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.

Grid-Based Platform for Mining Association Rules

2010 ◽  
Vol 34-35 ◽  
pp. 1961-1965
Author(s):  
You Qu Chang ◽  
Guo Ping Hou ◽  
Huai Yong Deng
Keyword(s):  
Data Mining ◽  
Large Datasets ◽  
Distributed Data Mining ◽  
Distributed Data ◽  
Data Set ◽  
Efficiency And Effectiveness ◽  
Geographically Distributed ◽  
Commercial Applications ◽  
Rule Interestingness ◽  
Grid Based

distributed data mining is widely used in industrial and commercial applications to analyze large datasets maintained over geographically distributed sites. This paper discusses the disadvantages of existing distributed data mining systems, and puts forward a distributed data mining platform based grid computing. The experiments done on a data set showed that the proposed approach produces meaningful results and has reasonable efficiency and effectiveness providing a trade-off between runtime and rule interestingness.

Knowledge discovery in sociological databases

2019 ◽  
Vol 3 (3) ◽  
pp. 315-332
Author(s):  
Zhiwen Pan ◽  
Jiangtian Li ◽  
Yiqiang Chen ◽  
Jesus Pacheco ◽  
Lianjun Dai ◽  
...  
Keyword(s):  
Data Mining ◽  
Data Management ◽  
Clustering Analysis ◽  
Attribute Selection ◽  
Data Set ◽  
Classification Analysis ◽  
Content Type ◽  
Hidden Knowledge ◽  
Data Mining Analysis

Purpose The General Society Survey(GSS) is a kind of government-funded survey which aims at examining the Socio-economic status, quality of life, and structure of contemporary society. GSS data set is regarded as one of the authoritative source for the government and organization practitioners to make data-driven policies. The previous analytic approaches for GSS data set are designed by combining expert knowledges and simple statistics. By utilizing the emerging data mining algorithms, we proposed a comprehensive data management and data mining approach for GSS data sets. Design/methodology/approach The approach are designed to be operated in a two-phase manner: a data management phase which can improve the quality of GSS data by performing attribute pre-processing and filter-based attribute selection; a data mining phase which can extract hidden knowledge from the data set by performing data mining analysis including prediction analysis, classification analysis, association analysis and clustering analysis. Findings According to experimental evaluation results, the paper have the following findings: Performing attribute selection on GSS data set can increase the performance of both classification analysis and clustering analysis; all the data mining analysis can effectively extract hidden knowledge from the GSS data set; the knowledge generated by different data mining analysis can somehow cross-validate each other. Originality/value By leveraging the power of data mining techniques, the proposed approach can explore knowledge in a fine-grained manner with minimum human interference. Experiments on Chinese General Social Survey data set are conducted at the end to evaluate the performance of our approach.

scholarly journals DFC

2021 ◽  
Vol 11 (2) ◽  
pp. 38-62
Author(s):  
Nida Meddouri ◽  
Hela Khoufi ◽  
Mondher Maddouri
Keyword(s):  
Data Mining ◽  
Ensemble Learning ◽  
Ensemble Methods ◽  
Large Data ◽  
Support Vector ◽  
Formal Concept ◽  
Data Set ◽  
Research Theme ◽  
Combination Of Classifiers ◽  
Hidden Knowledge

Knowledge discovery data (KDD) is a research theme evolving to exploit a large data set collected every day from various fields of computing applications. The underlying idea is to extract hidden knowledge from a data set. It includes several tasks that form a process, such as data mining. Classification and clustering are data mining techniques. Several approaches were proposed in classification such as induction of decision trees, Bayes net, support vector machine, and formal concept analysis (FCA). The choice of FCA could be explained by its ability to extract hidden knowledge. Recently, researchers have been interested in the ensemble methods (sequential/parallel) to combine a set of classifiers. The combination of classifiers is made by a vote technique. There has been little focus on FCA in the context of ensemble learning. This paper presents a new approach to building a single part of the lattice with best possible concepts. This approach is based on parallel ensemble learning. It improves the state-of-the-art methods based on FCA since it handles more voluminous data.

scholarly journals Attribute Reduction With Imputation Of Missing Data Using Fuzzy-Rougsh Set

2019 ◽  
Vol 8 (11) ◽  
pp. 202-207
Keyword(s):  
Data Mining ◽  
Missing Data ◽  
Attribute Reduction ◽  
Real Life ◽  
Data Imputation ◽  
Fuzzy Rough Set ◽  
Data Set ◽  
Missing Data Imputation ◽  
Real Life Data ◽  
Efficiency And Effectiveness

Attribute Reduction and missing data imputation have considerable influence in classification or other data mining task. New hybridization methodology like fuzzy rough set is more robust method to deal with imprecision and uncertainty for discrete as well as continuous data. Fuzzy rough attribute reduction with imputation (FRARI) algorithm has been proposed for attribute reduction with missing value imputation. So using FRARI algorithm complete reduce data set can be generated which has a great importance in different branches of artificial intelligence for data mining from databases. Efficiency and effectiveness of the proposed algorithm has been shown by experiment with real life data set.

