Mining Association rule with Attribute Oriented Induction High level Emerging Pattern (AOI-HEP) data mining technique

2018 ◽  
Author(s):  
Harco Leslie Hendric Spits Warnars ◽  
Ford Lumban Gaol ◽  
Yaya Heryadi ◽  
Agung Trisetyarso ◽  
Horacio Emilio Perez Sanchez
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Data Mining Technique ◽  
Mining Technique ◽  
High Level ◽  
Mining Association Rule ◽  
Emerging Pattern

scholarly journals MINING SIMILAR PATTERN WITH ATTRIBUTE ORIENTED INDUCTION HIGH LEVEL EMERGING PATTERN (AOI-HEP) DATA MINING TECHNIQUE

2017 ◽  
Vol 79 (7-2) ◽  
Author(s):  
Harco Leslie Hendric Spits Warnars ◽  
Nizirwan Anwar ◽  
Richard Randriatoamanana ◽  
Horacio Emilio Perez Sanchez
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Data Mining ◽  
Similar Pattern ◽  
Frequent Pattern ◽  
Data Mining Technique ◽  
Mining Technique ◽  
High Level ◽  
Concept Attribute ◽  
Emerging Pattern

AOI-HEP (Attribute Oriented Induction High Emerging Pattern) as new data mining technique has been success to mine frequent pattern and is extended to mine similar patterns. AOI-HEP is success to mine 3 and 1 similar patterns from IPUMS and breast cancer UCI machine learning datasets respectively. Meanwhile, the experiments showed that there was no finding similar patterns on adult and census UCI machine learning datasets. The experiments showed that finding AOI-HEP similar pattern in dataset is influenced by learning on chosen high level concept attribute in concept hierarchy and it is applied to AOI-HEP frequent pattern in previous research as well. The experiments chosed high level concept attributes such as workclass, clump thickness, means and marts for adult, breast cancer, census and IPUMS datasets respectively. In order to proof that the chosen high level concept attribute will influences the AOI-HEP similar pattern in dataset, then extended experiments were carried on and the finding were census dataset which had been none AOI-HEP similar pattern, had AOI-HEP similar pattern when learned on high level concept in marital attribute. Meanwhile, Breast cancer which had been had 1 AOI-HEP similar pattern, had none AOI-HEP similar pattern when learned on high level concept in attributes such as cell size, cell shape and bare nuclei. The 2 of 3 finding Similar patterns in IPUMS dataset have strong discriminant rule since having large growth rates such as 1.53% and 3.47%, and having large supports in target dataset such as 4.54% and 5.45 respectively. Moreover, there have small supports in contrasting dataset such as 2.96% and 1.57% respectively.         

scholarly journals Penerapan Data Mining Association Rule Menggunakan Algoritma FP-Growth Untuk Persediaan Sparepart Pada Bengkel

2021 ◽  
Vol 5 (2) ◽  
pp. 112-121
Author(s):  
Guntoro Guntoro ◽  
Charles Parmonangan Hutabarat
Keyword(s):  
Data Mining ◽  
Data Processing ◽  
Association Rule ◽  
Data Gathering ◽  
Engine Oil ◽  
Data Mining Technique ◽  
Mining Technique ◽  
Spark Plug ◽  
Gathering Data ◽  
Mining Association Rule

Many individuals are interested in starting a workshop. By responding to each customer's desires, the workshop company may continue to develop, and so the data mining technique can address this challenge. The FP-Growth algorithm is one of the methods that may be used to determine the stock availability of automotive spare components such as engine oil, spark plugs, oil filters, ac filters, batteries, tires, and so on. This research is divided into four stages: problem identification, data gathering, data processing, and data testing. Based on the results of the testing, AK (Battery), OM (Engine Oil), and BS (Spark plug) received support values of 33% and 80%, respectively. Furthermore, the BN (Ban) and KR (Kampas Bram) values were found with 33% support and 80% confidence. Furthermore, we obtain AK (Battery) and OM (Engine Oil) with 33% support and 80% confidence, and BN (Tires) and OM (Engine Oil) with 33% support and 80% confidence. OM (Engineering Oil), AK (Battery), and BS (Battery Storage) are the abbreviations for the terms OM (Engineering Oil), AK (Battery), and BS (Battery (Spark plug)).

