Data mining based on clustering and association rule analysis for knowledge discovery in multiobjective topology optimization

2019 ◽  
Vol 119 ◽  
pp. 247-261 ◽  
Author(s):  
Yuki Sato ◽  
Kazuhiro Izui ◽  
Takayuki Yamada ◽  
Shinji Nishiwaki
Keyword(s):  
Data Mining ◽  
Topology Optimization ◽  
Knowledge Discovery ◽  
Association Rule ◽  
Rule Analysis

A Novel Efficient Mining Association Rules Algorithm for Distributed Databases

2010 ◽  
Vol 108-111 ◽  
pp. 50-56 ◽  
Author(s):  
Liang Zhong Shen
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Association Rules ◽  
Association Rule ◽  
Association Rule Mining ◽  
Distributed Databases ◽  
New Method ◽  
Rule Mining ◽  
Mining Association Rules

Due to the popularity of knowledge discovery and data mining, in practice as well as among academic and corporate professionals, association rule mining is receiving increasing attention. The technology of data mining is applied in analyzing data in databases. This paper puts forward a new method which is suit to design the distributed databases.

scholarly journals Analisa Pola Penjualan Produk Sepeda Motor Yamaha Menggunakan Metode Algoritma Apriori

2021 ◽  
Vol 5 (3) ◽  
pp. 1107
Author(s):  
Siti Nurlela ◽  
Lilyani Asri Utami
Keyword(s):  
Data Mining ◽  
Marketing Strategy ◽  
Automotive Industry ◽  
Association Rule ◽  
The Other ◽  
Apriori Algorithm ◽  
Sales Data ◽  
Competitive Prices ◽  
Rule Analysis ◽  
Strategy Pattern

The development of automotive industry in Indonesia can be classifiedas very rapid and annually increasing, causing highly competitive circumstances because many companies provide various types of motorcycle brands with quality and competitive prices. The company must create a marketing strategy pattern that can increase the level of sales efficiency of Yamaha motorcycle products. To overcome this problem, a strategy that can help increasing sales of motorcycle products is needed, in which by utilizing sales data owned by the company. Data mining can be used to process company sales data by looking for association rules with apriori algorithm on motorcycle product variables. From the results of the association rule analysis on sales data, with a minimum support of 30% and a minimum confidence of 75% can produce 3 rules with 3 products that are most in demand by consumers, namely the NEW MIOM3 CW, NEWAEROX155VVA and N-MAX, by knowing the most selling products, the company can add the most selling product supply and develop a marketing strategy to market the products with other products by examining the comparative advantage of the most sold products over the other products.

scholarly journals An Intelligent Recommender System Based on Association Rule Analysis for Requirement Engineering

2020 ◽  
Vol 26 (1) ◽  
pp. 33-49
Author(s):  
Mohammad Muhairat ◽  
Shadi Bi ◽  
Bilal Hawashin ◽  
Mohammad Elbes ◽  
Mahmoud Al-Ayyoub
Keyword(s):  
Data Mining ◽  
Software Engineering ◽  
Recommender System ◽  
Association Rule ◽  
Large Data ◽  
Requirement Engineering ◽  
Main Category ◽  
Data Mining Techniques ◽  
Rule Analysis

Requirement gathering is a vital step in software engineering. Even though many recent researches concentrated on the improvement of the requirement gathering process, many of their works lack completeness especially when the number of users is large. Data Mining techniques have been recently employed in various domains with promising results. In this work, we propose an intelligent recommender system for requirement engineering based on association rule analysis, which is a main category in Data Mining. Such recommender would contribute in enhancing the accuracy of the gathered requirements and provide more comprehensive results. Conducted experiments in this work prove that FP Growth outperformed Apriori in terms of execution and space consumption, while both methods were efficient in term of accuracy.

Hiding Sensitive Association Rules by Sanitizing

2013 ◽  
Vol 694-697 ◽  
pp. 2317-2321
Author(s):  
Hui Wang
Keyword(s):  
Data Mining ◽  
Side Effects ◽  
Knowledge Discovery ◽  
Association Rules ◽  
Association Rule ◽  
Mining Technology ◽  
Data Mining Techniques ◽  
Confidential Data ◽  
Knowledge Hiding ◽  
Existing Data

The goal of knowledge discovery is to extract hidden or useful unknown knowledge from databases, while the objective of knowledge hiding is to prevent certain confidential data or knowledge from being extracted through data mining techniques. Hiding sensitive association rules is focused. The side-effects of the existing data mining technology are investigated. The problem of sensitive association rule hiding is described formally. The representative sanitizing strategies for sensitive association rule hiding are discussed.

scholarly journals PENENTUAN ATURAN ASOSIASI PADA TRANSAKSI PEMINJAMAN BUKU MENGGUNAKAN ALGORITMA FP-GROWTH

2018 ◽  
Vol 9 (1) ◽  
pp. 15
Author(s):  
Elwani Elwani
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Association Rule ◽  
Knowledge Discovery In Database

