaikha ◽  
Martaleli Bettiza ◽  
Nola Ritha
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Light Intensity ◽  
Apriori Algorithm ◽  
Weather Factors ◽  
Factors Affecting ◽  
Data Store ◽  
Repeated Pattern ◽  
Major Factors ◽  
Sun Light

Data on the rainfall is compelling to study as it becomes one of the major factors affecting the weather in a certain region and various aspects of life as well. Generally, predicting rainfall is performed by analyzing data in the past in certain methods. Rainfall is prone to follow repeated pattern in sequence of time. The utilization of big data mining is expected to result in any valuable information that used to be unrevealed in the big data store. Some methods used in data mining are Apriori Algorithm and Improved Apriori Algorithm. Improved Apriori itself is to represent the database in the form of matrix to describe its relation in the database. Data used in this research is the rainfall factor in 2016 in Tanjungpinang city. Based on the test of Improved Apriori Algorithm, it was found out that the relation of the rainfall and weather factors utilizing 2 item sets, that is, if the temperature is low (24,0 - 26,0), the humidity is high (85 - 100), then the rainfall is mild. If the temperature is low (24,0 - 26,0), the light intensity is low (0 – 3), then the rainfall is heavy, and 3 item sets if the temperature is low (24,0 - 26,0), the humidity is high (85 - 100), the sun light intensity is low (0-3), then the rainfall is medium.

scholarly journals FACTORS AFFECTING THE CUSTOMS CLEARANCE TIME AT PRIME CUSTOMS OFFICE TYPE A OF TANJUNG PRIOK

2020 ◽  
Vol 2 (2) ◽  
pp. 01-17
Author(s):  
Khamami Herusantoso ◽  
Ardyanto Dwi Saputra
Keyword(s):  
Data Mining ◽  
Type A ◽  
Database Analysis ◽  
Data Set ◽  
Complex Phase ◽  
Specific Data ◽  
Factors Affecting ◽  
Efficiency And Effectiveness ◽  
Hidden Knowledge ◽  
Clearance Process

In the dwell-time, the customs clearance is considered as the most complex phase, even though its portion is the shortest among other phases, such as pre-clearance and post clearance. In order to improve the efficiency and effectiveness on the services performed in the customs clearance process, the customs authorities must start considering the help of database analysis in identifying obstacles instead of depending on the personal analysis. Useful information is hidden among the importation data set and it is extractable through data mining techniques. This study explores the customs clearance process of import cargo whose document is declared through the red channel at Prime Customs Office Type A of Tanjung Priok (PCO Tanjung Priok), and applies a specific data mining classifier called the decision tree with J48 algorithm to evaluate the process. There are 11 classification models developed using unpruned, online pruning, and post-pruning features. One best model is chosen to extract the hidden knowledge that describes factors affecting the customs clearance process and allows the customs authorities to improve their services performed in the future.

scholarly journals Research on Factors Affecting Real Estate Values by Data Mining

2018 ◽  
Vol 6 (1) ◽  
pp. 220-239 ◽  
Author(s):  
Filiz Ersoz ◽  
Taner Ersoz ◽  
Muhammet Soydan
Keyword(s):  
Data Mining ◽  
Real Estate ◽  
Statistical Modelling ◽  
Unit Price ◽  
City Centre ◽  
Important Place ◽  
Factors Affecting ◽  
The Real ◽  
The Real Estate ◽  
Real Estate Valuation

Abstract Construction sector has an important place in Turkey’s economy. Real estate sales for the sector are increasing in parallel. However, the purchase cost is also important for those who are willing to buy a real estate. In the acquisition of real estate, factors such as size, location and age of the house are taken into consideration. The aim of the article is to conduct research on factors affecting real estate values by data mining. In this study, the most important variables that determine the value of the real estate have been investigated by data mining methods. The research has been carried out in Karabük and the variables determined according to the opinions of real estate experts. As classification methods, CHAID and C&RT algorithms have been used. It has been evaluated that both algorithm estimation results can be used. Within the framework of the study, the variables that have the most impact on the unit price have been determined, such as the size of the real estate, the distance to the city centre, the popularity, and the age of the building. The use of advanced technologies, such as statistical modelling and machine learning in real estate valuation and automatic value estimation, is of importance in determining the real value of the real estate.

