Time-related Differentiation of Complexity Costs Using Process Data Mining

2020 ◽  
Author(s):  
G. Schuh ◽  
C. Dolle ◽  
M. Kuhn ◽  
J. Koch ◽  
A. Menges ◽  
...  
Keyword(s):  
Data Mining ◽  
Process Data

IMPLEMENTASI ALGORITMA K-Modes UNTUK MENENTUKAN STRATEGI MARKETING STMIK BUDI DARMA

2018 ◽  
Vol 2 (1) ◽  
Author(s):  
Ewin Karman Nduru ◽  
Efori Buulolo ◽  
Pristiwanto Pristiwanto
Keyword(s):  
High School ◽  
Data Mining ◽  
High School Students ◽  
Marketing Strategy ◽  
Process Data ◽  
School Students ◽  
The North ◽  
The Government ◽  
Using Data ◽  
North Sumatra

Universities or institutions that operate in North Sumatra are very many, therefore, of course, competition in accepting new students is very tight, universities or institutions do certain ways or steps to be able to compete with other campuses in gaining interest from community or high school students who will continue their studies to a higher level. STMIK BUDI DARMA Medan (College of Information and Computer Management), is the first computer high school in Medan which was established on March 1, 1996 and received approval from the government through the Minister of Education and Culture, on July 23, 1996 with operating license number 48 / D / O / 1996, in promoting the campus, the team usually formed a promotion team to various regions in the North Sumatra Region to provide information to the community. Students who have learned in this campus are quite a lot who come from various regions in North Sumatra, from this point the need to process data from students who are active in college to be processed using data mining to achieve a target, one method that can be used in data mining, namely the ¬K-Modes clustering (grouping) algorithm. This method is a grouping of student data that will be a help to campus students in promoting, using the K-Modes algorithm is expected to help and become a reference for marketing in determining the marketing strategy STMIK Budi Darma MedanKeywords: STMIK Budi Darma, Marketing Strategy, K-Modes Algorithm.

scholarly journals Market Basket Analysis for Determination of Consumer Behavior at XYZ Stores Using R Programming

2021 ◽  
Vol 3 (2) ◽  
pp. 0210206
Author(s):  
Kelik Sussolaikah
Keyword(s):  
Data Mining ◽  
Consumer Behavior ◽  
Market Basket Analysis ◽  
Process Data ◽  
Market Basket ◽  
Data Mining Techniques ◽  
Programming Tool ◽  
The World ◽  
R Programming

Data mining is one of the fields of science in the world of informatics which has an important role, especially with regard to data. There are many algorithms and methods that can be used to process data. The paper this time the author tries to conduct research on consumer behavior by using one of the data mining techniques, namely market basket analysis. This research uses the R Programming tool, where it is hoped that the research can be carried out effectively and efficiently. Based on the research conducted, it is known that there has been a significant purchase of several items that have been described as a plot. The tendency of consumers to buy several items followed by other items can be a consideration for arranging the layout of goods on the sales shelf or arranging product stock in a supermarket.

scholarly journals Privacy-Preserving Process Mining in Healthcare

2020 ◽  
Vol 17 (5) ◽  
pp. 1612 ◽  
Author(s):  
Anastasiia Pika ◽  
Moe T. Wynn ◽  
Stephanus Budiono ◽  
Arthur H.M. ter Hofstede ◽  
Wil M.P. van der Aalst ◽  
...  
Keyword(s):  
Data Mining ◽  
Data Privacy ◽  
Process Mining ◽  
Personal Data ◽  
Data Transformation ◽  
Privacy Preserving ◽  
Sensitive Information ◽  
Process Data ◽  
Mining Community ◽  
Healthcare Process

