Data to Knowledge-Based Transformation

2021 ◽  
Vol 13 (4) ◽  
pp. 141-152
Author(s):  
Fauzan Asrin ◽  
*Saide Saide ◽  
Silvia Ratna
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Industrial Sector ◽  
Growth Patterns ◽  
Advantages And Disadvantages ◽  
Knowledge Based ◽  
Data Mining Approach ◽  
Important Evidence ◽  
Confidence Value ◽  
Computer Maintenance

The objectives of this study is to analyze a large amount of data that often appears to create a knowledge base that can be utilized by firm to enhance their decision support system. The authors used the association rules with rapid miner software, data mining approach, and predictive analysis that contains various data exploration scenarios. The study provides important evidence for adopting data mining methods in the industrial sector and their advantages and disadvantages. Chevron Pacific Indonesia (CPI) has a type of computer maintenance activity. Currently, a numerous errors often occur due to the accuracy in computer maintenance which has a major impact on production results. Therefore, this study focuses on association rules using growth patterns that often appear on variables that have been determined into the algorithm (FP-growth) which results in knowledge with a 100% confidence value and a 97% support value. The value results of this study has support and trust are expected to become knowledge for top management in deciding evergreen IT-business routines.

scholarly journals Data Mining as Support to Knowledge Management in Marketing

2015 ◽  
Vol 6 (2) ◽  
pp. 18-30 ◽  
Author(s):  
Marijana Zekić-Sušac ◽  
Adela Has
Keyword(s):  
Data Mining ◽  
Neural Networks ◽  
Knowledge Management ◽  
Association Rules ◽  
Marketing Strategies ◽  
Web Based ◽  
Data Mining Techniques ◽  
Data Mining Approach ◽  
Model Generalization ◽  
Retail Managers

Abstract Background: Previous research has shown success of data mining methods in marketing. However, their integration in a knowledge management system is still not investigated enough. Objectives: The purpose of this paper is to suggest an integration of two data mining techniques: neural networks and association rules in marketing modeling that could serve as an input to knowledge management and produce better marketing decisions. Methods/Approach: Association rules and artificial neural networks are combined in a data mining component to discover patterns and customers’ profiles in frequent item purchases. The results of data mining are used in a web-based knowledge management component to trigger ideas for new marketing strategies. The model is tested by an experimental research. Results: The results show that the suggested model could be efficiently used to recognize patterns in shopping behaviour and generate new marketing strategies. Conclusions: The scientific contribution lies in proposing an integrative data mining approach that could present support to knowledge management. The research could be useful to marketing and retail managers in improving the process of their decision making, as well as to researchers in the area of marketing modelling. Future studies should include more samples and other data mining techniques in order to test the model generalization ability.

scholarly journals Data Mining Approach for Knowledge-based Process Planning

2014 ◽  
Vol 15 ◽  
pp. 406-415 ◽  
Author(s):  
Berend Denkena ◽  
Justin Schmidt ◽  
Max Krüger
Keyword(s):  
Data Mining ◽  
Process Planning ◽  
Knowledge Based ◽  
Data Mining Approach

scholarly journals IMPLEMENTASI DATA MINING UNTUK MENGETAHUI POLA PEMBELIAN OBAT MENGGUNAKAN ALGORITMA APRIORI

2020 ◽  
Vol 17 (2) ◽  
pp. 396-402
Author(s):  
Nadya Febrianny Ulfha ◽  
Ruhul Amin
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
A Priori ◽  
Marketing Strategies ◽  
Business World ◽  
Association Pattern ◽  
Green Lake ◽  
Purchasing Patterns ◽  
The Relationship ◽  
Confidence Value

Competition in the business world requires entrepreneurs to think of finding a way or method to increase the transaction of goods sold. The purpose of this research is to provide drug stock data that is widely purchased by pharmacy customers at Kimia Farma, Green Lake branch in Jakarta. The algorithm used in this study is a priori to determine the relationship between the frequency of sales of drug brands most frequently purchased by customers. The association pattern formed with a minimum support of 40% and a minimum value of 70% confidence produces 17 association rules. The strong rules obtained are that if you buy a 500Mg Ponstan KPL @ 100, you will buy an Incidal OD 10Mg Cap with a support value of 59% and a confidence value of 84%. A priori algorithm can be used by companies to develop marketing strategies in marketing products by examining consumer purchasing patterns.