scholarly journals IMPLEMENTASI DATA MINING DALAM ANALISA POLA PEMINJAMAN BUKU DI PERPUSTAKAAN MENGGUNAKAN METODE ASSOCIATION RULE

JURTEKSI ◽  
2019 ◽  
Vol 5 (1) ◽  
pp. 89-96
Author(s):  
Edi Kurniawan
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
Growth Data ◽  
Data Mining Technique ◽  
Transaction Data ◽  
Mining Technique ◽  
New Information ◽  
Important Means ◽  
Mining Association Rule

Abstract: The library is one of the most important means to add insight and knowledge to everyone. In general, borrowing transaction data books that exist in a library are only left to accumulate by the library in the database without any utilization or further processing of the data that has long been stored. By utilizing the Data Mining technique using association rules with FP-Growth, these data will be very useful. Because from the data lending books to the library, new information can be gleaned about what books are often borrowed and know the pattern of relationships between books that have been borrowed together so that later it can be used to compile books in accordance with the existing borrowing patterns so that they can facilitate library visitors in the process of finding books. Keywords: Data Mining, Association Rule, FP-Growth, Library Abstrak: Perpustakaan merupakan salah satu sarana yang sangat penting untuk menambah wawasan dan keilmuan setiap orang. Pada umumnya data transaksi peminjaman buku yang ada pada sebuah perpustakaan hanya dibiarkan saja menumpuk oleh pihak perpustakaan di dalam database tanpa ada pemanfaatan atau pengolahan lebih lanjut dari data-data yang telah lama tersimpan tersebut. Dengan melakukan pemanfaatan menggunakan Teknik Data Mining metode association rules dengan FP-Growth, data-data tersebut akan jadi sangat bermanfaat. Karena dari data peminjaman buku pada perpustakaan tersebut dapat diggali informasi baru tentang buku-buku apa yang sering dipinjam dan mengetahui pola hubungan antara buku yang telah dipinjam secara bersama-sama sehingga nantinya dapat dimanfaatkan untuk melakukan penyusunan buku sesuai dengan pola peminjaman buku yang ada sehingga dapat mempermudah para pengunjung perpustakaan dalam proses pencarian buku. Kata Kunci : Data Mining, Asociation Rule, FP-Growth, Perpustakaan

scholarly journals Application of Data Mining Technique in Stock Market : An Analysis

2014 ◽  
pp. 294-295
Author(s):  
SACHIN KAMBEY ◽  
R. S. THAKUR ◽  
SHAILESH JALORI
Keyword(s):  
Data Mining ◽  
Stock Market ◽  
Association Rule ◽  
Movement Direction ◽  
Data Mining Technique ◽  
Business World ◽  
Mining Technique ◽  
Hidden Patterns ◽  
Technology Tool ◽  
And Behavior

Stock market prediction with data mining technique is one of the most important issues to be investigated and it is one of the fascinating issues of stock market research over the past decade. Many attempts have been made to predict stock market data using statistical and traditional methods, but these methods are no longer adequate for analyzing this huge amount of data. Data mining is one of most important powerful information technology tool in today’s competitive business world, it is able to uncover hidden patterns and predict future trends and behavior in stock market. This paper also highlights the application of association rule in stock market and their future movement direction.

From data to knowledge mining

2009 ◽  
Vol 23 (4) ◽  
pp. 427-441 ◽  
Author(s):  
Ana Cristina Bicharra Garcia ◽  
Inhauma Ferraz ◽  
Adriana S. Vivacqua
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
Evaluation Criteria ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Data Mining Technique ◽  
Mining Technique ◽  
Data Points

AbstractMost past approaches to data mining have been based on association rules. However, the simple application of association rules usually only changes the user's problem from dealing with millions of data points to dealing with thousands of rules. Although this may somewhat reduce the scale of the problem, it is not a completely satisfactory solution. This paper presents a new data mining technique, called knowledge cohesion (KC), which takes into account a domain ontology and the user's interest in exploring certain data sets to extract knowledge, in the form of semantic nets, from large data sets. The KC method has been successfully applied to mine causal relations from oil platform accident reports. In a comparison with association rule techniques for the same domain, KC has shown a significant improvement in the extraction of relevant knowledge, using processing complexity and knowledge manageability as the evaluation criteria.

scholarly journals Association Rule Data Mining Applications for Atlantic Tropical Cyclone Intensity Changes