Process mining has been successfully applied in the healthcare domain and has helped to uncover various insights for improving healthcare processes. While the benefits of process mining are widely acknowledged, many people rightfully have concerns about irresponsible uses of personal data. Healthcare information systems contain highly sensitive information and healthcare regulations often require protection of data privacy. The need to comply with strict privacy requirements may result in a decreased data utility for analysis. Until recently, data privacy issues did not get much attention in the process mining community; however, several privacy-preserving data transformation techniques have been proposed in the data mining community. Many similarities between data mining and process mining exist, but there are key differences that make privacy-preserving data mining techniques unsuitable to anonymise process data (without adaptations). In this article, we analyse data privacy and utility requirements for healthcare process data and assess the suitability of privacy-preserving data transformation methods to anonymise healthcare data. We demonstrate how some of these anonymisation methods affect various process mining results using three publicly available healthcare event logs. We describe a framework for privacy-preserving process mining that can support healthcare process mining analyses. We also advocate the recording of privacy metadata to capture information about privacy-preserving transformations performed on an event log.

Acoustic Signature Based Weld Quality Monitoring for SMAW Process Using Data Mining Algorithms

2015 ◽  
Vol 813-814 ◽  
pp. 1104-1113 ◽  
Author(s):  
A. Sumesh ◽  
Dinu Thomas Thekkuden ◽  
Binoy B. Nair ◽  
K. Rameshkumar ◽  
K. Mohandas
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Welding Process ◽  
Machine Learning Algorithms ◽  
Steel Plates ◽  
Support Vector ◽  
Welding Parameters ◽  
Process Data ◽  
Data Mining Algorithms ◽  
Mining Algorithms

The quality of weld depends upon welding parameters and exposed environment conditions. Improper selection of welding process parameter is one of the important reasons for the occurrence of weld defect. In this work, arc sound signals are captured during the welding of carbon steel plates. Statistical features of the sound signals are extracted during the welding process. Data mining algorithms such as Naive Bayes, Support Vector Machines and Neural Network were used to classify the weld conditions according to the features of the sound signal. Two weld conditions namely good weld and weld with defects namely lack of fusion, and burn through were considered in this study. Classification efficiencies of machine learning algorithms were compared. Neural network is found to be producing better classification efficiency comparing with other algorithms considered in this study.

Challenges of Data Acquisition and Analysis for Characteristics-Driven and Metrology-Based Optimization of Milling Process Development

2014 ◽  
Vol 704 ◽  
pp. 233-238
Author(s):  
Laura Niendorf ◽  
Markus Grosse Boeckmann ◽  
Robert Schmitt
Keyword(s):  
Data Mining ◽  
Process Development ◽  
Early Stage ◽  
Milling Process ◽  
Production Environment ◽  
Process Data ◽  
Related Data ◽  
Research Activities ◽  
Future Production ◽  
Set Up

The research and practical use of data and data-mining in production environment is still at an early stage. Although almost every manufacturing company collects a lot of process and product related data they often do neither use nor deploy this data in order to optimize or even analyze their production processes. The acquisition of process data brings several advantages. On the one hand the implicit knowledge is permanently stored and on the other hand it is possible to learn from previous process failures. The acquired knowledge could then be applied to all future production tasks. Although many research activities can be observed since the late 90s, none of them managed the transfer to practical usage. In order to encourage the practical transfer of data-mining in production environment this paper presents a metrology-based test set-up and therewith arising challenges when consistently acquiring and processing inhomogeneous process, product and machine data. For the experimental set-up, on-machine metrology systems were developed and integrated into a 5-axis milling machine to gain much significant data.

scholarly journals Phaedra, a Protocol-Driven System for Analysis and Validation of High-Content Imaging and Flow Cytometry

2012 ◽  
Vol 17 (4) ◽  
pp. 496-506 ◽  
Author(s):  
Frans Cornelissen ◽  
Miroslav Cik ◽  
Emmanuel Gustin
Keyword(s):  
Data Mining ◽  
Flow Cytometry ◽  
High Throughput Screening ◽  
Complex Data ◽  
Process Data ◽  
User Friendliness ◽  
Response Curves ◽  
High Content Imaging ◽  
Advanced Analysis ◽  
Rich Data

