REFII Model as a Base for Data Mining Techniques Hybridization with Purpose of Time Series Pattern Recognition

2015 ◽  
pp. 237-270 ◽  
Author(s):  
Goran Klepac ◽  
Robert Kopal ◽  
Leo Mršić
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Pattern Recognition ◽  
Data Mining Techniques

scholarly journals Particularities of data mining in medicine: lessons learned from patient medical time series data analysis

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Shadi Aljawarneh ◽  
Aurea Anguera ◽  
John William Atwood ◽  
Juan A. Lara ◽  
David Lizcano
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Knowledge Discovery ◽  
Time Series Data ◽  
Medical Patient ◽  
Lessons Learned ◽  
Physiological Signals ◽  
Knowledge Discovery In Databases ◽  
Series Data ◽  
Data Mining Techniques

AbstractNowadays, large amounts of data are generated in the medical domain. Various physiological signals generated from different organs can be recorded to extract interesting information about patients’ health. The analysis of physiological signals is a hard task that requires the use of specific approaches such as the Knowledge Discovery in Databases process. The application of such process in the domain of medicine has a series of implications and difficulties, especially regarding the application of data mining techniques to data, mainly time series, gathered from medical examinations of patients. The goal of this paper is to describe the lessons learned and the experience gathered by the authors applying data mining techniques to real medical patient data including time series. In this research, we carried out an exhaustive case study working on data from two medical fields: stabilometry (15 professional basketball players, 18 elite ice skaters) and electroencephalography (100 healthy patients, 100 epileptic patients). We applied a previously proposed knowledge discovery framework for classification purpose obtaining good results in terms of classification accuracy (greater than 99% in both fields). The good results obtained in our research are the groundwork for the lessons learned and recommendations made in this position paper that intends to be a guide for experts who have to face similar medical data mining projects.

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 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.

scholarly journals Exploiting Data Mining Techniques For Improving the Efficiency of Time Series Data

2014 ◽  
pp. 284-293
Author(s):  
TARUN DHAR DIWAN ◽  
PRADEEP CHOUKSEY ◽  
R. S. THAKUR ◽  
BHARAT LODHI
Keyword(s):  
Data Mining ◽  
Time Series ◽  
High Performance ◽  
Time Series Data ◽  
Research Work ◽  
Visual Programming ◽  
Series Data ◽  
Data Mining Techniques ◽  
Data Mining Algorithms ◽  
Vantage Points

The research work in data mining has achieved a high attraction due to the importance of its applications This paper addresses some theoretical and practical aspects on Exploiting Data Mining Techniques for Improving the Efficiency of Time Series Data using SPSS-CLEMENTINE. This paper can be helpful for an organization or individual when choosing proper software to meet their mining needs. In this paper, we propose utilizes the famous data mining software SPSS Clementine to mine the factors that affect information from various vantage points and analyse that information. However the purpose of this paper is to review the selected software for data mining for improving efficiency of time series data. Data mining techniques is the exploration and analysis of data in order to discover useful information from huge databases. So it is used to analyse a large audit data efficiently for Improving the Efficiency of Time Series Data. SPSS- Clementine is object-oriented, extended module interface, which allows users to add their own algorithms and utilities to Clementine’s visual programming environment. The overall objective of this research is to develop high performance data mining algorithms and tools that will provide support required to analyse the massive data sets generated by various processes that is used for predicting time series data using SPSS- Clementine. The aim of this paper is to determine the feasibility and effectiveness of data mining techniques in time series data and produce solutions for this purpose.

scholarly journals Forecasting Spare Parts Demand of Military Aircraft: Comparisons of Data Mining Techniques and Managerial Features from the Case of South Korea

2020 ◽  
Vol 12 (15) ◽  
pp. 6045
Author(s):  
Boram Choi ◽  
Jong Hwan Suh
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Random Forest ◽  
South Korea ◽  
Demand Forecasting ◽  
Spare Parts ◽  
Weapon System ◽  
Military Aircraft ◽  
Data Mining Techniques ◽  
The Military

In a weapon system, the accurate forecasting of the spare parts demand can help avoid the excess inventory, leading to the efficient use of budget. It can also help develop the combat readiness of the weapon system by improving weapon system utilization. Moreover, as performance-based logistics (PBL) projects have recently emerged, the accurate demand forecasting of spare parts has become an important issue for the PBL contractors as well. However, for the demand forecasting of spare parts, the time series methods, typically used in the military sector, have low prediction accuracies and the PBL contractors are mostly based on the judgment of practitioners. Meanwhile, most of the previous studies in the military sector have not considered the managerial characteristics of spare parts (e.g., reparability and the irregularity of maintenance). No previous work has considered any such features, which can indicate the reliability of spare parts (e.g., mean time between failures (MTBF)), although they can affect the spare parts demand. Therefore, to develop a more accurate forecasting of the spare parts demand of military aircraft, we designed and examined a systematic approach that uses data mining techniques. To fill up the research gaps of related works, our approach also considered the managerial characteristics of spare parts and included the new features that represent the reliability of spare parts. Consequently, given the case of South Korea and the full feature set, we found random forest gave better results than the other data mining techniques and the conventional time series methods. Using the best technique Random Forest, we identified the contribution of each managerial feature set to improving the prediction accuracy, and we found the reliability and operation environment are valuable feature sets in a significant way, so they should be collected, managed more carefully, and included for better prediction of spare parts demand of military aircraft.

Development of Data Mining Driven Software Tool to Forecast the Customer Requirement for Quality Function Deployment

2018 ◽  
pp. 625-658
Author(s):  
Shivani K. Purohit ◽  
Ashish K. Sharma
Keyword(s):  
Data Mining ◽  
Time Series ◽  
Comparative Analysis ◽  
Time Series Analysis ◽  
Product Development ◽  
Quality Function Deployment ◽  
Software Tool ◽  
Quality Function ◽  
Data Mining Techniques ◽  
Using Data

Quality Function Deployment (QFD) is widely used customer driven process for product development. Thus, Customer Requirements (CRs) play a key role in QFD process. However, the diversification in marketplace makes these CRs more dynamic and changing, giving rise the need to forecast CRs to improve competitiveness and increase customer satisfaction. The purpose can be served by using Data Mining techniques of forecasting. With the pool of forecasting techniques available, it is important to evaluate a suitable one for more effective results. To this end, the paper presents a novel software tool to efficiently forecast CRs in QFD. The tool allows for forecasting using various data mining based time series analysis techniques that strongly assists in doing comparative analysis and evaluating out the most apt technique for forecasting of CRs. The tool is developed using VB.Net and MS-Access. Finally, an example is presented to demonstrate the practicability of proposed software tool.

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