Regional, institutional and individual factors affecting selection of minimally invasive nephroureterectomy in Japan: A national database analysis

2012 ◽  
Vol 20 (7) ◽  
pp. 695-700 ◽  
Author(s):  
Toru Sugihara ◽  
Hideo Yasunaga ◽  
Hiromasa Horiguchi ◽  
Tetsuya Fujimura ◽  
Hiroaki Nishimatsu ◽  
...  
Keyword(s):  
Minimally Invasive ◽  
National Database ◽  
Individual Factors ◽  
Database Analysis ◽  
Factors Affecting ◽  
Selection Of

scholarly journals Facing the needs for clean bicycle data – a bicycle-specific approach of GPS data processing

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Sven Lißner ◽  
Stefan Huber
Keyword(s):  
Data Processing ◽  
Gps Data ◽  
Data Set ◽  
Specific Data ◽  
Driving Mode ◽  
Mode Detection ◽  
Current State ◽  
Mode Recognition ◽  
Recorded Data ◽  
Gps Tracks

Abstract Background GPS-based cycling data are increasingly available for traffic planning these days. However, the recorded data often contain more information than simply bicycle trips. GPS tracks resulting from tracking while using other modes of transport than bike or long periods at working locations while people are still tracking are only some examples. Thus, collected bicycle GPS data need to be processed adequately to use them for transportation planning. Results The article presents a multi-level approach towards bicycle-specific data processing. The data processing model contains different steps of processing (data filtering, smoothing, trip segmentation, transport mode recognition, driving mode detection) to finally obtain a correct data set that contains bicycle trips, only. The validation reveals a sound accuracy of the model at its’ current state (82–88%).

Increasing revenue and attendance in Canadian Baptist churches

2015 ◽  
Vol 42 (12) ◽  
pp. 1071-1089
Author(s):  
Alan Chan ◽  
Bruce G. Fawcett ◽  
Shu-Kam Lee
Keyword(s):  
Church Health ◽  
Eastern Canada ◽  
Data Set ◽  
Content Type ◽  
Factors Affecting ◽  
Scarce Resources ◽  
Baptist Churches ◽  
And Performance ◽  
The Impact ◽  
Simulation Exercises

Purpose – Church giving and attendance are two important indicators of church health and performance. In the literature, they are usually understood to be simultaneously determined. The purpose of this paper is to estimate if there a sustainable church congregation size using Wintrobe’s (1998) dictatorship model. The authors want to examine the impact of youth and adult ministry as well. Design/methodology/approach – Using the data collected from among Canadian Baptist churches in Eastern Canada, this study investigates the factors affecting the level of the two indicators by the panel-instrumental variable technique. Applying Wintrobe’s (1998) political economy model on dictatorship, the equilibrium level of worship attendance and giving is predicted. Findings – Through various simulation exercises, the actual church congregation sizes is approximately 50 percent of the predicted value, implying inefficiency and misallocation of church resources. The paper concludes with insights on effective ways church leaders can allocate scarce resources to promote growth within churches. Originality/value – The authors are the only researchers getting the permission from the Atlantic Canada Baptist Convention to use their mega data set on church giving and congregation sizes as per the authors’ knowledge. The authors are also applying a theoretical model on dictatorship to religious/not for profits organizations.

A Meta-Mining Ontology Framework for Data Processing

2021 ◽  
Vol 12 (2) ◽  
pp. 37-56
Author(s):  
Man Tianxing ◽  
Nataly Zhukova ◽  
Alexander Vodyaho ◽  
Tin Tun Aung
Keyword(s):  
Data Mining ◽  
Data Processing ◽  
Data Streams ◽  
Specific Data ◽  
Ontology Merging ◽  
Knowledge Models ◽  
Task Requirements ◽  
Ontology Extraction ◽  
Merging Method ◽  
And Task

Extracting knowledge from data streams received from observed objects through data mining is required in various domains. However, there is a lack of any kind of guidance on which techniques can or should be used in which contexts. Meta mining technology can help build processes of data processing based on knowledge models taking into account the specific features of the objects. This paper proposes a meta mining ontology framework that allows selecting algorithms for solving specific data mining tasks and build suitable processes. The proposed ontology is constructed using existing ontologies and is extended with an ontology of data characteristics and task requirements. Different from the existing ontologies, the proposed ontology describes the overall data mining process, used to build data processing processes in various domains, and has low computational complexity compared to others. The authors developed an ontology merging method and a sub-ontology extraction method, which are implemented based on OWL API via extracting and integrating the relevant axioms.

An Experimental Study of Spammer Detection on Chinese Microblogs

2020 ◽  
Vol 30 (11n12) ◽  
pp. 1759-1777
Author(s):  
Jialing Liang ◽  
Peiquan Jin ◽  
Lin Mu ◽  
Jie Zhao
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
User Behavior ◽  
Real Data ◽  
User Profile ◽  
Data Set ◽  
Sina Weibo ◽  
Factors Affecting ◽  
The Government ◽  
Hot Event

With the development of Web 2.0, social media such as Twitter and Sina Weibo have become an essential platform for disseminating hot events. Simultaneously, due to the free policy of microblogging services, users can post user-generated content freely on microblogging platforms. Accordingly, more and more hot events on microblogging platforms have been labeled as spammers. Spammers will not only hurt the healthy development of social media but also introduce many economic and social problems. Therefore, the government and enterprises must distinguish whether a hot event on microblogging platforms is a spammer or is a naturally-developing event. In this paper, we focus on the hot event list on Sina Weibo and collect the relevant microblogs of each hot event to study the detecting methods of spammers. Notably, we develop an integral feature set consisting of user profile, user behavior, and user relationships to reflect various factors affecting the detection of spammers. Then, we employ typical machine learning methods to conduct extensive experiments on detecting spammers. We use a real data set crawled from the most prominent Chinese microblogging platform, Sina Weibo, and evaluate the performance of 10 machine learning models with five sampling methods. The results in terms of various metrics show that the Random Forest model and the over-sampling method achieve the best accuracy in detecting spammers and non-spammers.

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