High-content screening has brought new dimensions to cellular assays by generating rich data sets that characterize cell populations in great detail and detect subtle phenotypes. To derive relevant, reliable conclusions from these complex data, it is crucial to have informatics tools supporting quality control, data reduction, and data mining. These tools must reconcile the complexity of advanced analysis methods with the user-friendliness demanded by the user community. After review of existing applications, we realized the possibility of adding innovative new analysis options. Phaedra was developed to support workflows for drug screening and target discovery, interact with several laboratory information management systems, and process data generated by a range of techniques including high-content imaging, multicolor flow cytometry, and traditional high-throughput screening assays. The application is modular and flexible, with an interface that can be tuned to specific user roles. It offers user-friendly data visualization and reduction tools for HCS but also integrates Matlab for custom image analysis and the Konstanz Information Miner (KNIME) framework for data mining. Phaedra features efficient JPEG2000 compression and full drill-down functionality from dose-response curves down to individual cells, with exclusion and annotation options, cell classification, statistical quality controls, and reporting.

Part Data Mining for Information Re-Use in a PLM Context

2007 ◽  
Author(s):  
Omar Msaaf ◽  
Roland Maranzana ◽  
Louis Rivest
Keyword(s):  
Data Mining ◽  
Computer Aided Design ◽  
Product Lifecycle Management ◽  
Process Data ◽  
Information Process ◽  
Cad System ◽  
Related Information ◽  
Product And Process ◽  
Aided Design ◽  
Lifecycle Management

Difficulty in locating existing information in order to reuse it constitutes a major challenge to productivity. The use of PLM systems (Product Lifecycle Management) aims in particular to reduce the time and cost of developing a product by facilitating the re-use of existing parts or related information (process plans, tools, FEM, estimates, etc.). When information is alphanumerical, using search engines, such as those made popular on the internet, is efficient. However, a significant portion of information used in engineering rests within CAD (Computer Aided Design) models, making such search tools irrelevant. To aid in the re-use of information, two problems must be resolved: it is first necessary to be able to locate similar parts in the electronic database of the company, and then be able to systematically identify their differences. This article presents some of the results from our work on part, product and process data mining (P3DM). It focuses on tools developed to search similar 3D geometric models and to identify their differences. The PartFinder application locates similar parts by comparing signatures extracted from their solid representations. The 3DComparator aims to identify the differences in terms of Form and Fit between the identified parts. In both cases, the recommended approach is independent of the CAD system, and can also deal with parts represented by IGES or STEP files. Moreover, the approach does not require that the parts occupy the same position and have the same orientation in space. These two points, CAD and position independence, are the main benefits of our approach compared to other existing applications. Lastly, if the comparison takes place between two evolutions of the same geometrical representation of a part, a third tool allows the comparison of the specification trees. The SpecComparator is also presented briefly. An example based on industrial data illustrates the benefit that could be generated.

FROM DATA MINING TO BEHAVIOR MINING

2006 ◽  
Vol 05 (04) ◽  
pp. 703-711 ◽  
Author(s):  
ZHENGXIN CHEN
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Knowledge Economy ◽  
Graph Mining ◽  
Complex Data ◽  
Fundamental Change ◽  
Process Data ◽  
Research And Practice ◽  
Behavior Mining ◽  
Recent Developments

Knowledge economy requires data mining be more goal-oriented so that more tangible results can be produced. This requirement implies that the semantics of the data should be incorporated into the mining process. Data mining is ready to deal with this challenge because recent developments in data mining have shown an increasing interest on mining of complex data (as exemplified by graph mining, text mining, etc.). By incorporating the relationships of the data along with the data itself (rather than focusing on the data alone), complex data injects semantics into the mining process, thus enhancing the potential of making better contribution to knowledge economy. Since the relationships between the data reveal certain behavioral aspects underlying the plain data, this shift of mining from simple data to complex data signals a fundamental change to a new stage in the research and practice of knowledge discovery, which can be termed as behavior mining. Behavior mining also has the potential of unifying some other recent activities in data mining. We discuss important aspects on behavior mining, and discuss its implications for the future of data mining.

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