scholarly journals Data Mining in Elite Beach Volleyball – Detecting Tactical Patterns Using Market Basket Analysis

2019 ◽  
Vol 18 (2) ◽  
pp. 1-19 ◽  
Author(s):  
Sebastian Wenninger ◽  
Daniel Link ◽  
Martin Lames
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Rule Mining ◽  
Aggregated Data ◽  
Data Mining Approach ◽  
Sequential Rule ◽  
Sequential Association Rule Mining ◽  
High Level ◽  
Sports Coaches ◽  
Sports Data

Abstract Sports coaches today have access to a growing amount of information that describes the performance of their players. Methods such as data mining have become increasingly useful tools to deal with the analytical demands of these high volumes of data. In this paper, we present a sports data mining approach using a combination of sequential association rule mining and clustering to extract useful information from a database of more than 400 high level beach volleyball games gathered at FIVB events in the years from 2013 to 2016 for both men and women. We regard each rally as a sequence of transactions including the tactical behaviours of the players. Use cases of our approach are shown by its application on the aggregated data for both genders and by analyzing the sequential patterns of a single player. Results indicate that sequential rule mining in conjunction with clustering can be a useful tool to reveal interesting patterns in beach volleyball performance data.

DATA MINING APPROACH FOR ADVANCEMENT OF “ASSOCIATION RULE MINING” USING “R PROGRAMMING”

2016 ◽  
Vol 10 (4) ◽  
pp. 6
Author(s):  
VIJ RAHUL KUMAR ◽  
KALRA PARVEEN ◽  
JAWALKAR C.S. ◽  
◽  
◽  
...  
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Association Rule Mining ◽  
Rule Mining ◽  
Data Mining Approach ◽  
R Programming

scholarly journals Present State-of-The-ART of Dynamic Association Rule Mining Algorithms

2019 ◽  
Vol 9 (1) ◽  
pp. 309-316
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Rule Mining ◽  
Healthcare Data ◽  
Data Mining Approach ◽  
Mining Community ◽  
Pattern Growth ◽  
Mining Algorithms

Association Rule Mining (ARM) is a data mining approach for discovering rules that reveal latent associations among persisted entity sets. ARM has many significant applications in the real world such as finding interesting incidents, analyzing stock market data and discovering hidden relationships in healthcare data to mention few. Many algorithms that are efficient to mine association rules are found in the existing literature, apriori-based and Pattern-Growth. Comprehensive understanding of them helps data mining community and its stakeholders to make expert decisions. Dynamic update of association rules that have been discovered already is very challenging due to the fact that the changes are arbitrary and heterogeneous in the kind of operations. When new instances are added to existing dataset that has been subjected to ARM, only those instances are to be used in order to go for incremental mining of rules instead of considering the whole dataset again. Recently some algorithms were developed by researchers especially to achieve incremental ARM. They are broadly grouped into Apriori-based and Pattern-Growth. This paper provides review of Apriori-based and Pattern-Growth techniques that support incremental ARM.

Prediction of Status Patterns of Wind Turbines: A Data-Mining Approach

2011 ◽  
Vol 133 (1) ◽  
Author(s):  
Andrew Kusiak ◽  
Anoop Verma
Keyword(s):  
Data Mining ◽  
Wind Turbines ◽  
Association Rule ◽  
Performance Monitoring ◽  
Prediction Models ◽  
Rule Mining ◽  
Data Mining Algorithms ◽  
Data Mining Approach ◽  
The Status ◽  
Mining Algorithms

This paper presents the application of data-mining techniques for identification and prediction of status patterns in wind turbines. Early prediction of status patterns benefits turbine maintenance by indicating the deterioration of components. An association rule mining algorithm is used to identify frequent status patterns of turbine components and systems that are in turn predicted using historical wind turbine data. The status patterns are predicted at six time periods spaced at 10 min intervals. The prediction models are generated by five data-mining algorithms. The random forest algorithm has produced the best prediction results. The prediction results are used to develop a component performance monitoring scheme.

scholarly journals Present State-of-The-Art of Association Rule Mining Algorithms

2019 ◽  
Vol 9 (1) ◽  
pp. 6398-6405
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Association Rule Mining ◽  
State Of The Art ◽  
Synthetic Data ◽  
Data Sets ◽  
Evolutionary Analysis ◽  
Rule Mining ◽  
Transaction Database ◽  
Mining Algorithms

A Data mining is the method of extracting useful information from various repositories such as Relational Database, Transaction database, spatial database, Temporal and Time-series database, Data Warehouses, World Wide Web. Various functionalities of Data mining include Characterization and Discrimination, Classification and prediction, Association Rule Mining, Cluster analysis, Evolutionary analysis. Association Rule mining is one of the most important techniques of Data Mining, that aims at extracting interesting relationships within the data. In this paper we study various Association Rule mining algorithms, also compare them by using synthetic data sets, and we provide the results obtained from the experimental analysis

scholarly journals ANALYSIS OF THESIS ADVISING PATTERN IN MASTER PROGRAM ON FACULTY OF AGRICULTURE IPB USING DATA MINING APPROACH

Edulib ◽  
2018 ◽  
Vol 8 (2) ◽  
pp. 194
Author(s):  
Lilis Syarifah ◽  
Imas Sukaesih Sitanggang ◽  
Pudji Muljono
Keyword(s):  
Data Mining ◽  
Upland Rice ◽  
Future Research ◽  
Rule Mining ◽  
Related Item ◽  
Text Documents ◽  
Master Program ◽  
R Language ◽  
Data Mining Approach ◽  
Using Data

The thesis is student study report which is accomplished as a requirement of graduation for Master program. Selecting study’s topic and advisors influence implementation of the study. Therefore, study’s topic is able to improve academic institution quality, however a large number of thesis documents on the repository cause difficulty to get information related to advisor’s expertness and the frequent or rare topic is former studied. Association rule mining can be used to mine information on the related item. This study aims to analyze advising patterns system in Master program on Agriculture based on supervisors and their topic research on metadata thesis of IPB repository and text documents of summary using data mining approach. The datas were collected from the repository of Bogor Agricultural University website and processed using R language programming. Pattern result of the reseach were that the most popular association on supervisor was occurred at support value of 0.00793 or equivalent to 7 theses and four popular topics were Botanical insecticide, Global warming, Upland Rice, and Land Use Change. The analysis result could be useful information to be reference or suggest future research or appropriate supervisor among agricultural.

scholarly journals ASSOCIATION RULE MINING UNTUK MENINGKATKAN PROMOSI PRODUK ( STUDI KASUS PADA PD. XYZ )

2018 ◽  
Vol 7 (2) ◽  
pp. 284-288
Author(s):  
Doni Winarso ◽  
Anwar Karnaidi
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Association Rule Mining ◽  
Rule Mining

Analisis association rule adalah teknik data mining yang digunakan untuk menemukan aturan asosiatif antara suatu kombinasi item. penelitian ini menggunakan algoritma apriori. Dengan  algoritma tersebut dilakukan pencarian  frekuensi dan item barang yang paling sering muncul. hasil dari penelitian in menunjukkan bahwa algoritma apriori  dapat digunakan untuk menganalisis data transaksi sehingga diketahui mana produk yang harus  dipromosikan. Perhitungan metode apriori menghasilkan suatu pola pembelian yang terjadi di PD. XYZ. dengan menganalisis pola tersebut dihasilakn kesimpulan bahwa produk  yang akan dipromosikan yaitu cat tembok ekonomis dan peralatan cat berupa kuas tangan dengan nilai support 11% dan confidence 75% .

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