<p>Tujuan dari penelitian ini dapat membantu Perpustakaan STMIK – AMIK Dumai untuk mengambil kesimpulan menentukan jenis Buku yang paling banyak diminati oleh mahasiswa. Istilah Data mining dan knowledge discovery in database (KDD) sering kali digunakan secara bergantian untuk menjelaskan proses penggalian informasi tersembunyi dalam suatu basis data yang besar. Penelitian ini dilakukan untuk mempelajari Data mining merupakan proses untuk mendapatkan informasi yang berguna dari gudang basis data yang berupa ilmu pengetahuan. Penelitian ini melakukan analisa data dengan menggunakan Data mining dan metode algoritma FP-Growth dan Tools Rapidminer studio7.3. Algoritma FP-Growth menganalisis data transaksi peminjaman buku untuk mengetahui dalam perpustakaan. Hasil algoritma FP-Growth dapat menemukan rule atau knowledge untuk menganalisa strategi dalam menentukan transaksi peminjaman buku dan dapat digunakan untuk proses ekstraksi rule atau knowledge yang dihasilkan. Association rule adalah salah satu teknik utama dalam Data mining dan merupakan bentuk yang paling umum dipakai dalam menemukan pattern atau poladari suatu kumpulan data. Berdasarkan hasil pengujian dan analisa Assoction Rule menggunakan Algoritma FP-Growth dan Tools Rapidminer Studio 7.3. Jadi jumlah Rules keseluruhan yang telah diproses adalah 7 keputusan atau pengetahuan baru dengan nilai kombinasi 12 jenis buku, nilai Support A (%) terendah adalah 0,143 dengan Confidence ≥ 50 % “Yes” dan ≤ 50 % “No”.</p><p><br /><strong>Kata Kunci</strong>: Knowledge Discovery in Database (KDD), Data mining, Fp-Growth, Association rule,</p>

Mining Association Rules

2010 ◽  
pp. 647-673 ◽  
Author(s):  
Mihai Gabroveanu
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Association Rules ◽  
Association Rule ◽  
Knowledge Discovery In Databases ◽  
Discovery Process ◽  
Fuzzy Association Rules ◽  
Large Databases ◽  
Hidden Knowledge ◽  
Basic Concepts

During the last years the amount of data stored in databases has grown very fast. Data mining, also known as knowledge discovery in databases, represents the discovery process of potentially useful hidden knowledge or relations among data from large databases. An important task in the data mining process is the discovery of the association rules. An association rule describes an interesting relationship between different attributes. There are different kinds of association rules: Boolean (crisp) association rules, quantitative association rules, fuzzy association rules, etc. In this chapter, we present the basic concepts of Boolean and the fuzzy association rules, and describe the methods used to discover the association rules by presenting the most important algorithms.

scholarly journals Knowledge Discovery And Virtual Tour To Support Tourism Promotion

2020 ◽  
Vol 2 (2) ◽  
pp. 94-106
Author(s):  
Gusti Ngurah Mega Nata ◽  
Steven Anthony ◽  
Putu Pande Yudiastra
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Environmental Conditions ◽  
Association Rule ◽  
Association Rule Mining ◽  
Frequent Pattern ◽  
Rule Mining ◽  
Tourist Destination ◽  
Virtual Tour ◽  
Tourist Destinations

Planning a tourism trip is an important part for tourists so that their tour is satisfying. Service bureaus that have a function to help provide information and prepare tourist travel plans for tourists often provide random destination choices because they do not know the pattern of selecting tourist destinations. This will be detrimental to tourists when service bureaus make wrong tourism travel plans. Tourists also often find it difficult to determine which tourist destination to go to because they do not know the environmental conditions in tourist destinations. To overcome this problem, in this study, knowledge discovery and virtual tours are carried out to increase the promotion of tourism. Knowledge discovery is finding information or knowledge. Knowledge discovery uses data mining techniques to perform data analysis and find patterns. The data mining model that can be used is the frequent pattern by looking for Association Rule Mining from the data. Virtual tour is a technique that can provide 360 ??+ 180 degree images. The virtual tour will be able to show the overall environmental conditions at the tourist destination. The results that have been obtained are in the form of a quick recommendation of tourist attractions in accordance with the country of origin of tourists based on the Association rule mining values. The virtual tour has presented a 360 degree panoramic photo view to inform the environment situation in the place commented by the system.

scholarly journals FUZZY SET APPROACHES TO DATA MINING OF ASSOCIATION RULE

2014 ◽  
pp. 236-239
Author(s):  
SHIVANGI SENGAR ◽  
SAKSHI PRIYA ◽  
URVASHI KHATUJA
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Fuzzy Set ◽  
Association Rule ◽  
Extended Techniques ◽  
Research Community ◽  
Large Set ◽  
Fuzzy Data ◽  
Typical Data ◽  
Large Databases

Data mining on large databases has been a major concern in research community due to the difficulty of analyzing huge volume of data. This paper focuses on the large set area i.e. on fuzzy sets and knowledge discovery of data. Association rules* provide information in accessing significant correlations in large databases. We have combined an extended techniques developed in both fuzzy data mining and knowledge discovery model in order to deal with the uncertainty found in typical data.

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