Meta-Knowledge Based Approach for an Interactive Visualization of Large Amounts of Association Rules

2009 ◽  
pp. 200-223
Author(s):  
Sadok Ben Yahia ◽  
Olivier Couturier ◽  
Tarek Hamrouni ◽  
Engelbert Mephu Nguifo
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Efficient Solution ◽  
Explicit Knowledge ◽  
Interactive Visualization ◽  
Information Loss ◽  
Compact Representation ◽  
Knowledge Based ◽  
Extraction Step ◽  
Visualization Techniques

Providing efficient and easy-to-use graphical tools to users is a promising challenge of data mining, especially in the case of association rules. These tools must be able to generate explicit knowledge and, then, to present it in an elegant way. Visualization techniques have shown to be an efficient solution to achieve such a goal. Even though considered as a key step in the mining process, the visualization step of association rules received much less attention than that paid to the extraction step. Nevertheless, some graphical tools have been developed to extract and visualize association rules. In those tools, various approaches are proposed to filter the huge number of association rules before the visualization step. However both data mining steps (association rule extraction and visualization) are treated separately in a one way process. Recently different approaches have been proposed that use meta-knowledge to guide the user during the mining process. Standing at the crossroads of Data Mining and Human-Computer Interaction, those approaches present an integrated framework covering both steps of the data mining process. This chapter describes and discusses such approaches. Two approaches are described in details: the first one builds a roadmap of compact representation of association rules from which the user can explore generic bases of association rules and derive, if desired, redundant ones without information loss. The second approach clusters the set of association rules or its generic bases, and uses a fisheye view technique to help the user during the mining of association rules. Generic bases with their links or the associated clusters constitute the meta-knowledge used to guide the interactive and cooperative visualization of association rules.

scholarly journals The influence of active region information on the prediction of solar flares: an empirical model using data mining

2005 ◽  
Vol 23 (9) ◽  
pp. 3129-3138 ◽  
Author(s):  
M. Núñez ◽  
R. Fidalgo ◽  
M. Baena ◽  
R. Morales
Keyword(s):  
Data Mining ◽  
Active Region ◽  
Solar Flares ◽  
Behavior Pattern ◽  
Pattern Learning ◽  
Data Mining Approach ◽  
Region Data ◽  
Using Data ◽  
Confidence Value ◽  
Region Information

Abstract. Predicting the occurrence of solar flares is a challenge of great importance for many space weather scientists and users. We introduce a data mining approach, called Behavior Pattern Learning (BPL), for automatically discovering correlations between solar flares and active region data, in order to predict the former. The goal of BPL is to predict the interval of time to the next solar flare and provide a confidence value for the associated prediction. The discovered correlations are described in terms of easy-to-read rules. The results indicate that active region dynamics is essential for predicting solar flares.

scholarly journals Penerapan Metode Data Mining Pada Point of Sale Berbasis Web Menggunakan Algoritma Apriori

2021 ◽  
Vol 5 (3) ◽  
pp. 1158
Author(s):  
Adam Firmansyah ◽  
M Iwan Wahyudin ◽  
Ben Rahman
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
A Priori ◽  
Point Of Sale ◽  
Minimum Support ◽  
Confidence Value

To be able to understand which products have been purchased by customers, it is done by describing the habits when customers buy. Use association rules to detect items purchased at the same time. This study uses an a priori algorithm to determine the association rules when buying goods. The results of the study and analyzing the data obtained a statement that using the a priori algorithm to select the combined itemset using a minimum support of 25% and a minimum confidence of 100%, found the association rule, namely, if the customer buys at the same time. Buying goods has the highest value of support and trust. Likewise with the support value of 25%, the confidence value is 100%. In this way, if a customer buys an item, the probability that the customer buys the item is 100%

scholarly journals Implementation Of Data Mining On Suzuki Motorcycle Sales In Gemilang Motor Prosperous With Apriori Algorithm Method

2020 ◽  
Vol 2 (1) ◽  
pp. 23-29
Author(s):  
Anju Eliarsyam Lubis ◽  
Paska Marto Hasugian
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Strong Correlation ◽  
High Frequency ◽  
Efficient Algorithm ◽  
Pattern Analysis ◽  
Apriori Algorithm ◽  
Frequency Pattern ◽  
One Stage ◽  
Confidence Value