Predicting the Academic Performance of Students Using Utility-Based Data Mining

2020 ◽  
pp. 56-85
Author(s):  
Sidath R. Liyanage ◽  
K. T. Sanvitha Kasthuriarachchi
Keyword(s):  
Data Mining ◽  
Academic Performance ◽  
Tertiary Education ◽  
Analytical Framework ◽  
Machine Learning Algorithms ◽  
Educational Statistics ◽  
Factors Affecting ◽  
Educational Institute ◽  
Main Factors ◽  
Sphere Of Influence

Data mining in education has become an important topic in the sphere of influence of data mining. Mining educational data encompasses developing models, plotting data, and utilizing machine learning algorithms to derive patterns on educational data by attempting to uncover hidden patterns, create information for hidden relationships using educational statistics, and perform many more operations that are unfeasible using traditional computational tools. This research aims to identify the main factors that influence the academic performance of learners in tertiary education system in Sri Lanka. A conceptual framework and an analytical framework on factors affecting the academic performance was constructed with this aim. The analytical framework was then validated with the data collected from technology learners in a tertiary educational institute.

Quality Control of Micro-Drilled Hole Wall of PWBs Using Data-Mining

2005 ◽  
Author(s):  
Eiichi Aoyama ◽  
Toshiki Hirogaki ◽  
Keiji Ogawa ◽  
Tsuyoshi Otsuka ◽  
Katsutoshi Yamauchi
Keyword(s):  
Data Mining ◽  
Surface Roughness ◽  
Present Report ◽  
Weft Yarn ◽  
Hole Drilling ◽  
Printed Wiring Boards ◽  
Factors Affecting ◽  
Hole Wall ◽  
Using Data ◽  
Through Hole

In the manufacturing of printed wiring boards (PWBs), various methods have been developed in order to improve the circuit packaging density. Micro-drills are generally used to make smaller diameter through-holes in PWBs, which are desired for the miniaturization of equipment. However, a problem has emerged in that copper plating degraded by hole drilling can reduce the reliability of the electrical connection between layers. The surface roughness of drilled hole wall is one of the important factors affecting the plating quality. The purpose of the present report is to apply data-mining to the surface roughness data of drilled through-hole walls, and to elucidate the factors required to control the drilled hole quality. The following conclusions were obtained. (1) The data-mining aided by a computer was found to be effective to control the drilled hole wall quality in the PWBs manufacturing. (2) It was clear that the surface roughness of drilled hole walls depended on three factors: the drill temperature, cutting distance, and the width of the fiber bundle of weft yarn.

scholarly journals Investigating factors affecting library visits by university students using data mining

2018 ◽  
Vol 28 (1) ◽  
Author(s):  
Puarungroj Wichai ◽  
Pongpatrakant Pathapong ◽  
Boonsirisumpun Narong ◽  
Phromkhot Suchada
Keyword(s):  
Data Mining ◽  
University Students ◽  
Factors Affecting ◽  
Using Data

scholarly journals Role of FCBF Feature Selection in Educational Data Mining

2020 ◽  
Vol 39 (4) ◽  
pp. 772-778
Author(s):  
Maryam Zaffar ◽  
Manzoor Ahmad Hashmani ◽  
K.S. Savita ◽  
Syed Sajjad Hussain Rizvi ◽  
Mubashar Rehman
Keyword(s):  
Data Mining ◽  
Feature Selection ◽  
Prediction Model ◽  
Student Performance ◽  
Performance Prediction ◽  
Prediction Models ◽  
Educational Data Mining ◽  
Action Plans ◽  
Factors Affecting ◽  
Academic Organization

The Educational Data Mining (EDM) is a very vigorous area of Data Mining (DM), and it is helpful in predicting the performance of students. Student performance prediction is not only important for the student but also helpful for academic organization to detect the causes of success and failures of students. Furthermore, the features selected through the students’ performance prediction models helps in developing action plans for academic welfare. Feature selection can increase the prediction accuracy of the prediction model. In student performance prediction model, where every feature is very important, as a neglection of any important feature can cause the wrong development of academic action plans. Moreover, the feature selection is a very important step in the development of student performance prediction models. There are different types of feature selection algorithms. In this paper, Fast Correlation-Based Filter (FCBF) is selected as a feature selection algorithm. This paper is a step on the way to identifying the factors affecting the academic performance of the students. In this paper performance of FCBF is being evaluated on three different student’s datasets. The performance of FCBF is detected well on a student dataset with greater no of features.

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