2011 ◽  
Vol 26 (3) ◽  
pp. 337-353 ◽  
Author(s):  
Ruixin Yang ◽  
Jiang Tang ◽  
Donglian Sun
Keyword(s):  
Data Mining ◽  
Tropical Cyclone ◽  
Association Rule ◽  
Tropical Storms ◽  
Rapid Intensification ◽  
Data Mining Technique ◽  
Mining Technique ◽  
Intensity Prediction ◽  
Intensity Changes ◽  
Tropical Depressions

Abstract This study applies a data mining technique called association rule mining to the analysis of intensity changes of North Atlantic tropical cyclones (TCs). The “best track” data from the National Hurricane Center and the Statistical Hurricane Intensity Prediction Scheme databases were stratified into tropical depressions, tropical storms, and category 1–5 hurricanes based on the Saffir–Simpson hurricane scale. After stratification, the seven resulting groups of TCs plus two additional aggregation groups were further separated into intensifying, weakening, and stable TCs. The analysis of the stratified data for preprocessing revealed that faster northward storm motion (the meridional component of storm motion) favors tropical storm intensification but does not favor the intensification of hurricanes. Intensifying tropical storms are more strongly associated with a higher convergence in the upper atmosphere (200-hPa relative eddy momentum flux convergence) than weakening tropical storms, while intensifying hurricanes are more strongly associated with lower convergence values. The mined association rules showed that cofactors usually display higher-intensity prediction power in the stratified TC groups. The data mining results also identified a predictor set with fewer factors but improved probabilities of rapid intensification. This study found that the data mining technique not only sheds light on the roles of multiple-associated physical processes in tropical cyclone development—especially in rapid intensification processes—but also will help improve TC intensity forecasting. This paper provides an outline on how to use this data mining technique and how to overcome low occurrences of mined conditions in order to improve TC intensity forecasting capabilities.

scholarly journals Penerapan Metode Association Rule Untuk Menganalisa Pola Pemakaian Bahan Kimia Di Laboratorium Menggunakan Algoritma FP-Growth (Studi Kasus di Laboratorium Kimia PT. PLN (Persero) Sektor Pembangkitan Belawan Medan)

2019 ◽  
Vol 3 (2) ◽  
pp. 56
Author(s):  
Buyung Solihin Hasugian
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Frequent Itemset ◽  
Data Mining Technique ◽  
Data Set ◽  
Mining Technique ◽  
Several Variables ◽  
Alternative Algorithm ◽  
Chemical Usage ◽  
Tree Development

<p class="Default"><em>The pattern of using chemicals in the laboratory of PT. PLN (Persero) </em><em>Sektor Pembangkitan </em><em>Belawan Medan is not only to find out what chemicals are used but also to find out the amount of chemicals left so that laboratory officials can properly manage the use of these chemicals. One appropriate way to determine the pattern of use of these chemicals is to use data mining techniques. The Data Mining technique used in this case is the FP-Growth Algorithm. FP-Growth is an alternative algorithm that can be used to determine the most frequent set of data in a data set. The study was conducted using several variables, namely the date and chemicals used. The results of this study are in the form of a chemical usage pattern which is processed using software, namely implementing the FP-Growth algorithm using the concept of FP-Tree development in searching for Frequent Itemset.</em></p><p class="Default"><em> </em></p><pre><em>Keywords: Data Mining, Association Rules, Frequent Itemset, FP-Growth</em></pre>

scholarly journals COMPROMISO ESTUDIANTIL EN EL DESEMPEÑO ACADÉMICO Y SUS DIFERENCIAS POR GÉNERO

2020 ◽  
Vol 9 (01) ◽  
Author(s):  
Erika Yunuen Morales Mateos ◽  
María Arely López Garrido ◽  
Laura López Díaz
Keyword(s):  
Data Mining ◽  
Information Technology ◽  
Data Mining Technique ◽  
Mining Technique ◽  
Academic Fields ◽  
High Level

The purpose of this research was to identify groups of students characterized by their student commitment. There were 31 participating students belonging to careers related to information technology from a university un the southern of Mexico. For this, the authors applied the UWES-S with a series of questions related to the academic fields. The data mining technique called clustering was subsequently applied to identify the group using the WEKA tool. It is highlighted as a result that the group of women has high levels of student’s commitment, vigor and absorption, compared to men, who have a high level of dedication.

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