The sale is part of the marketing that determine the survival of the company. With the sale, the company can achieve the goals or targets. To be a company that continues to grow in motorcycle sales, the company should be able to compete in increasing sales volume. Starting from the launch prodak the best in sophistication motorcycles, up to a very attractive price cuts the attention of consumers. Things like that already sanggat often do, so the company can still compete, Motorcycles is a two-wheeled transfortasi tool used more and more common people. From teenagers to old orag, not infrequently motorcycle including important sanggat needs. If we do not have it feels very hard in activity quickly. Make sales without any restriction of sales data accumulate, until finally overwhelmed the company in terms of taking care of customer files. To find the most sales required Apriori Algorithm. Apriori algorithm, including the type of association rules on Data Mining. One stage of association that can produce an efficient algorithm is with high frequency pattern analysis. In an association can be determined by two benchmarks, namely: Support and Confidence. Support "penunang value" is the percentage of combinations of items in a database, and Confidence "value certainty" is strong correlation between the items in an association's rules. Apriori algorithm, including the type of association rules on Data Mining. One stage of association that can produce an efficient algorithm is with high frequency pattern analysis. In an association can be determined by two benchmarks, namely: Support and Confidence. Support "penunang value" is the percentage of combinations of items in a database, and Confidence "value certainty" is strong correlation between the items in an association's rules. Apriori algorithm, including the type of association rules on Data Mining. One stage of association that can produce an efficient algorithm is with high frequency pattern analysis. In an association can be determined by two benchmarks, namely: Support and Confidence. Support "penunang value" is the percentage of combinations of items in a database, and Confidence "value certainty" is strong correlation between the items in an association's rules.

scholarly journals Generation of Association Rules of Data Mining for Lung Cancer by Air Pollution

2020 ◽  
Vol 9 (3) ◽  
pp. 2874-2879
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Air Pollutants ◽  
Tamil Nadu ◽  
Respiratory Illness ◽  
Atmospheric Pollutants ◽  
Healthcare Database ◽  
Rapid Urbanization ◽  
Association Rules Mining ◽  
Data Mining Approach

Revelation to adverse air pollutants attributed harmful effects in humans health. This research targets to evaluate the influence of atmospheric pollutants via determining the number of hospitalization underlying pulmonary complication in Chennai, Tamil Nadu. This tropical metropolitan city and also capital of Tamil Nadu have recently endured with the atmospheric pollutants. Due to rapid urbanization, followed by installation of numerous industries over the years have gradually affected the air quality. Chennai has respiratory illness in maximum record owing to atmospheric pollutants. The atmospheric pollutants and its impact on wellbeing could be due to pollutant’s ability in inducing oxidative stress, allergy and irritation, and it is reasonable that high points for air pollutants is producing hospitalization in great number. In this paper, a efficacious and novel study utilizing data mining approach involving ‘suggestion rules’ had imparted, wherein its capability to search for an fundamental linking among qualities with greater database and the capacity to handle inexact database that frequently happens under real world scenario which appeared rapidly problematic. A detection of association dealings, regular designs or connections between items set or components in databases is association rules mining. Association rules are very beneficial in atmospheric pollutants and healthcare database because they deal prospect to lead smart analysis and produce valuable data also frame important data bases rapidly and routinely, so that progress effective plans to minimize health contact to the atmospheric pollutants. Data completed pre-processing phase to assist condition of demonstrating procedure. With respect to conclusion, association rules mining had performed by Apriori, Eclat and FP growth algorithm the results showed that the latter was much accurate and consumes lesser time

scholarly journals Data Mining Penjualan Produk Dengan Metode Apriori Pada Indomaret Galang Kota

2018 ◽  
Vol 1 (2) ◽  
Author(s):  
Sheih Al Syahdan ◽  
Anita Sindar
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Product Placement ◽  
Data Mining Technique ◽  
Frequency Pattern ◽  
Food And Beverage ◽  
Minimum Requirements ◽  
Analytical Tools ◽  
Confidence Value ◽  
Problem Data

<p>The large number of transactions, companies need analytical tools to provide information that is useful for the company in determining the layout of goods, what items are most in demand by consumers and others. As experienced by several other supermarkets, product placement is a major problem. Data mining is a technique for digging up information that is hidden or hidden. This study will identify several types of association rules relating to sales transaction data, namely support and confidence values. The data used are 25 food and beverage products. Data mining technique uses associative rule with the Apriori method, aims to find a combination of items with a frequency pattern of the transaction results. After all high frequency patterns are found, then the association rules that meet the minimum requirements are found for confidence associative rules A → B minimum confidence = 25%, confidence value of A → B rules.</p>

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